<m i c a t e g o r y=” $ . Abusus . Koureni . Kuraci . Kouri . K o l i k k o u r i ” v a l u e=” $ { 2 . s t r } $ { 3 . l e x } c i g a r e t dennˇ e ”> <w i d=” 1 ” s t r=” ! / ˆ ( ( ne ) | ( Ne ) ) $/ ” /> <w i d=” 2 ” s t r=” / ˆ ( ( k o uˇr´ı ) | ( Kouˇr´ı ) ) $/ ” /> <w i d=” 3 ” pos=” numeral ” /> <mi c a t e g o r y=” $ . Abusus . Koureni . Kuraci . Kouri . K o l i k k o u r i ” v a l u e=” k o uˇr´ı $ { 2 . s t r } c i g a r e t dennˇ e ”> <w i d=” 1 ” l e x=” / ˆ ( ( kuˇra ´ k ) | ( kuˇr aˇ c ka ) | ( Kuˇra ´k ) | ( Kuˇraˇ c ka ) ) $/ ” /> <w i d=” 2 ” pos=” numeral ” /> <mi c a t e g o r y=” $ . Abusus . Koureni . Kuraci . Kouri . K o l i k k o u r i ” v a l u e=” $ { 2 . s t r } $ { 3 . l e x } $ { 4 . l e x e ”> } $ { 5 . l e x } c i g a r e t dennˇ <w i d=” 1 ” s t r=” ! / ˆ ( ( ne ) | ( Ne ) ) $/ ” /> <w i d=” 2 ” s t r=” / ˆ ( ( k o uˇr´ı ) | ( Kouˇr´ı ) ) $/ ” /> <w i d=” 3 ” pos=” numeral ” /> <w i d=” 4 ” pos=” r e s i d u a l ” /> <w i d=” 5 ” pos=” numeral ” /> <mi c a t e g o r y=” $ . Abusus . Koureni . Kuraci . Kouri . K o l i k k o u r i ” v a l u e=” k o uˇr´ı $ { 2 . s t r } $ { 3 . l e x } $ { 4 . l e x } c i g a r e t dennˇ e ”> <w i d=” 1 ” l e x=” / ˆ ( ( kuˇra ´ k ) | ( kuˇr aˇ c ka ) | ( Kuˇra ´k ) | ( Kuˇraˇ c ka ) ) $/ ” /> <w i d=” 2 ” pos=” numeral ” /> <w i d=” 3 ” pos=” r e s i d u a l ” /> <w i d=” 4 ” pos=” numeral ” />
%) was reached in the cigarette smoking quantity identification. Comparing the results from the basic and contextual analysis, the number of identified smokers decreased from 13 282 to 5 614. A drop of 57,73 % strongly favours a contextual analysis in accuracy before a simple exact match. Despite the fact, that error rate is minimal, further research will focus on further improvement of contextual analysis, especially TARs.
Application in Biomedicine and Healthcare The proposed smoker identification system is able to easily process terabytes of narrative medical records. The system is an input data format undependable, so it can be used on variable data source without further customisation. The system is prepared for an automatic classification based on patient status as smoker, non-smoker, and ex-smoker. With acceptable accuracy of more than 98 %, the system is prepared for a survey Big Data analysis of EMRs. The further customisation will be addressed in the next steps.
Acknowledgements
This paper has been partially supported by the SVV 260 267 project of Charles University. The 386 587 EMRs, with a total input size of 1,6 4 GB , were processed in the Apache Lucene based system IBM Watson Explorer Content Analytics. During the pro- References cessing, each word in the input text file was linguistically [1] Bastida G, Beltr´ an B. Ulcerative colitis in smokers, nonrecognised, and labelled if it belongs to a certain catesmokers and exsmokers. World J Gastroenterol 2011; 17: gory/subcategory. All collateral information about each 2740–7. word were then stored in the Index [13], in our case in 55 [2] Bl´aha M, Janˇca D, Klika P, Muˇz´ık J, Duˇsek L. Project ICOP – Architecture of Software Tool for Decision Support in Oncolfiles of total size 3,81 GB. Smoking classification categories ogy. Data and Knowledge for Medical Decision Support. Proand standard NLP processing enriched the Index thus far, ceedings of the EFMI Special Topic Conference. 2013; 130–134. that indexed data are more than double the origin.
Results From the performed analysis it is possible to conclude that from 314 202 different EMRs, 20 704 were related to smoking. From this restricted set, 5 614 EMRS were classified as belonging to a smoker, 11 912 to a non-smoker, and 3 178 to an ex-smoker. Concerning 5 614 smoking patients, the majority (51,05 %) declares that smokes 10 or 20 cigarettes per day5 . Smoking related records were manually re-evaluated. The automatic classification system differs from a human method in approximately 1,25 %. This is highly acceptable result considering, that in some cases, a human evaluator had a difficulty to decide to which category the particular EMR belongs to. Slightly higher fault rate (1,99 4 The file sizes oscillated between few bytes (the smallest file was only 26 B long containing just one word) and several kilobytes (the
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[3] Calabrese E, Yanai H, Shuster D, et al. Low-dose smoking resumption in exsmokers with refractory ulcerative colitis. J Crohns Colitis 2012; 6: 756–62. [4] Cochrane Collaboration. cochrane.org.
Available
from:
https://www.
[5] Hartzband P, Groopman J. Untangling the Web –Patients, Doctors, and the Internet. N Engl J Med 2010; 362:1063–1066. [6] Holzinger A, Stocker C, Ofner B, Prochaska G, Brabenetz A, Hofmann-Wellenhof R. Combining HCI, Natural Language Processing, and Knowledge Discovery – Potential of IBM Content Analytics as an Assistive Technology in the Biomedical Field. Human-Computer Interaction and Knowledge Discovery in Complex, Unstructured, Big Data. 2013; 7947: 13–24. [7] Johnson SB, Bakken S, Dine D, Hyun S, Mendon¸ca E, Morrison F, Bright T, Van Vleck T, Wrenn J, Stetson P. An electronic health record based on structured narrative. J Am Med Inform Assoc. 2008; 15(1): 54–64. ˇ ıd R, Kub´ [8] Klimeˇs D, Sm´ asek M, Vyzula R, Duˇsek L. DIOS –Database of Formalized chemotherapeutic Regimens. Data biggest over 15,3 kB) in a testing set. A median size of input file was 727 B. 5 One particular patient used to smoke 120 cigarettes per day.
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[9] [10] [11] [12]
and Knowledge for Medical Decision Support. Proceedings of the EFMI Special Topic Conference. 2013; 165–169. Project UIMA, Apache UIMA. Available from: https://uima. apache.org/. Regular expressions. Available from: https://docs.oracle. com/javase/7/docs/api/java/util/regex/Pattern.html. Schiff GD, Bates DW. Can Electronic Clinical Documentation Help Prevent Diagnostic Errors NEJM 2010; 362: 1066–1069. Stonov´ a M. Unstructured Data in Evidence-based MHealthcare. Semantic Inte- roperability in Biomedicine and Healthcare. IJBH 2015; 2(1): 47–49.
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[13] Stonov´ a M. Unstructured Data in Healthcare. Semantic Interoperability in Biomedicine and Healthcare. IJBH 2014; 2(1): 34–36. [14] Walsh KE, Gurwitz JH. Medical abbreviations: writing little and communicating less. Arch. Dis. Child 2008; 93: 816–817. [15] Zvolsk´ y M. Automating the Use of Clinical Practice Guidelines in the Health Information Infrastructure. Semantic Interoperability in Biomedicine and Healthcare. IJBH 2014; 2(1): 51–52.
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Original Article
Software Suite for Data Collection and Processing in Long-term Care Slavka Viteckova1 , Radim Krupicka1 , Ondrej Klempir1 , Zoltan Szabo1 , Hana Vankova2 , Martina Kuckir2 , Iva Holmerova2 1
Faculty of Biomedical Engineering, Czech Technical University in Prague, Kladno, Czech Republic 2
CELLO-ILC-CZ Faculty of Humanities, Charles University and Centre of Gerontology Prague
Abstract Data evaluation is crucial for effective quality management of long-term care institutions. Data from long-term care institutions can be used for further analysis. For instance, it is possible to evaluate patients self-sufficiency or frailty and the corresponding risk factors, or evaluate effectiveness and quality of long-term care. Data collection (in long-term care institutions in the Czech Republic) is performed using paper questionnaires, after which the data are manually processed. However, this method is not suitable from the viewpoint of bulk processing and long-term storage.
Therefore, we have developed a software suite for data collection and long-term storage, which is designed to fit the needs of long-term nursing care institutions. The needs of such a system and its role in long-term care are discussed first. A description of the software suite is then presented. Next, the clinical application of newly developed software is shown and evaluated. Finally, the paper concludes with a discussion on the scope of further development.
Keywords Consumer health information, decision-support systems, healthcare service innovation and IT, health information on the Web, quality control
Correspondence to: Slavka Viteckova Faculty of Biomedical Engineering, CTU in Prague Address: Nam. Sitna 3105, 272 01 Kladno E–mail: [email protected]
1
Introduction
Demographic trends lead to a higher proportion of old and very old people in the global population. Providing health and social care for the needs of growing numbers of older people in the face of rising costs is a major challenge. For example, it is estimated that the proportion of people aged 65+ in China will double from 7% to 14% of the population in 26 years [1]. In 2010, people aged 65+ accounted for 13% of the U.S. population. This number is expected to rise to 19 % of the population by 2030 [2]. According to projected demographic data, the number of senior citizens is expected to rise as a percentage of the global population. Thus, ageing and chronic diseases associated with it will become significant health-related and socio-economic issues [3, 4, 5]. Long-term care (LTC) is a broad term that refers to medical and social services designed to meet the needs of people, most often elderly individuals, whose ability to perform daily activities has been impaired by chronic health problems [6]. Although attempts have been made to modernize and increase the quality of long-term care, c
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its availability and quality are inadequate in the Czech Republic. It is necessary to have at hand data which are accurate and comparable with each other to make progress in determining cost effective services and maintain satisfactory quality of care. To this end, data collection is among the crucial elements of effective planning and quality management of long-term care. With such data, it is possible to analyze important aspects of long-term care, such as selfsufficiency or frailty, and their corresponding risk factors. Furthermore, data of this kind can be used to accurately predict the need for institutional care and social services [9], and can thus improve economic efficiency. In addition to its economic implications, data collection is important for social and demographic research [7, 8]. LTC providers raised the demand for creating software for facilities that face a lack of funds, IT equipment, and qualified personnel able to store, process, and evaluate data on a long-term basis and thus contribute to better facility efficiency and development of health in the growing senior population [10, 11]. At present, longterm nursing institutions in the Czech Republic use paper questionnaires for data acquisition and storage. This IJBH – Volume 4 (2016), Issue 2
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method is untenable and inappropriate for long-term storage, mass processing, and effective and rapid evaluation of data. A viable solution is a software to aggregate data from routine practices to provide evidence that is highly relevant to long-term care evaluation and planning. Easyto-use software is needed for daily use in LTC institutions. Moreover, such software must be sufficiently flexible to enable various questionnaires to cover wide spectra of tested problem domains. Hence, in this paper, we propose a software suite - a set of bundled programs - called “GDiag” that can satisfactorily carry out all these interconnected tasks. GDiag covers the overall life cycle of questionnaires: not only does it facilitate questionnaire creation, but also digitization, fast and safe storage, and processing of the acquired data. This suite is intended to create questionnaires that can be filled in either electronically or by hand. In the latter case, the completed printed questionnaire can be scanned subsequently and then the data converted into electronic form.
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Electronic data are crucial for processing and evaluation of planning strategies and quality management of long-term care. We thus designed and implemented the Questionnaire Editor (see section 2.1). Once the questionnaire has been created with the editor, there are two ways to complete it (Figure 1). The first option is to complete the questionnaire via a custom web application. The second is to export the questionnaire into PDF format and print it. Further, we developed a questionnaire scanner that digitizes printed questionnaires and saves the data to a database (see section 2.2). In addition to standard scanning process of retrieving scanned data to local device, e.g. computer, GDiag enables to send scanned data directly from scanner to remote central web application via FTP. The target web application allows you to process a scanned questionnaire automatically via a web user interface (see section 2.3). The printed version is useful especially when assessing the mental and cognitive state of the patient. These questionnaires often include hand written responses in addition to pre-defined answers.
2.1
Figure 1: Overall view of the GDiag software suite. There are two methods to complete questionnaires: a fully electronic method (A), and a printed method (B).
The system GDiag has focused on the data of the Complex Geriatric Assessment and other basic information (data of individual patients).This provides a powerful tool to develop the information system on long-term care and its provision which is able to combine information about functional status of patients with some selected indicators of quality of care. Both parts of this dataset are based on scientific evidence. The ambition of the project is to avoid disadvantages of available systems (e.g. complexity of interRAI) and to enable basic data collection that could facilitate quality improvement, management and development of long-term care. IJBH – Volume 4 (2016), Issue 2
Software Description
The Questionnaire Editor
We developed a desktop application to create a wide range of questionnaires (Figure 2). The Questionnaire Editor lets the user drag and drop elements to create questionnaires. The element toolbar contains passive elements, as well as the active ones. Passive elements include barcodes, labels, images, and separating elements such as lines and borders. These elements assist with the overall format and look of the questionnaire. Active elements are elements designed to fill in the data, such as single letter input fields, checkboxes, date fields, fields for writing or drawing by hand. Each element has a set of attributes. The most important attribute is the name attribute. Data entered in active elements are later stored as key-value pairs, where the name attribute is the key. Therefore, the names of active elements have to be unique in the questionnaire.
Figure 2: Questionnaire editor.
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In order to create a collection of data relating to one patient, each questionnaire contains a set of input fields for the social security number of that patient. In addition to these input fields, each questionnaire has a barcode that encodes the type of questionnaire and the version number. The questionnaire, therefore, has a unique identifier that is used during the digitizing process. Once the questionnaire is completed, the output is stored in XML format. Each questionnaire element is represented by an XML element and the attributes of each element are represented by XML attributes. The XML template file contains all necessary information to build a full-fledged questionnaire, including position, visibility, and stack order of elements. It allows an identical questionnaire to be rebuilt in various applications, such as the questionnaire scanner or web application.
Figure 3: Tool for digitizing questionnaires.
The result is a questionnaire that can be printed in or 2.3 The Web Application exported to XPS format. The web application has two purposes. First, it serves as a central storage location for the massive amount of questionnaire data. Second, it is used for filling in ques2.2 Digitizing of Questionnaires tionnaires electronically (Figure 4). To achieve this, web application loads the XML template file, reads both elements and its attributes and builds appropriate web form. Digitizing the printed questionnaire is the most important step in the data collection process. The process itself is responsible for retrieving data from scanned questionnaires. When the completed questionnaire has been scanned, the orientation of the paper, (landscape or portrait) needs to be identified. Then, the usable area is detected. This area is the main area of the questionnaire, excluding surrounding blank sections or margins. To that end, the usable area is bordered by four small squares in its corners. Then, the barcode is captured and the electronic template in XML format is found. The XML template is required to identify the user’s inputs that have to be digitized. Furthermore, the state of each checkbox is identified and hand-written text and hand-drawn images are located. The digitized data can contain errors, such as a misspelled patient name. Sometimes, the state of a checkbox can be incorrectly detected. For this reason, the digitized data cannot be stored directly without a manual check of it being performed first. Hence, the scanned questionnaire is displayed next to the visualized electronic template with electronic data (Figure 3). In this step, the misspelled digitized data can be corrected manually.
Figure 4: Web application to complete questionnaires. Minimental State Examination questionnaire.
The web application utilizes a relational database to store questionnaire data. Moreover, all scanned questionnaires are stored in an image file format. Because of the diverse nature of questionnaires and the need to maintain the scalability of web applications, the database Finally, the digitized and corrected data are exported (DB) schema is largely based on the entity-attribute-value to central storage along with the scanned raw question- (EAV) data model. In addition to EAV data tables, the DB contains logging tables. Logging tables preserve naires, which are saved for possible later use. c
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sets of chronological records, to provide evidence of data change sequences. These records store the timestamp of the change, the previous value, and the user who made the change. The XML templates are stored as files on a file system; in addition, references to those files are maintained in the database. The application offers a search of questionnaire data. An optional feature is to automatically make the questionnaire responses anonymous. Collections of data can be created on the basis of criteria, such as patient’s age and sex, and then exported to various formats, therefore allowing the data to be fully accessible for additional evaluation. The web application can operate at three different security levels. In the first level, fully anonymous mode, patients’ sensitive data (e.g., social number) are not stored. To achieve this, the sensitive data are hashed prior to being saved to the database. Furthermore, all occurrences of sensitive data in the digitized questionnaire are covered in black. In this case, there is no means to recover the sensitive data. The second level, partly anonymous mode, works in the same manner as fully anonymous mode, except for an additional external mapping tool that pairs sensitive data (e.g., personal identification number) with its hash value. This mapping tool is separated from the web application; therefore, it can use more strict security policies. The third security level, non-anonymous mode, allows the web application to store sensitive data directly in the DB without interventions. The operation mode can be configured by an administrator. A demo version of the web application is available at http://gdiag.fbmi.cvut.cz.
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Clinical Evaluation
This software suite was developed, tested and validated in close cooperation with the Centre of Gerontology and CELLO Faculty of Humanities of Charles University in Prague and with consultation from the Czech Alzheimer Society. Data evaluated in pilot study were collected at two locations of the Centre of Gerontology. The data acquisition for quality care evaluation is performed twice a hospitalization per patient via standardized assessment questionnaires. The first monitoring process takes place on the first day of hospitalization; the second monitoring process takes place on the last day of hospitalization. The clinical evaluation was conducted from January 2013 to March 2013, and involved prospective data collection using a set of questionnaires generated by the GDiag software. It includes The Montreal Cognitive Assessment, Activity of Daily Living (ADL), Get Up & Go Test (TUG) and Mobility Test, The Barthel Index, Clock Test, Mini Nutritional Assessment, Geriatric Depression Scale, Pain Assessment, Risk for Pressure Ulcers Assessment (the Norton Scale), Mini Mental State Assessment (MMSE), and Elderly Falls Screening Test. Thereafter, IJBH – Volume 4 (2016), Issue 2
the questionnaires were inspected, scanned, and digitalized using our software and were saved in the central storage area. The total number of collected questionnaires was 2500 at the time of completion of pilot study. For the purposes of a pilot study evaluating the use of GDiag software in LTC daily routines, we selected three questionnaires for detailed statistical analysis: ADL, TUG, and MMSE. Of the initial 213 patients recruited, some individuals did not undergo all three assessments. Data for the study was obtained always by the trained researchers who were trained in the used nursing assessment tools. All questionnaires are assessed by summary scales which describes the severity of impairment or degree of risk in a problem domain. Local ethics committee has approved the study, and the participants gave their informed consent. Although each questionnaire contained set of questions and the GDiag supports export and evalutation of each questions separately, we did not compare individual input questions with its corresponding outpus; rather the total scores of the input and output questionnaires were compared. The characteristics of the questions (ADL, MMSE, and TUG) assessed during admission and discharge are listed in Table 1. The presented data demonstrate the changes in the monitored variables. Further analysis and interpretation of various data collected via GDiag can be found in geriatric and gerontological original research papers [12].
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Strenghts and Limitations
Owing to the online nature of GDiag utilization of mobile devices must be taken into account. The utilization of mobile devices into the LTC daily workflow leads to consider further constraints. Sustainability, data protection and authentication, linking the mobile device with the existing clinical information system, and designing an effective interface are major of specific constraints related to mobile environment [13]. Mobile applications are platform dependent; therefore each requirement to new or updated functionality implies application reimplementation for each platform independently. In contrast to mobile application which leads to high financial expenses, application versions divergence and short application life cycle, central part of GDiag is web-based. It follows that every functionality update is done only once - to the central webbased GDiag application. The other two tools of GDiag suite are platform-dependent (Windows-based). To improve sustainability we choose C] programming language for Windows-based tools and ASP.NET for web-based tool. Both languages strongly support testing, team work, code management, and automatic deployment which are preconditions for software quality and long life cycle with backward compatibility. Data protection and authentication is crucial in clinical software. Common strong authentication policies on desktop computers such as a user’s password are used to c
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Table 1: GDiag pilot study results. Improvement – the total questionnaire score at admission (the first day of hospitalization) was lower than score on discharge (the last day of hospitalization); no-change – score at admission equals the score on discharge; deterioration - score at admission was higher than the score on discharge.
ADL TUG MMSE
Sample size 203 173 150
Improvement 66 % 37.6 % 48.7 %
access the computer or tablet to complete questionnaire. Central web-application is protected by role-based security model. Furthermore, user inactivity during a set period of time results in automatic logout. Insomuch as mobile devices can be subject to lose or theft, no patient data are downloaded to the device and no patient data remain on the device. Next, the central web application is configurable to different levels of security. The last level, in which sensitive data are stored directly (raw data), is strongly discouraged. GDiag is not a part of any other medical information system. It is standalone software suite. The advantage of this solution in the clinical practice is independent maintenance and deployment. On top of that, loosely tided systems have a wider scale of use cases. On the other hand, there is a lack of exchange of patient information with other systems. This issue can be solved one- or bidirectional gateway. Due to variety of medical information systems (IS), the gateway are IS-specific (especially bi-directional gateway). As the gateway transmits patient data it is vulnerable and need to be secured. Although the Questionnaire Editor allows each element to be described as a mandatory element, and the Questionnaire Scanner verifies that mandatory fields are completed, it is possible to save questionnaire data with blank mandatory fields. Questionnaires are used for functional ability assessments in LTC facilities. However, some patients are not able to complete the evaluation for various reasons. In this case, some questionnaire fields will be blank. Nevertheless, the questionnaire is valuable, provides information about the patient’s health, and must be included in the health care documentation. Thus, the questionnaire scanner highlights blank mandatory fields but saves the record. Since the nursing work environment is subject to constant interruptions and is error prone, a robust user interface (UI) is crucial to the success of the system [14]. We designed and implemented the UI according to usability principles that are perceived to be more usable and inspired greater confidence among physicians than the guided navigation interface [15]. The behavior and performance of the GDiag software was dynamically verified on a set of test cases before being delivered to the LTC staff. Feedback was provided by the nursing staff after testing the software at the clinic during daily routines. This testing-improving process was repeated several times to improve the software. c
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No change 28.6 % 61.2 % 32 %
Deterioration 5.4 % 1.1 % 19.3 %
Discusssion
The nature of long-term nursing care tasks in light of new methods has led to a high demand for software that supports long-term bulk data collection, storage, and evaluation. In this paper, we proposed a novel software package for data acquisition from questionnaires, with a special focus on a simple clinical application. The use of our proposed GDiag suite in daily clinical practice can help find answers to key questions facing decision makers in long-term care, e.g., to identify the nature and amount of health services needed, through an evaluation of the quality of care provided. Although the cost of computerization might appear to be a significant hindrance to this advancement, the benefits of GDiag are considerable because it is a web-based solution. Web-based applications are low cost because they are centralized. Further, due to its centralized nature, GDiag is suitable for use in multi-location (multi-office) long-term care institutions. This approach takes advantage of the fact that patients’ data are stored in a central element, the web application, which allows the sharing of patients’ records among geographically dispersed LTC offices. Through GDiag, clinicians and other LTC staff can access information regarding a patient’s past sojourns in LTC institutions. Furthermore, they can receive feedback regarding the results of the patient’s health-status assessment in order to initiate care planning and interventions in a timely manner. Moreover, the integration of patient data from all locations of LTC can be judged to support and improve the quality of communication between LTC stuff within and across sites. [16]. As the computerization is often associated with decreased mobility of nursing staff [17], we found that electronic and printed versions of questionnaires as well as their mutual conversion are necessary to meet the needs of LTC institutions. Whereas electronic versions seem to be better for tablet users, both (paper and mobile version of questionnaire completion) encourage mobility of LTC stuff. Give the rising availability of cheaper computers and similar devices, the case for investment in these capabilities is becoming increasingly powerful. If the economic and personal reasons do not permit the use of electronic questionnaires, their printed versions can be used instead. Moreover, printed questionnaires are useful, in particular to assess the mental and cognitive state of patients. These questionnaires often include, in addition to pre-defined anIJBH – Volume 4 (2016), Issue 2
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swers, hand-written responses. We have shown that our software package has several advantages over the relevant existing software. Other software often cannot record the patient’s input, e.g., drawings, etc. Some competing software (e.g., QDS [18]) offer the possibility of printing questionnaires and digitizing them once they are filled out by patients, but many others lack this feature [19]. Our software offers two methods to complete a questionnaire. By offering the option to complete the questionnaire electronically or by hand, we provide users with the choice of working online or offline. To our knowledge there is no other survey tool which supports online and offline completion with subsequent digitization and storage. Moreover, our software suite is strongly focused on questionnaire’s custom layout design that is essential especially on cognitive assessment questionnaires. To maintain the simplicity of the Questionnaire Editor while retaining its ability to create questionnaires in any scale and for different clinical areas, users are not restricted when naming element identifiers. This allows them to easily and rapidly create questionnaires without background knowledge. On the other hand, questionnaire templates do not comply with HL7 standards. As a result, the exchange of patient information with other systems may be problematic. The solution is to create a library of questionnaire elements or blocks that are HL7 compatible. Subsequently, the user can create a questionnaire from the pre-defined elements. Such a library should be specific to the clinical area of the health care provider. However, an ongoing issue regarding the nursing stuff is their computer literacy with evidence that some nurses are reluctant users of computers. This reluctance to engage with information and communications technologies (ICT) needs sensitive management because the experience of the benefits of using ICT and electronic patient records increases their acceptability [20] and therefore their likely successful implementation. There is the evidence that nurses reported that the actual computer systems were cumbersome, illogical, slow, complicated and unreliable at times [21]. As they suggested we involved LTC nurses directly in software design to ensure that system supports nursing work. In the design and development of our software suite, special care was taken to allow the utilization of acquired data for research purposes. This is exemplified by the option to anonymize data. Furthermore, the data is automatically pooled with a personal ID and a date of completion, which helps evaluate patient progress during treatment. Software suite GDiag was developed in close cooperation with long-term care facilities. Because the questionnaires provide data about a patient’s functional ability, they are included in health care documentation. Although GDiag only stores functional ability assessment data, and does not store general patient health care documentation, it is documentation in “electronic form” and is subject to Czech law [22]. Therefore, entry corrections must be searchable by correction date and the identifier IJBH – Volume 4 (2016), Issue 2
of the user who performed the correction. Further, the original entry must remain legible. These requirements are fulfilled through the web application logging system. Within this legislation, there are no other special requirements for data or data hosting institutions. Apart from the provision described in [22], there is no definition of electronic health records (EHRs) in the Czech legislation, and there are no legal provisions defining the content of an EHR [23]. Similar to health care professionals, social workers are involved in some long-term care facilities in the Czech Republic. Because they require information about the condition of patients, LTC facilities are required by law to provide social workers with this information. Unlike health care staff, social workers do not have permission to view personal data (e.g., personal identification number). In this case, the Web Application should operate in partial anonymous mode, which discloses personal data only to specific groups (e.g., health care staff). Although our software package was developed in close cooperation with long-term care institutions, it is not narrowly focused. It instead offers a wide range of applications in various fields of healthcare to assess and improve patient care. Its versatility can be attributed to the ambivalent nature of the Questionnaire Editor, which allows the creation of many different questionnaires.
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Conclusion
We developed a software suite called GDiag to perform tasks included in the entire life cycle of a questionnaire: creation, digitization, and storage of acquired data. Although GDiag is designed for long-term care institutions, it can be employed for other applications as well. GDiag conforms to the Czech law with regard to management of health data. Further, for clinical evaluation, we created a set of well-structured questionnaires for nursing care assessment. This set of questionnaires was used during data acquisition over four months in long-term nursing care institutions. The digitized data from completed questionnaires were used to evaluate quality of nursing care during hospitalization. In addition to clinical applications, the questionnaires and obtained data can also be used for research in the field of long-term nursing care in the Czech Republic.
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Future Plans
First, we are going to implement web based version of questionnaire editor to make it platform-independent. Thus, GDiag Questionnaire editor will be available for non-Windows based systems too. Second, we are going to extend the GDiag software suite with new functions for data processing (drawings processing, quality parameters processing and displaying etc.). c
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Acknowledgements This project was supported by the Ministry of Health NT13705-4/2012: ”Development of system and software for structured functional assessment, collecting and processing data on long-term care, its quality and demand and by the Grant Agency of the Czech Technical University in Prague, grant No. SGS16/117/OHK4/1T/17.”
Conflicts of Interests The authors indicated no potential conflicts of interest.
References [1] Kinsella, K. and W. He (2009), An Aging World: 2008, US Department of Commerce, Washington. [2] Healy J. The benefits of an ageing population. The Australia Institute - Discussion Papers, 2004, Available from http://apo.org.au/node/8775 [3] Colombo F, Llena-Nozal A, Mercier J, Tjadens F. Help Wanted? Providing and Paying for Long-term Care. OECD Publishing; Paris 2011. [4] EC. Long-term care in ageing societies - Challenges and policy options. Brussels: European Commission, 2013 Contract No.: 41. [5] OECD/EC. A Good Life in Old Age? Monitoring and Improving Quality in Long-term Care. 2013. [6] McCall N. Long Term Care: Definition, Demand, Cost, and Financing. In: McCall N, ed. Who Will Pay for Long Term Care?: Insights from the Partnership Programs. Chicago: Health Administration Press, 2001:3-31. http://www.ache. org/PUBS/1mccall.pdf.AccessedJuly22,2016. [7] Holmerov´ a I, Koopmans R, Skela Saviˇ c B, Egerv´ ari A, Hermann B, Ruseckiene R et al. Advancing Long Term Care: Central European Perspectives. Journal of the American Medical Directors Association. 2012;13(7):578-580. [8] Federal Interagency Forum on Ageing-Related statistics [Internet]. 2016 [cited 2 February 2016]. Available from: http://www.agingstats.gov/main_site/data/2012_ documents/population.aspx [9] Huang J, Lin K, Li I. Service needs of residents in communitybased long-term care facilities in northern Taiwan. Journal of Clinical Nursing. 2007;17(1):99-108. [10] Callinan, S. M., Brandt, N. J. Tackling Communication Barriers Between Long-Term Care Facility and Emergency Department Transfers to Improve Medication Safety in Older Adults.? J Gerontol Nurs 2015, 41:8-13.
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[11] Yoon, S., Suero-Tejeda, N., Bakken, S. A Data Mining Approach for Examining Predictors of Physical Activity Among Urban Older Adults. J Gerontol Nurs 2015, 41: 14-20. [12] Machacova, K., Vankova, H., Volicer, L., Veleta, P., Holmerova, I. Dance as Prevention of Late Life Functional Decline Among Nursing Home Residents. Journal of Applied Gerontology, 2015(August), 1-18. ISSN 0733-4648. DOI 10.1177/0733464815602111 [13] Ehrler F, Wipfli R, Teodoro D, Sarrey E, Walesa M, Lovis C. Challenges in the Implementation of a Mobile Application in Clinical Practice: Case Study in the Context of an Application that Manages the Daily Interventions of Nurses. JMIR mhealth and uhealth. 2013;1(1):e7. [14] Kalisch B, Aebersold M. Interruptions and Multitasking in Nursing Care. The Joint Commission Journal on Quality and Patient Safety. 2010;36(3):126-132. [15] Tsopra R, Jais J, Venot A, Duclos C. Comparison of two kinds of interface, based on guided navigation or usability principles, for improving the adoption of computerized decision support systems: application to the prescription of antibiotics. Journal of the American Medical Informatics Association. 2014;21(e1):e107-e116. [16] Li J, Westbrook J, Callen J, Georgiou A. The role of ICT in supporting disruptive innovation: a multi-site qualitative study of Nurse Practitioners in Emergency Departments. BMC Med Inform Decis Mak. 2012;12(1):27. [17] Luff P, Heath C. Mobility in collaboration. ACM conference on Computer supported cooperative work. 1998. p. 305-314. [18] Novaresearch.com. QDSTM : Questionnaire Development System – NOVA Research Company [Internet]. 2016 [cited 2 February 2016]. Available from: http://www.novaresearch. com/QDS [19] Project-redcap.org. REDCap [Internet]. 2016 [cited 2 February 2016]. Available from: http://project-redcap.org [20] Li J, Westbrook J, Callen J, Georgiou A. The role of ICT in supporting disruptive innovation: a multi-site qualitative study of Nurse Practitioners in Emergency Departments. BMC Med Inform Decis Mak. 2012;12(1):27. [21] Stevenson J, Nilsson G, Petersson G, Johansson P. Nurses’ experience of using electronic patient records in everyday practice in acute/inpatient ward settings: A literature review. Health Informatics Journal. 2010;16(1):63-72. [22] Act No. 372/2011 Coll., on health services and the terms and conditions for the providing of such services (The Act on Healthcare Services), as amended by Act No. 167/2012 Coll. [23] 6. Overview of the national laws on electronic health records in the EU Member States (National Report for the Czech Republic) [Internet]. 2016 [cited 2 February 2016]. Available from: http://ec.europa.eu/health/ehealth/docs/ laws_czech_republic_en.pdf
IJBH – Volume 4 (2016), Issue 2
42
Original Article
Effect of Activity Tracker on Risk Factors of Metabolic Syndrome Martina Vlas´ akov´ a1 , Jan Muˇ z´ık1 1
Spin-off Company and Research Results Commercialization Center,
The First Faculty of Medicine, Charles University, Prague, Czech Republic
Abstract Background: The insufficient physical activity together with other factors causes the overweight, high blood pressure, and diabetes mellitus. The use of pedometers has a positive influence on increasing of physical activity and decreasing of the body weight. The Activity tracker brings a new challenge for the assessment of physical activity. They are most popular among the users, because they offer more functionalities than standard pedometers. Objectives:The aim of this study was to identify the impact of activity tracker using on the factors of the metabolic syndrome (complex of risk factors which occur often together and are likely to arise on the basis of insuline resistance). Methods: Totally 172 English written articles published before June 2016 were found by searching in the Web of Science database. Six trials have met the defined criterias. The positive influence of activity tracker on metabolic syndrome factors was proved by five trials.
Results:The results of three studies point to significant decreasing of participant weights. Next study showed decrease in participant waist circumference and results of other studies pointed to reduction of risk in development of type 2 diabetes. Conclusion: Results of these studies have indicated that the activity tracker has a positive influence on the high risk factors of metabolic syndrome. But the effect of using the activity tracker is ambiguous, hence there is a need of more high-quality random researches for assessment of these influences.
Keywords Activity tracker; Electronic activity monitor system, Physical acitivity; Metabolic syndrome; Self-monitoring
Correspondence to: Martina Vlas´ akov´ a The First Faculty of Medicine, Charles University Address: Kateˇrinsk´ a 32, 121 08 Prague 2 E–mail: [email protected]
Introduction Current status The World Health Organization reports 38 million deaths of total 56 million were caused by noncommunicable diseases such as cardiovascular diseases, cancer, diabetes, chronic respiratory diseases and others in 2012 [2]. 31 % deaths from noncommunicable diseases are a consequence of cardiovascular diseases (11 % deaths from the cardiovascular diseases are a consequences of high blood glucose) and 3 % deaths from the noncommunicable diseases are due to diabetes. One of three adults had overweight and every tenth person was obese in 2014. The prevalence of diabetes mellitus is 8,5 % [1]. Most of the noncommunicable diseases is the result of four IJBH – Volume 4 (2016), Issue 2
IJBH 2016; 4(2):42–45 received: July 15, 2016 accepted: August 15, 2016 published: September 20, 2016
specific behavior (use of tobacco, low physical activity, unhealthy diet and harmful alcohol consumption). It leads to four key metabolic changes (high blood pressure, overweight/obesity, higher blood glucose level and higher cholesterol) [2]. The insulin resistance, abdominal obesity, hypertension and hyperglycemia with atherogenic dyslipidemia form the basic component of metabolic syndrome (MS) [3]. Healthy dietary habits, regular physical activity and normal body weight lead to prevention of risk factors [2]. The pedometers were used for measuring of physical (non)activity in recent years. Results of the studies indicate that using of pedometer can motivate users to increase physical activity, consequently decrease their body weight [4]. The applicable form of feedback is necessary for the right motivation as not always the pedometer interc
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vention led to increasing of physical activity. The feedback must be adjusted to a characteristic of each patient. It is important the user can understand it because it is the necessary condition for changing his behavior and keep new habits [5]. Pedometers are simple and available tool for physical activity assessment. Information from pedometer are easy to understand and easy to use for the users – which is good for their wide application [6]. It is necessary to set up achievable goals in order to keep motivation which could be changed based on actual measured data. Pedometers offer information about number of steps but nothing about motion intensity, frequency or duration of the activity [7].
Activity tracker Activity trackers have a big potential in this way. They are more and more popular among users [9]. They offer simple data gathering, fast feedback to user and data sharing using computer or smart phone [8]. They provide more functionalities in comparison to pedometers – the caloric consumption, measuring of the hearth rate or quality of sleeping. Another option is providing of visual back feedback during physical activity, verbal encouraging and social comparing. Lewis [9] defined this device as Electronic Activity Monitor System (EAMS) – a wireless device that objectively measures lifestyle PA and can provide feedback, simultaneously display the basic activity information which awakens self-monitoring of user activity behavior using device display or via application.
Objective of the study The aim of this review was to identify the connection between using of activity tracker and metabolic syndrome risk factors.
Methods The articles were identified using the electronic database called Web of Science. Key words were following:(“Fitness and tracker” or “Digital and tracker” or “Activity and tracker” or “Wearable and tracker” or “Wearable and device” or “Wearable and technology” or “Pedometer” or “Self* and tracker”) and (“Metabolic disease syndrome” or “Metabolic risk faktors” or “Diabetes” or “High pressure” or “Syndrom X” or “Metabolic syndrome” or “Insuline resistence” or “Cardiovascular disease”). The searching was focused only on English written full text articles which were published before June 2016. The study designs, systematic reviews and studies with underage participants have not been included in this study. The selection has been done in four steps – the selection of duplicities and sorting by the title, abstract and full text. The criterion was the activity tracker intervention, or connection of pedometer with application or computer with remote couching on metabolic syndrome c
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risk factors. The chosen studies has been compared afterwards.
Results The 172 studies were found based on of the selected key words and 77 of them eliminated in first two steps. The full text were assessed in 95 trials. Big amount of trials in the third step was due the intense research of the specific EAMS using. The 86 studies were eliminated, because the EAMS was used as monitoring of physical activity without patient feedback, or because the study subject was of other focus. Two studies were eliminated due Spanish full text and one full text was impossible to find [10]. The six studies were filtered and put on the review.
Characteristics of the studies The chosen studies were very heterogeneous – in their duration, number of participants, following parameters and the way of intervention (Table 1). This is the reason why it was very difficult to draw a comparison. The longest study took 16 months [14], compared to the shortest which was four weeks study [16]. Three studies had much more than 200 participants [12, 13, 15], the average number of the participants was 182 but with the standard deviation (SD) equal to 178. The average number of participants which finished the study was 70 % ( SD=25). The overweight was the input parameter in three studies [13, 16, 14]. Two studies involved workers participating in a company prevention programs [12, 15] and one study included only women who had gestational diabetes in the past [11]. The objectively measurable data were the results of five studies – it was a waist circumference, body mass index (BMI), blood pressure, fasting plasma glucose and 2-h glucose levels on a75-g oral glucose tolerance test and measuring of HBA1c. One study used for measuring a questioner assessing risk of developing type 2 diabetes mellitus for measuring - the Australian Type 2 Diabetes Risk Assessment Tool (AUSDRISK) [12]. The participants were divided into three groups based on the results from AUSDRISK—high, intermediate and low risk of developing type 2 diabetes within five years.
The Outcomes of the studies The common objective of all selected studies was using of EAMS as an intervention tool with the main goal to find out the effect on chosen metabolic syndrome risk factors. Kim study [11] did not monitor any positive influence on decreasing of metabolic syndrome factors. But other studies monitored a positive influence. RoweRoberts study [12] registered decreasing of AUSDRISK score in 23 % of participants. And higher physical activity was measured in the participants with high risk of developing type 2 diabetes in comparison with other groups – the median number of steps was 8588 whereas IJBH – Volume 4 (2016), Issue 2
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the group of medium risk (7836 steps) and the group of low risk (7878 steps). Studies by Fukoa [13], Sepah [14] and Richardson [16] measured significant decreasing in participant body weights. The experiment of Freak-Poli [15] measured decreasing in the waist circumference by 1.6 cm (SD=5.9). The relationship among participants was common attribute in studies where participants came from the company prevention programs [12, 15]. These studies did not include the individual target program nor the structured behavioral program – in comparison with other studies. Although both studies measured significant positive influence on the monitoring parameters (decreasing AUDRISK score and waist circumference). None of the studies did not check the monitored parameters after the intervention with the goal to find out an influence on the metabolic syndrome risk factors in long term period.
Discussion This systematic review summarizes the results of EAMS intervention on the metabolic syndrome risk factors including only studies available in database Web of Science published before June 2016. The results indicate the EAMS could have a positive influence on the factors of metabolic syndrome – it means on decreasing of risk of developing type 2 diabetes, body weight and waist circumference decreasing. However the results aren’t unambiguous and more intense researches are needed. Objectively measured data and subjective assessments should be evaluating parameter of future studies. Participants of the research should be able to use proactively all the features that EAMS offers – including goals set individually, understandable feedback and data sharing among like-minded users. The studies should focus on the subsequent assessment of EAMS impact in long period after intervention.
Limits of review The big heterogeneity of chosen studies is the limit for this review which caused difficulties in comparison and assessment of the results. More preferable key words will be needed to choose in future work (including of a word “accelerometer”) and increasing in the number of searched databases. Simultaneously it is necessary to choose a key for comparing the quality of studies and their assessment (determination of minimal study lenght, study type, number of participants who complete the study). Acknowledgements This work was supported by the Specific research project SVV 260 267 and the p OP VK project: Mezin´arodn´ı spolupr´ ace na Fakultˇe biomedic´ıˇ nsk´eho inˇzen´ yrstv´ı CVUT, reg. no. project: CZ.1.07/2.3.00/20.0093.
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References [1] Global report on diabetes. World Health Organization; 2016. [2] World health statistics 2016: monitoring health for the SDGs, sustainable development goals. World Health Organization; 2016. [3] MeSH Browser Record [Internet]. U.S National Library of Medicine. U.S. National Library of Medicine. [4] Bravata DM, Smith-Spangler C, Sundaram V, Gienger AL, Lin N, Lewis R, et al. Using Pedometers to Increase Physical Activity and Improve Health. Jama. 2007;298(19):2296. [5] Polonsky WH, Fisher L. When Does Personalized Feedback Make A Difference? A Narrative Review of Recent Findings and Their Implications for Promoting Better Diabetes SelfCare. Curr Diab Rep Current Diabetes Reports. 2015;15(8). [6] Coffman MJ, Ferguson BL, Steinman L, Talbot LA, DunbarJacob J. A Health Education Pilot for Latina Women with Diabetes. Clinical Nursing Research. 2012;22(1):70–81. [7] Allet L, Knols RH, Shirato K, Bruin EDD. Wearable Systems for Monitoring Mobility-Related Activities in Chronic Disease: A Systematic Review. Sensors. 2010Aug;10(10):9026–52. [8] Bloomgarden Z, Li X-Y. Helping people with diabetes to exercise . Journal of Diabetes. 2014;7(2):150–2. [9] Lewis ZH, Lyons EJ, Jarvis JM, Baillargeon J. Using an electronic activity monitor system as an intervention modality: A systematic review. BMC Public Health. 2015;15(1). [10] Chang SA, Lee JM, Sohn TS, Son HS, Park SW, Baik SH, et al. Pedometer-Determined Physical Activity in Type 2 Diabetes in Korea. The Journal of Korean Diabetes Association J Korean Diabetes Assoc. 2007;31(1):83. [11] Kim C, Draska M, Hess MM, Wilson EE. A web-based pedometer programme in women with a recent history of gestational diabetes. Diabetic medicine?: a journal of the British Diabetic Association . 2012;29(2):278–83. [12] Rowe-Roberts D, Mueller FF, Cercos R. Preliminary results from a study of the impact of digital activity trackers on health risk statusPreliminary results from a study of the impact of digital activity trackers on health risk status. Studies in health technology and informatics . 2014Aug8;204:143–8. [13] Fukuoka Y, Gay, CL, Joiner KL. A Novel Diabetes Prevention Intervention Using a Mobile App A Randomized Controlled Trial With Overweight Adults at Risk. American journal of preventive medicine. 2015Aug;49(2): 223–37. [14] Sepah SC, Jiang L, Peters AL. Translating the Diabetes Prevention Program into an Online Social Network: Validation against CDC Standards. The Diabetes Educator. 2014Oct;40(4):435–43. [15] Freak-Poli RL, Wolfe R, Walls H, Backholer K, Peeters A. Participant characteristics associated with greater reductions in waist circumference during a four-month, pedometerbased, workplace health program. BMC Public Health. 2011;11(1):824. [16] Richardson CR, Brown BB, Foley S, Dial KS, Lowery JC. Feasibility of Adding Enhanced Pedometer Feedback to Nutritional Counseling for Weight Loss. J Med Internet Res Journal of Medical Internet Research. 2005;7(5).
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16 months
13 weeks
4 months
4 weeks
Sepah, SC (2014)
Kim, C (2012)
Freak‐Poli, RLA (2011)
Richardson, CR (2005)
Quasi‐ experiment
Quasi‐ experiment
RCT ‐ 2arms
Quasi‐ experiment
RCT ‐ 2arms
12
539
49
220
61
Quasi‐ experiment
Rowe ‐ Roberts, 7 months D (2014)
5 months
212
Typ of trial
Fukuoka, Y (2015)
Number of participants
Lenght of trial
100%
79%
42%
65%
100%
36%
Participan ts in the end of the study
AUSDRISK
AUSDRISK Score at Commencement High 21,2%, Medium 40,6%, Low 38,2%
weight, HBA1C
Study results
age ≥18 years, 57% woman, participants pedometer, waist circumference were recruited from ten website, emails workplaces BMI ≥ 30, at least one of the follo wing cardiovascular dise pedometer, ase risk factors: weight website, session diabetes, hypertension, hypercholesterolemia, o besity, or coronary arter y disease
Average weight loss was 1,9 kg over three weeks.
Average reduction of weist cirkumference was 1,6 cm (SD=5,9).
No significant changes from baseline to follow‐up were noted in the behavioural constructs or behaviours, particularly physical activity, between study arms.
yes
no
SportBrain Firs t Step
no
not specified
not specified
no
pedometer, mobil application, session, individalized goals
pedometer, The average HbA1C regressed from within the private online prediabetes range (5,7%‐6,4%) to the normal range Omron HJ‐320 social network, (< 5,7%), average of 5,0% Tri‐Axis session, health and 4,8% weight loss at 16 weeks and 12 months, Pedometer coaching and a respectively. wireless scale
yes
no
Fitbit Ultra
Device type
no
no
no
yes
no
yes
yes
no
yes
yes
yes
no
Participants Share Individually knew each results on targeted other before social program the start of networks the study
Intervention group lost an average of 6,2 (5,9) kg (–6,8% [5,7%]), The intervention group’s steps per day increased by 2,551 (4,712) compared to the control group’s decrease of 734(3,308) steps per day, Omron Active The intervention group had greater reductions in hip Style Pro HJA‐ 350IT circumference , blood pressure, intake of saturated fat, sugar‐sweetened beverages, The intervention had no significant effect on fasting lipid or glucose levels.
Participants with high AUSDRISK scores at commencement seemed to be the most motivated to increase activity levels and continue using the device over the period of the study, this participants activity tracker had the highest average steps per day over the pilot (8,588 compared to 7,836 for medium risk and 7,878 for low risk) , 23% of participants reducing their AUSDRISK score over the period of 7 months.
Intervention
fasting plasma pedometers, age ≥18 years, 100% glucose and 2‐h webbased women, gestational glucose levels on a75‐ education, diabetes delivery within g oral glucose internet forum the past 3 years tolerance test
age ≥18 years, BMI of ≥ 24 kg/m2 , 62% women, diagnosis of prediabetes
weight, BMI, hip circumference, age =55,2 (SD=9,0) , blood pressure, lipid BMI= 33,3 kg/m2 (SD=6,0), 77% women profile and glucose level
Rated values
Information about participants
yes
no
yes
yes
yes
no
Structured behavioral program
yes
yes
yes
yes
yes
yes
Self‐ monitoring
...
Autor (year of publication)
Table 1: Characteristics of the studies.
Vlas´akov´a M., Muˇz´ık J.– Effect of Activity Tracker on Risk Factors of Metabolic Syndrome
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IJBH – Volume 4 (2016), Issue 2
46
Short Original Article
The Clinical Educational Simulation Scenario – How to Create the Correct Design? Lenka Vondruˇskov´ a1 , Jan Hendl2 1 2
First Faculty of Medicine, Charles University, Prague, Czech Republic
Institute of Sociological Studies, Faculty of Social Sciences, Charles University, Prague, Czech Republic
Abstract The virtual clinical scenario was caused for educational support and for support in decision making. The advantage of the simulation scenario is feedback and active participation of the student. There are various designs of virtual scenarios available. Correspondence to: Lenka Vondruˇskov´ a First Faculty of Medicine, Charles University, Prague, Czech Republic Address: Kateˇrinsk´ a 32, 121 08 Prague 2 E–mail: [email protected]
Background The Association of American Medical Colleges defines the Virtual Patient (VP) as a specific type of computer program which simulates real clinical scenarios, guiding students in the role of health care provider when obtaining a medical history, during subsequent clinical examinations, and during diagnosis and the determination of a treatment plan. [1] Today, simulative computer clinical scenarios are used in health care for educational purposes. [2] The use of simulations in the education of non-medical health care workers provides students with training in decision making skills based on real clinical situations. [3] Cendan, Lok say about VP technology: “The main components of VPs include interactivity on the learners´ part (as opposed to passively watching videos), the simulation of medical condition, and the visual and/or physical presentation of the conditions. The manifestation of the VPs can differ greatly and include 1) case studies presented on web pages or CD-ROMs, 2) immersive virtual reality simulations, and 3) robotic human-scale mannequins.” The authors further add: “Simply stated, VPs are computer-based simulation of a patient and are typically composed of three components: inputs, simulation, and outputs.” [4] IJBH – Volume 4 (2016), Issue 2
The studies solving the correct design of the simulation scenario have given a recommedation for the construction.
Keywords Education, clinical, simulation, interactivity, feedback IJBH 2016; 4(2):46–48 received: July 15, 2016 accepted: August 15, 2016 published: September 20, 2016
Objectives The benefits of using technology in education include the possibility of remote access, instant feedback, and the opportunity to update content. [5] Using this form means that educational activities and achievements can be monitored. Virtual education programs provide the decision making experience in a relatively safe environment. [5] The author was involved in using a VP in the education of pharmaceutical workers. He points out that the use of computer technology in education can be as effective as traditional education methods; at the same time, he mentions that the clinical scenario is useful primarily for strategic decision making, which is also mentioned by Cook in his critical literary research from 2009. [6] In the published results of his study summarizing medical simulations, Issenberg points out that simulations make education easier under specific circumstances, such as: feedback mediation, repetition, curriculum integration, clinical variation, active student participation, result definition, and simulation validity. [7] In their study, Cendan and Lok mention the advantages of the VP compared to the standard patient: the VP enables the same experience repeatedly, provides feedback, enables revision of previous decisions and comparisons with best-practice protocols, and scenarios are available on-line. They also emphasize the opportunity to mediate a simulated examination of the patient with regard to specific symptoms, such as abnormal breathing sounds (which are difficult to explain in words), or specific neurological findings. [4] c
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Cendan and Lok describe the experimental theory of Kolb and Fryse, the so-called Cyclical-Learning Model. The Cyclical-Learning Model comprises several consecutive steps: specific experience, reflective observation, abstract conceptualization, and active experiment or plan. The authors point out that this theory motivates the student to be an active participant in the process. [4] The study mentions that the VP is a useful tool in comparison with zero, or no intervention, therefore making the learning process easier. [4]
Methods In 2013, a meta-analysis of simulation programs in teaching urgent medicine was undertaken. The limitation part of the study mentions a wide range of variation across the entire analysis; differences were related to the instructional design of individual programs, their speciality, and the students. The conclusion states that, “Technology–enhanced simulation for EM learners is associated with moderate or large favourable effects in comparison with no intervention and generally small and nonsignificant benefits in comparison with other instruction.” [8] Cook mentions the need for research to answer the question of how to effectively implement the VP [2]; he mentions the need for research into procedural operations training and research into the effectiveness of simulation instructional design. [8] “The Comparative Effectiveness of Instructional Design Features in Simulation-Based Education. Systematic Review and Meta-Analysis” study mentions a number of practical recommendations based on the identification of characteristic features of VP design, Issenberg being the author of feature identification. [9] The key features for efficient instruction design include: “range of difficulty, repetitive practice, distributed practice, cognitive interactivity, multiple learning strategies, individualized learning, mastery learning, feedback, longer time and clinical variation.” [9] The authors divided the results according to Kirkpatricks classification and abstract informations described as: “Satisfaction, learning – knowledge and skill, time, process and product, how to treat the patient (time and process), and results (effect on the patient).” [9] The authors of the study mention the need for research that would cast some light on the mechanism of efficient simulated education – “what works, for whom, and in which context.” [9]
Results The effect of virtual clinical scenarios is often compared to no, or zero intervention. [2] Cook presupposes that repetition until demonstration of the requested level, the advanced state of the organizers, and secure feedback can improve the educational results. He also adds in his c
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conclusion that further research into the issue of how to efficiently implement the VP is needed. [2] Cook states in the conclusion of another study: “In comparison with no intervention, technology-enhanced simulation training in health professions education is consistently associated with large effects for outcomes of knowledge, skills and behaviours and moderate effects for patient-related outcomes.” [10] The identification of the key features for the creation of the clinical simulation design could be a recommendation on how to construct an efficient simulation program. The results of the studies focused on research into the efficiency of virtual scenarios/tools in medical education may be ambiguous due to the substantial variability of their individual program designs, field differentiation, and also the students. [3, 8] It may be presumed that if we are to evaluate the efficiency of VP programs, it would be prudent to focus on a specific target group, a specific field, and a specific research subject for the efficiency of the VP program. Virtual clinical scenarios and tools are used in both medical and non-medical environments; therefore, the level of knowledge of health care workers, doctors, nurses, paramedics, and pharmaceutical workers, will also vary.
Conclusion The work will address the processing of case studies for non-medical health care fields, particularly general nursing. Five or six virtual clinical case scenarios will be used. Each will comprise a tree of solutions for the related clinical case/scenario. The case studies will focus on the intensive care unit. The efficiency of the program will be evaluated afterwards. Because the construction of virtual clinical case studies can be costly, the already existing technical solution in the Open Labyrinth environment will be used.
Acknowledgements The work was supported by the project of SVV-2016260 267 of Charles University.
References [1] Association of American Medical Colleges, 2007. Effective Use of Educational Technology in Medical Education: Summary Report of the 2006 AMC Colloquium on Educational Technology. AMC, Washington DC [2] Cook, D.A., Erwin, P.J., Triola, M.M. 2010. Computerized virtual patient in health professions education: a systematic review and meta-analysis. AcadMed. 2010 Oct, 85 (10): 1589-602. Doi: 10.1097/acm.ob013e3181edfe13 [3] Kim, J., Park, J.H., Shin, S. Effectiveness of simulation-based nursing education depending on fidelity: a meta-analysis. BMC Med Educ. 2016 May 23;16(1):152. doi: 10.1186/s12909-0160672-7
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[4] Cendan, J., Lok, B. The use of virtual patients in medical school curricula. Adv Physiol Educ. 2012 Mar;36(1):48-53. doi: 10.1152/advan.00054.2011 [5] Zlotos, L., Power, A., Chapman, P. A scenario-based virtual patient program to support substance misuse education. Pharm Educ. 2016 Apr. 25 80 (3) :48
[8] Ilgen, J.S., Sherbino, J., Cook, D.A. Technology-enhanced simulation in emergency medicine: a systematic review and meta-analysis. Acad Emerg Med. 2013 Feb;20(2):11727.doi:10.1111/acem.12076
[6] Cook, D.A., Triola, M.M., 2009. VPs: a critical literature review and proposed next steps. Medical Education 43, 303e311
[9] Cook, D., Hamstra, S.J., Brydges, R., Zendejas, B., Szostek, J.H., Wang, A.T., Erwin, P.J. and Hatala, R. Comparative effectiveness of instructional design features in simulation-based education: systematic review and metaanalysis. MedTeaCH 2013; 35: e867-898.
[7] Issenberg, S.B., McGaghie, W.C., Petrusa, E.R., LeeGordon, D., Scalese, R.J. Features and uses of high-fidelity medical simulations that lead to effective learning: a BEME systematic review. MedTeach 2005 Jan, 27(1): 10-28
[10] Cook, D., Hatala, R., Brydges, R., Zendejas, B., Szostek, J.H., Wang A.T. Technology enhanced simulation for health proffesions education a systematic review and metaanalysis. Jama 2011, 306: 978-88
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Abstract
Structuring Information from Narrative Clinical Reports Karel Zv´ ara1,2 , Marie Tomeˇ ckov´ a2 , Jan Peleˇska2 , Vojtˇ ech Sv´ atek3 , Jana Zv´ arov´ a1,2 1
Charles University, First Faculty of Medicine, Institute of Hygiene and Epidemiology 2 3
EuroMISE Mentor Association, Prague, Czech Republic
University of Economics, Faculty of Informatics and Statistics,
Department of Information and Knowledge Engineering, Czech Republic
Correspondence to: Karel Zv´ ara Institute of Hygiene and Epidemiology, First Faculty of Medicine, Charles University & General University Hospital in Prague Address: Studniˇ ckova 7, 128 00 Prague 2, Czech Republic
IJBH 2016; 4(2):49–50 received: July 15, 2016 accepted: August 15, 2016 published: September 20, 2016
E–mail: [email protected]
Background Sharing information about patients’ health status and related processes is a prerequisite for health service delivery in distributed, specialized and collaborative care settings. In that context, reusing information traditionally documented by health professionals in their narrative clinical reports is an important issue for quality and efficiency of medical decision making. Therefore, tools for extracting information inherent in narrative clinical report as data or knowledge [1] are essential for advancing health systems.
record and reused for medical decision support and quality assurance tasks.
Results
The three-phase preprocessing method was validated on 49 anonymous narrative clinical reports from the field of cardiology. First cardiologist annotated 1500 clinical terms found in 49 medical narrative reports to codebook terms using classification systems ICD 10, SNOMED CT, LOINC and LEKY. Second cardiologist validated annotations of the first cardiologist. Correct clinical terms and Objectives codebook terms were stored in the database to be reused for extracting structured information from other narrative Reusing health information in electronic health clinical reports. records, for medical decision support or quality assurance tasks requires advanced methodologies for extracting structured information from narrative clinical reports. Conclusion
Methods A three-phase preprocessing method (3PP method) was developed see [2]. In the first phase a narrative clinical report is tokenized. In the second phase the tokenized clinical report is normalized. The normalized clinical report is well readable for health professionals with the knowledge of the natural language used in the narrative clinical report. In the third phase the normalized clinical report is enriched with extracted structured information. The final result of the third phase of the 3PP method is the semi-structured normalized clinical report where extracted clinical terms are matched to codebook terms. Codebook terms can be stored in electronic health c
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We can extract structured information from Czech narrative clinical reports by the proposed 3PP method and link it to electronic health record. Structured information can support much better medical decision making and quality assurance tasks. In the same way, we can apply 3PP method to narrative clinical reports based on other natural languages. Narrative clinical reports are very important part of healthcare documentation but the reuse of them using information and communication technologies is difficult. Therefore it is important that we can extract at least part of this information and convert it to a structured form that is computer readable and can support reuse of clinical information and structuring data for subsequent processing. IJBH – Volume 4 (2016), Issue 2
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Zv´ara K. et al.– Structuring Information from Narrative Clinical Reports
Keywords Narrative clinical report, tokens, structured information, classification systems, nomenclatures
Acknowledgements The work was partially supported by the grant SVV 260 267 of Charles University.
IJBH – Volume 4 (2016), Issue 2
References [1] Zv´ arov´ a J, Vesel´ y A, Vajda I. Data, Information and Knowledge. In: Berka P, Rauch J, Zighe D.A. (Eds.) Data Mining and Medical Knowledge Management: Cases and Applications, 36 IGI Global, Hershey, 2009:1-36 [2] Zv´ ara K, Tomeˇ ckov´ a M, Peleˇska J, Sv´ atek V, Zv´ arov´ a J. Advancing electronic health record by structured information from narrative clinical reports [Submitted to Methods of Information in Medicine]
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IJBH
IJBH 2016
ISSN 1805-8698
An Official Journal of the EuroMISE Mentor Association
International Journal on Biomedicine and Healthcare
Část II – Česky Sémantická interoperabilita v biomedicíně a zdravotnictví
Part II – Czech
www.ijbh.org
II
IJBH – International Journal on Biomedicine and Healthcare
Obsah 1
S´emantick´ a interoperabilita v biomedic´ınˇe a zdravotnictv´ı ˇ Vesel´y A., Zv´ Svaˇcina S., arov´ a J.
2–3
Vn´ım´ an´ı a pˇrij´ım´ an´ı doporuˇcen´ych postup˚ u pro klinickou praxi praktick´ymi l´ekaˇri Bart´ akov´ a J., Moravˇc´ıkov´ a D., Pavlakis A., Jiskra J.
4–6
Bezpeˇcnost dat v biomedic´ınˇe Berger J., Beyr K.
P˚ uvodn´ı ˇcl´ anek
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Poruchy gen˚ u kolagenu typ I u ˇcesk´ych pacient˚ u s osteogenesis imperfecta Hruˇskov´ a L., Mazura I.
P˚ uvodn´ı ˇcl´ anek
10–14
Stanoven´ı pohlav´ı plodu pomoc´ı testov´ an´ı voln´e DNA Hynek M., Zv´ arov´ a J., Zembol F., Mareˇsov´ a I., Stejskal D.
P˚ uvodn´ı ˇcl´ anek
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Identifikace fenotypu PAS asociovan´eho s fol´ aty na z´ akladˇe rozd´ıln´ych expres´ı mezi muˇzi a ˇzenami ˇarek M. Krsiˇcka D., Pourov´ a R., S´
16-19
Poˇc´ıtaˇcov´e modelov´ an´ı dyssynchronn´ıho srdce Loˇzek M., Janouˇsek J., Riedlbauchov´ a L., Lhotsk´ a L.
20-21
Standardizace sbˇeru dat o bezpeˇcnosti l´eˇciv´ych pˇr´ıpravk˚ u v pˇredregistraˇcn´ıch klinick´ych studi´ıch a jeho optimalizace Montoniov´ a R., Zv´ arov´ a J.
Kr´ atk´y p˚ uvodn´ı ˇcl´ anek
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Komunitn´ı v´yvoj a pokroˇcil´e funkce nositeln´e elektroniky v oblasti self-managementu diabetu Muˇzn´y M., Arsand E., Muˇz´ık J.
P˚ uvodn´ı ˇcl´ anek
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ˇ a v zahraniˇc´ı Elektronick´y zdravotn´ı z´ aznam v CR Schlenker A., Hr˚ uza T.
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Coancestry koeficient Slov´ ak D., Zv´ arov´ a J.
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Identifikace kuˇr´ ak˚ u v nestrukturovan´e l´ekaˇrsk´e dokumentaci Stonov´ a M.
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Softwarov´ a sada pro sbˇer a zpracov´ an´ı dat v dlouhodob´e p´eˇci V´ıteˇckov´ a S., Krupiˇcka R., Klemp´ıˇr O., Szab´ o Z., Vaˇ nkov´ a H., Kuckir M., Holmerov´ a I.
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Vliv fitness n´ aramku na rizikov´e faktory metabolick´eho syndromu Vlas´ akov´ a M., Muˇz´ık J.
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Klinick´y vzdˇel´ avac´ı simulativn´ı sc´en´ aˇr – jak tvoˇrit spr´ avn´y design? Vondruˇskov´ a L., Hendl J.
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Strukturov´ an´ı informace z klinick´ych zpr´ av Zv´ ara K., Tomeˇckov´ a M., Peleˇska J., Sv´ atek V., Zv´ arov´ a J.
IJBH – Volume 4 (2016), Issue 2
Pˇredmluva Kr´ atk´y p˚ uvodn´ı ˇcl´ anek
Abstrakt
P˚ uvodn´ı ˇcl´ anek
Abstrakt Kr´ atk´y p˚ uvodn´ı ˇcl´ anek P˚ uvodn´ı ˇcl´ anek Abstrakt
Abstrakt
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1
Pˇredmluva
S´ emantick´ a interoperabilita v biomedic´ınˇ e a zdravotnictv´ı ˇ ep´ Stˇ an Svaˇ cina1 , Arnoˇst Vesel´ y2 , Jana Zv´ arov´ a3 1 2 3
ˇ a republika 3. intern´ı klinika 1. l´ekaˇrsk´e fakulty Univerzity Karlovy a Vˇseobecn´e fakultn´ı nemocnice v Praze, Cesk´
ˇ e zemˇedˇelsk´e univerzity v Praze, Cesk´ ˇ a republika Katedra informaˇcn´ıho inˇzen´yrstv´ı Fakulty ekonomiky a managementu Cesk´
´ ˇ a republika Ustav hygieny a epidemiologie, 1. l´ekaˇrsk´ a fakulta, Univerzita Karlova a Vˇseobecn´ a fakultn´ı nemocnice v Praze, Cesk´
Druh´e ˇc´ıslo ˇcasopisu International Journal on Biomedicine and Healthcare (IJBH) publikuje v tomto ˇc´ısle 15 ˇcl´ank˚ u doktorand˚ u k t´emat˚ um biomedic´ınsk´e informatiky. Vˇsechny ˇcl´anky jsou publikov´ any v angliˇctinˇe a ˇceˇstinˇe. Terminologie ˇcl´ank˚ u byla analyzov´ ana pomoc´ı vybran´ ych klasifikaˇcn´ıch syst´em˚ u a nomenklatur a diskutov´ana na semin´aˇri, kter´ y se konal 22. z´ aˇr´ı 2016 v Akademick´em klubu 1. l´ekaˇrsk´e fakulty Univerzity Karlovy v Praze. S´emantick´a interoperabilita a schopnost z´ıskat konkr´etn´ı informace pomoc´ı technick´ ych prostˇredk˚ u jsou z´akladn´ı podm´ınkou pro vyuˇzit´ı technologi´ı telemedic´ıny v elektronick´em zdravotnictv´ı. S´emantick´ a interoperabilita ˇreˇs´ı probl´emy, jak nejl´epe usnadnit k´ odov´ an´ı, pˇrenos a vyuˇzit´ı informace o zdravotnick´ ych sluˇzb´ ach mezi poskytovateli, pacienty a obˇcany. Podstatou s´emantick´e interoperability je podpoˇrit spolupr´ aci mezi lidsk´ ymi akt´ery a zdravotnick´ ymi organizacemi, nikoli pouze interoperabilitu mezi poˇc´ıtaˇci. Schopnost syst´emu porozumˇet zas´ılan´ ym u ´daj˚ um (s´emantickou interoperabilitu) vyˇzaduje pouˇzit´ı stejn´e terminologie (tj. klasifikaˇcn´ıch syst´em˚ u a nomenklatur) a pouˇzit´ı stejn´eho jazyka pro komunikaci a pro z´ aznam (datov´e standardy). Pokud je informace v biomedic´ınˇe a zdravotnictv´ı sd´ılena voln´ ym textem, pˇredpokladem pro s´emantickou interoperabilitu je pˇr´ıstup k jeho smyslu. Pravdˇepodobnˇe nejlepˇs´ım pouˇziteln´ ym obecn´ ym klasifikaˇcn´ım syst´emem pro zdravotnictv´ı je SNOMED CT. Mezin´ arodn´ı organizace IHTSDO (International Health Terminology Standards Development Organization) byla zaloˇzena v roce 2012. IHTSDO se zav´azala k udrˇzov´ an´ı a rozˇsiˇrov´an´ı terminologie v oblasti zdravotn´ı p´eˇce a k dosaˇzen´ı toho, aby SNOMED CT byl celosvˇetov´ ym spoleˇcn´ ym jazykem pro zdravotnickou terminologii. SNOMED CT je nejkomplexnˇejˇs´ı a nejpˇresnˇejˇs´ı klinick´ a terminologie na svˇetˇe. SNOMED CT byl vyvinut v ˇsirok´e mezin´ arodn´ı spolupr´aci tak, aby bylo zajiˇstˇeno, ˇze splˇ nuje r˚ uzn´e potˇreby a oˇcek´av´an´ı celosvˇetov´e l´ekaˇrsk´e profese a bude pˇrij´ım´an jako celosvˇetov´ y spoleˇcn´ y jazyk pˇri p´eˇci o zdrav´ı. Pacienti
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a zdravotniˇct´ı pracovn´ıci budou m´ıt prospˇech z lepˇs´ıch zdravotn´ıch z´aznam˚ u, klinick´a rozhodnut´ı a anal´ yzy budou m´ıt vyˇsˇs´ı kvalitu, konzistenci a bezpeˇcnost pˇri poˇ a repubskytov´an´ı zdravotn´ı p´eˇce. V roce 2012 se Cesk´ lika stala ˇclenem IHTSDO, spojuj´ıc´ı celosvˇetov´e u ´sil´ı vyvinout, udrˇzovat a umoˇznit pouˇz´ıv´an´ı SNOMED CT ve zdravotnick´ ych syst´emech po cel´em svˇetˇe. Po vstupu ˇ e republiky do IHTSDO je SNOMED CT k dispozici Cesk´ ˇ e republice pro pouˇzit´ı v elektronick´ v cel´e Cesk´ ych zdravotn´ıch z´aznamech, v´ yzkumu v oblasti zdrav´ı a dalˇs´ıch ˇ e republice maj´ı aplikac´ıch. Uˇzivatel´e terminologi´ı v Cesk´ tak´e pˇr´ıstup k novˇe vytv´aˇren´ ym zdroj˚ um v IHTSDO. Toto ˇc´ıslo IJBH obsahuje pˇr´ıspˇevky doktorand˚ u zab´ yvaj´ıc´ıch se nov´ ymi technologiemi pro telemedic´ınu (Muˇzn´ y a kol., Vlas´akov´a a kol.), smˇernicemi klinick´e praxe (Bart´akov´a a kol.), klinick´ ymi simulacemi vzdˇel´avac´ıch sc´en´aˇr˚ u (Vondruˇskov´a a kol.) a poˇc´ıtaˇcov´ ym modelov´an´ım (Loˇzek a kol.). D´ale jsou zde pr´ace k v´ yznamu bezpeˇcnosti biomedic´ınsk´ ych dat (Berger a kol.), elektronick´ ych zdravotn´ıch z´aznam˚ u (Schlenker a kol.) a k softwarov´ ym n´astroj˚ um pro standardizovan´ y sbˇer dat a jejich zpracov´an´ı (V´ıteˇckov´a a kol., Montoniova a kol.). Extrahovat informace z narativn´ıch klinick´ ych zpr´av je tak´e d˚ uleˇzitou ot´azkou biomedic´ınsk´e informatiky (Zv´ara a kol., Stonov´a). Genetick´a data zaˇc´ınaj´ı b´ yt d˚ uleˇzitou informac´ı pro mnoho klinick´ ych discipl´ın. Kromˇe v´ yznamu stochastick´ ych pˇr´ıstup˚ u v genetice (Slov´ak a kol.) je vyuˇzit´ı genetick´ ych dat d˚ uleˇzit´e pro r˚ uzn´e klinick´e n´ahledy na osteogenesis imperfecta (Hruˇskov´a a kol.) a pˇri stanoven´ı pohlav´ı plodu pomoc´ı neinvazivn´ıho testov´an´ı voln´e DNA (Hynek a kol.) a v identifikaci odliˇsn´eho fenotypu ASD (Krsiˇcka a kol.). Editoˇri pˇrej´ı vˇsem z´ajemc˚ u o t´emata zveˇrejnˇen´ ych prac´ı neruˇsen´e ˇcten´ı a doufaj´ı, ˇze i dalˇs´ı v´ yvoj SNOMED CT bude podporovat i ˇceskou verzi SNOMED CT a dojde n´aslednˇe i k jej´ımu zaveden´ı do ˇcesk´eho elektronick´eho zdravotnictv´ı.
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Kr´ atk´ y p˚ uvodn´ı ˇ cl´ anek
Vn´ım´ an´ı a pˇrij´ım´ an´ı doporuˇ cen´ ych postup˚ u pro klinickou praxi praktick´ ymi l´ ekaˇri Jana Bart´ akov´ a1,2 , Dana Moravˇ c´ıkov´ a3 , Andreas Pavlakis4 , Jan Jiskra2 1 2
´ ˇ a republika Ustav biofyziky a informatiky, 1. l´ekaˇrsk´ a fakulta, Univerzita Karlova, Praha, Cesk´
ˇ a republika 3. intern´ı klinika, Vˇseobecn´ a fakultn´ı nemocnice a 1. l´ekaˇrsk´ a fakulta, Univerzita Karlova, Praha, Cesk´ 3
ˇ a l´ekaˇrsk´ ˇ a republika Spoleˇcnost vˇseobecn´eho l´ekaˇrstv´ı, Cesk´ a spoleˇcnost Jana Evangelisty Purkynˇe, Praha, Cesk´ 4
Ekonomick´e a obchodn´ı vˇedy, Neapolis Univerzita Pafos, Pafos, Kypersk´ a republika
Abstrakt Doporuˇcen´e postupy pro klinickou praxi by mˇely v´est ke zlepˇsen´ı zdravotn´ı p´eˇce. Samotn´y form´at doporuˇcen´ı ale m˚ uˇze silnˇe ovlivnit jejich vn´ım´an´ı, u ´spˇeˇsn´e pˇrijet´ı a n´aslednou implementaci do klinick´e praxe.
Cesta ke zlepˇsen´ı jejich form´atu a n´aslednˇe i jejich efektivity je porozumˇet jejich vn´ım´an´ı a odhalit pˇrek´aˇzky jejich pˇrijet´ı koncov´ymi uˇzivateli.
Kl´ıˇ cov´ a slova Doporuˇcen´e postupy, praktiˇct´ı l´ekaˇri, implementace, klinick´a praxe, efektivita
Kontakt: IJBH 2016; 4(2):2–3 Jana Bart´ akov´ a ´ Ustav biofyziky a informatiky 1. LF UK Adresa: Salmovsk´ a 478/1, 128 00, Praha 2 E–mail: [email protected]
C´ıle v´ yzkumu
zasl´ ano: 15. ˇ cervence 2016 pˇrijato: 15. srpna 2016 publikov´ ano: 20. z´ aˇr´ı 2016
a tud´ıˇz vedou k zlepˇsen´ı zdravotn´ı p´eˇce, a t´ım produkuj´ı zdravotn´ı pˇr´ınosy [2].
V souˇcasn´e dobˇe bˇeˇz´ı v´ yzkum mezi praktick´ ymi l´ekaˇri Bohuˇzel jejich dopad z˚ ust´av´a omezen´ y, a to zejm´ena ˇ e republice a v Kypersk´e republice. Tato studie m´a v Cesk´ z d˚ uvodu jejich nejist´e efektivity [3] a prokazatelnˇe za c´ıl z´ıskat d˚ uleˇzit´e informace pro efektivn´ı implemenn´ızk´eho pˇrijet´ı do klinick´e praxe [4, 5, 6, 10, 11, 12]. taci doporuˇcen´ ych postup˚ u do klinick´e praxe praktick´ ych Doporuˇcen´ı jsou ˇcasto vn´ım´ana jako ohroˇzuj´ıc´ı l´ekaˇrskou l´ekaˇr˚ u. Naˇsimi konkr´etn´ımi c´ıli jsou: autonomii, zjednoduˇsuj´ıc´ı proces l´ekaˇrsk´eho rozhodov´an´ı yvoj a br´an´ıc´ı 1. identifikace pˇrek´ aˇzek a faktor˚ u, kter´e ovlivˇ nuj´ı a jsou povaˇzov´ana za pˇr´ıliˇs rigidn´ı, brzd´ıc´ı v´ individu´aln´ımu pˇr´ıstupu k pacient˚ um [2, 7, 8]. Nen´ı tud´ıˇz vn´ım´an´ı a pˇrij´ım´ an´ı doporuˇcen´ı v klinick´e praxi; pˇrekvapen´ım, ˇze vzhledem ke vˇsem tˇemto probl´em˚ um je 2. identifikace specifick´ ych postoj˚ u a potˇreb prak- pochopen´ı pˇrek´aˇzek, kter´e ovlivˇ nuj´ı vn´ım´an´ı a pˇrij´ım´an´ı ˇ e republice a v Kypersk´e re- doporuˇcen´ı do klinick´e praxe, d˚ tick´ ych l´ekaˇr˚ u v Cesk´ uleˇzitou souˇc´ast´ı v´ yvoje publice s n´asledn´ ym srovn´ an´ım; efektivn´ı strategie implementace. 3. navrˇzen´ı strategi´ı zefektivˇ nuj´ıc´ıch implementaci a pˇrijet´ı doporuˇcen´ ych postup˚ u do klinick´e praxe.
Souˇ casn´ y stav pozn´ an´ı V posledn´ıch 20 letech roste z´ ajem o doporuˇcen´e postupy pro klinickou praxi. Ty jsou definov´ any jako syste” maticky se vyv´ıjej´ıc´ı stanoviska pom´ ahaj´ıc´ı praktikuj´ıc´ım l´ekaˇr˚ um a pacient˚ um pˇri rozhodov´ an´ı o vhodn´e zdravotn´ı p´eˇci za specifick´ ych klinick´ ych okolnost´ı“ [1]. Jsou d˚ uleˇzit´ ym n´astrojem pozn´ an´ı, protoˇze informuj´ı praktick´e l´ekaˇre o spr´avn´e klinick´e praxi, podporuj´ı jej´ı n´ asledov´an´ı, IJBH – Volume 4 (2016), Issue 2
Uplatnˇ en´ı v biomedic´ınˇ e a zdravotnictv´ı Zdravotnick´e syst´emy celosvˇetovˇe investuj´ı nemal´e prostˇredky do zvyˇsov´an´ı kvality za u ´ˇcelem podpory klinicky a n´akladovˇe efektivn´ı zdravotn´ı p´eˇce. V pˇr´ıpadˇe doporuˇcen´ı pro klinickou praxi jsou zdroje vynakl´ad´any do jejich v´ yvoje, ale pouze mal´a pozornost je vˇenov´ana ochotˇe l´ekaˇr˚ u tato doporuˇcen´ı n´asledovat. Je diskutabiln´ı, zda m´a cenu vyv´ıjet dalˇs´ı doporuˇcen´ı do t´e doby, neˇz c
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Bart´akov´a J. a kol.– Vn´ım´an´ı a pˇrij´ım´an´ı doporuˇcen´ych postup˚ u pro klinickou praxi praktick´ymi l´ekaˇri
bude vyˇreˇsen probl´em s jejich ˇspatn´ ym vn´ım´ an´ım a nedostateˇcn´ ym pˇrijet´ım koncov´ ymi uˇzivateli. V naˇs´ı pilotn´ı studii vedeme dotazn´ıkov´e ˇsetˇren´ı mezi ˇ e republice a v Kypersk´e repraktick´ ymi l´ekaˇri v Cesk´ publice. Dotazn´ık byl se souhlasem pˇrevzat z anglick´e studie [9] a upraven pro naˇse potˇreby. Byl rozesl´an a schv´alen vzorkem ˇcesk´ ych a kypersk´ ych praktick´ ych l´ekaˇr˚ u. Nyn´ı rozes´ıl´ ame dotazn´ık mezi praktick´e l´ekaˇre, kteˇr´ı byli n´ahodnˇe vybr´ ani. Naˇse hypot´eza je, ˇze doporuˇcen´ı nejsou praktick´ ymi l´ekaˇri kompletnˇe pˇrij´ım´ana a specifika dan´e zemˇe ovlivˇ nuj´ı efektivitu implementace a vn´ım´an´ı doporuˇcen´ı koncov´ ymi uˇzivateli. Jak m˚ uˇzeme vidˇet z ned´ avn´ ych studi´ı [10, 11, 12], je v´ yzkum tˇechto probl´em˚ u pˇr´ıleˇzitost´ı ke zlepˇsen´ı vyd´avan´ ych doporuˇcen´ı. Protoˇze zlepˇsen´ı implementace a pˇrijet´ı doporuˇcen´ı bˇeˇznˇe vyˇzaduj´ı v´ıce pozornosti a u ´sil´ı neˇz jejich samotn´e navrˇzen´ı, vˇeˇr´ıme, ˇze v´ ysledky naˇs´ı pilotn´ı studie poskytnou dobr´ y z´ aklad pro budouc´ı v´ yzkum s mezin´arodn´ım srovn´ an´ım.
Podˇ ekov´ an´ı Tato pr´ace byla podpoˇrena projektem SVV-2016260267 Univerzity Karlovy.
Reference [1] Institute of Medicine, Guidelines for Clinical Practice: from development to use. Washington, D.C.: National Academies Press, 1992. [2] T. Delamothe, Wanted: guidelines that doctors will follow.“, ” BMJ, vol. 307, no. 6898, p. 218, Jul. 1993. [3]
Guidelines for doctors in the New World.“, Lancet (London, ” England), vol. 339, no. 8803, pp. 1197–8, May 1992.
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[4] L. C. Brown, J. A. Johnson, S. R. Majumdar, R. T. Tsuyuki, and F. A. McAlister, Evidence of suboptimal management of ” cardiovascular risk in patients with type 2 diabetes mellitus and symptomatic atherosclerosis.“, CMAJ, vol. 171, no. 10, pp. 1189–92, Nov. 2004. [5] E. Kendall, N. Sunderland, H. Muenchberger, and K. Armstrong, When guidelines need guidance: considerations and ” strategies for improving the adoption of chronic disease evidence by general practitioners.“, J. Eval. Clin. Pract., vol. 15, no. 6, pp. 1082–90, Dec. 2009. [6] J. Grimshaw and I. Russell, Achieving health gain through cli” nical guidelines. I: Developing scientifically valid guidelines.“, Qual. Health Care, vol. 2, no. 4, pp. 243–8, Dec. 1993. [7] J. M. Grimshaw and I. T. Russell, Effect of clinical guideli” nes on medical practice: a systematic review of rigorous evaluations.“, Lancet (London, England), vol. 342, no. 8883, pp. 1317–22, Nov. 1993. [8] P. E. Dans, Credibility, cookbook medicine, and common ” sense: guidelines and the college.“, Ann. Intern. Med., vol. 120, no. 11, pp. 966–8, Jun. 1994. [9] A. McColl, H. Smith, P. White, and J. Field, General practi” tioner’s perceptions of the route to evidence based medicine: a questionnaire survey.“, BMJ, vol. 316, no. 7128, pp. 361–5, Jan. 1998. [10] Y.-M. Kim, S. J. Lee, S. J. Jo, and K. N. Park, Implemen” tation of the guidelines for targeted temperature management after cardiac arrest: a longitudinal qualitative study of barriers and facilitators perceived by hospital resuscitation champions.“, BMJ Open, vol. 6, no. 1, p. e009261, Jan. 2016. [11] M. C. W. Joosen, K. M. van Beurden, B. Terluin, J. van Weeghel, E. P. M. Brouwers, and J. J. L. van der Klink, Improving ” occupational physicians’ adherence to a practice guideline: feasibility and impact of a tailored implementation strategy.“, BMC Med. Educ., vol. 15, p. 82, Jan. 2015. [12] Z. Trogrli´ c, E. Ista, H. H. Ponssen, J. F. Schoonderbeek, F. Schreiner, S. J. Verbrugge, A. Dijkstra, J. Bakker, and M. van der Jagt, Attitudes, knowledge and practices concerning deli” rium: a survey among intensive care unit professionals.“, Nurs. Crit. Care, Mar. 2016.
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P˚ uvodn´ı ˇ cl´ anek
Bezpeˇ cnost dat v biomedic´ınˇ e Jiˇr´ı Berger1 , Karel Beyr1 1
´ ˇ a republika Ustav patologick´e fyziologie, 1. l´ekaˇrsk´e fakulty Univerzity Karlovy, Praha, Cesk´
Abstrakt Aktu´aln´ım t´ematem v biomedic´ınˇe je digitalizace dat. Ta pˇrin´aˇs´ı efektivnˇejˇs´ı zpracov´an´ı, ale z´aroveˇ n potenci´aln´ı bezpeˇcnostn´ı a etick´a rizika. Tˇemto rizik˚ um se nedost´av´a takov´a pozornost, jakou si zasluhuj´ı. V ˇcl´anku jsme se zamˇeˇrili na r˚ uzn´e zp˚ usoby ˇsifrov´an´ı digitalizovan´ych dat a ke kaˇzd´emu jsme popsali pˇr´ınosy a nev´yhody. Ve vˇetˇsinˇe pˇr´ıpad˚ u jdou bezpeˇcnost a efektivita vyuˇzit´ı proti sobˇe.
Popisujeme i zp˚ usoby, kter´e umoˇzn ˇuj´ı efektivn´ı zpracov´an´ı zachov´avaj´ıc´ı ide´aln´ı zabezpeˇcen´ı, ty jsou zat´ım bohuˇzel pouze ve st´adiu v´yvoje.
Kl´ıˇ cov´ a slova Biomedic´ınsk´e ˇsifrov´an´ı
u ´daje,
bezpeˇcnost
dat,
homomorfn´ı
Kontakt: IJBH 2016; 4(2):4–6 Jiˇr´ı Berger ´ Ustav patologick´ e fyziologie, 1. l´ ekaˇrsk´ e fakulty Univerzity Karlovy ˇ a republika Adresa: U Nemocnice 5, 128 53 Praha 2, Cesk´
zasl´ ano: 15. ˇ cervence 2016 pˇrijato: 15. srpna 2016 publikov´ ano: 20. z´ aˇr´ı 2016
E–mail: [email protected]
C´ıle v´ yzkumu Biomedic´ına je specifick´ a v tom, ˇze na rozd´ıl od ostatn´ıch oblast´ı vyuˇz´ıv´ a velkou ˇc´ ast nestrukturovan´ ych heterogenn´ıch dat, jejichˇz zpracov´ an´ı a t´ım i zabezpeˇcen´ı je ve srovn´ an´ı s bˇeˇznˇe zpracov´ avan´ ymi homogenn´ımi strukturovan´ ymi daty specifickou oblast´ı. Uved’me napˇr´ıklad: • Rozd´ıly mezi pˇr´ıstupy k zabezpeˇcen´ı nestrukturovan´ ych heterogenn´ıch dat oproti strukturovan´ ym homogenn´ım dat˚ um. • Zabezpeˇcen´ı prov´ adˇen´e za pomoci ˇsifrov´ an´ı, kter´e pˇrin´aˇs´ı obecn´ y konflikt mezi u ´rovn´ı zabezpeˇcen´ı a pˇr´ıstupu k dat˚ um. • Posouzen´ı vhodnosti ˇsifrovac´ıch algoritm˚ u pro segmentovˇe orientovan´e zabezpeˇcen´ı velk´ ych objem˚ u dat za pomoci r˚ uzn´ ych kl´ıˇc˚ u.
technologie nab´ızej´ı rychlejˇs´ı a efektivnˇejˇs´ı zpracov´an´ı a sd´ılen´ı obrovsk´eho mnoˇzstv´ı biomedic´ınsk´ ych dat, coˇz je obecnˇe vysoce pˇr´ınosn´e pro v´ yzkum, prevenci, zpracov´an´ı trendov´ ych a statistick´ ych v´ ystup˚ u a mnoho dalˇs´ıch oblast´ı, kter´e mohou pˇrin´aˇset vysokou pˇridanou hodnotu jak pro populaci, tak pro jednotliv´e pacienty. Zdravotn´ı p´eˇce pracuje s velmi citliv´ ymi daty pacient˚ u a jejich ochrana je hlavn´ım z´ajmem. Celosvˇetovˇe doch´az´ı k zav´adˇen´ı elektronizace biomedic´ınsk´ ych dat. V USA napˇr´ıklad prob´ıh´a n´arodn´ı v´ yzkumn´ y program Health Information Techno” logy for Economic and Clinical Health Act“, (HITECH), kter´ y se zamˇeˇruje na n´avrh a definici pravidel, minimalizuj´ıc´ıch zneuˇzit´ı a u ´nik citliv´ ych biomedic´ınsk´ ych, pˇr´ıpadnˇe konkr´etn´ıch pacientsk´ ych dat. Kl´ıˇcov´ ym parametrem pak je maximalizace efektivity zpracov´an´ı a kvality v´ ystupn´ıch dat pˇri zachov´an´ı vˇsech bezpeˇcnostn´ıch a etick´ ych pravidel, kter´a s takov´ ymi daty souvis´ı.
Souˇ casn´ y stav pozn´ an´ı
• Ovˇeˇren´ı zp˚ usob˚ u vyhled´ av´ an´ı v ˇsifrovan´ ych datech tak, aby nemohlo doj´ıt k prolomen´ı definovan´e St´ale rychleji se vˇetˇsina heterogenn´ıch biomeu ´rovnˇe bezpeˇcnosti, ale pˇresto bylo moˇzn´e na de- dic´ınsk´ ych informac´ı sdruˇzuje pod syst´emy umoˇzn ˇuj´ıc´ı jeˇ ım komfinovan´e u ´rovni z´ısk´ avat zobecnˇen´e nebo anonymi- jich centralizovan´e zpracov´an´ı v re´aln´em ˇcase. C´ zovan´e v´ ysledky. pletnˇejˇs´ı z´aznamy do zpracov´an´ı vstupuj´ı, t´ım vyˇsˇs´ı hodnotu mohou m´ıt pˇri zpracov´an´ı r˚ uzn´ ych druh˚ u anal´ yz • Aplikace v biomedic´ınˇe, kter´e pˇrevzaly zp˚ usoby na z´akladˇe zdravotn´ı dokumentace cel´e populace a soubezpeˇcnosti dat a ˇsifrov´ an´ı z jin´ ych obor˚ u, kde visej´ıc´ıch biomedic´ınsk´ ych informac´ı. obecnˇe doch´az´ı k ˇr´ adovˇe vyˇsˇs´ım investic´ım do t´eto U kaˇzd´eho takto nastaven´eho projektu, aˇc autoˇri tooblasti (telekomunikace, finanˇcn´ı instituce). hoto ˇcl´anku pˇripouˇst´ı, ˇze vˇetˇs´ı neˇz technick´ y to bude y probl´em, je patrnˇe nejefektivnˇejˇs´ım V dneˇsn´ı dobˇe je trend digitalizace zdravotnick´ ych dat organizaˇcnˇe etick´ usobem pr´ace poskytov´an´ı anonymizovan´ ych informac´ı skuteˇcnost´ı. T´ım nar˚ ust´ a potˇreba nastaven´ı a definice od- zp˚ pov´ıdaj´ıc´ıho zabezpeˇcen´ı takov´ ych dat. Souˇcasn´e digit´aln´ı odborn´e veˇrejnosti jako vstupn´ıho zdroje pro dalˇs´ı zpraIJBH – Volume 4 (2016), Issue 2
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cov´an´ı. V dneˇsn´ı dobˇe je obecn´ ym pravidlem zpˇr´ıstupnit informace k nekomerˇcn´ım aktivit´ am v maxim´aln´ı m´ıˇre, pokud projekt z´ısk´ a plnou nebo ˇc´ asteˇcnou finanˇcn´ı podporu z veˇrejn´ ych zdroj˚ u a v pˇrev´ aˇzn´e vˇetˇsinˇe pˇr´ıpad˚ u jsou tyto projekty zˇrizov´ any nebo spolufinancov´any pr´avˇe t´ımto zp˚ usobem. Nast´ av´ a tak kl´ıˇcov´ a ot´ azka, jak biomedic´ınsk´e informace, obzvl´ aˇstˇe s ohledem na citlivost uchov´avan´ ych dat, poskytnout odborn´e veˇrejnosti v podobˇe, kter´a neomez´ı potenci´ al dalˇs´ıho zpracov´an´ı a v´ yzkumu a souˇcasnˇe zajist´ı neprolomitelnou hranici soukrom´ı a uchov´an´ı citliv´ ych u ´daj˚ u. I pˇres anonymizaci nebo zobecnˇen´ı pouˇzit´ ych populaˇcn´ıch dat hroz´ı riziko nepˇr´ım´e identifikace konkr´etn´ıch informac´ı o pacientech, coˇz i pˇri sebemenˇs´ı pochybnosti posunuje technick´ y probl´em do etick´e roviny s dopadem na komplexn´ı vn´ım´ an´ı t´eto oblasti[1]. Proto je nutn´e analyzovat r˚ uzn´e zp˚ usoby zabezpeˇcen´ı a ˇsifrov´an´ı a vybrat z nich takov´e, kter´e spln´ı tyto specifick´e poˇzadavky a souˇcasnˇe zajist´ı na u ´rovni standardn´ıch prostˇredk˚ u maxim´ aln´ı v´ ytˇeˇznost dat pˇri souˇcasn´em striktn´ım dodrˇzen´ı bezpeˇcnosti a ochrany osobn´ıch u ´daj˚ u. Kromˇe technick´ ych prostˇredk˚ u lze oˇcek´ avat i nutnost regulace vytˇeˇzov´an´ı dat tak, aby bylo zamezeno jak´emukoliv i tˇreba jen teoretick´emu nebo nepˇr´ım´emu zneuˇzit´ı a u ´niku citliv´ ych informac´ı. Ve svˇetˇe existuj´ı legislativn´ı u ´pravy jako napˇr´ıklad Health Insurance Portability and Ac” countability Act“ (HIPAA), kter´ a v USA definuje standardy transakc´ı se zdravotn´ımi z´ aznamy. V r´ amci EU tuto oblast sice ˇreˇs´ı smˇernice EU Data Protection Directive 95/46/EC, ale ta definuje jen poˇzadavek souhlasu pacienta se zpracov´an´ım jeho u ´daj˚ u. Obecn´ y nebo jednotn´ y pˇr´ıstup k ochranˇe citliv´ ych u ´daj˚ u vˇsak EU st´ ale nem´a[2].
cov´an´ı hromadn´ ych dat napˇr´ıklad u platformy Hadoop d´ıky Hadoop Distributed File System (HDFS).
Z´ akladn´ı ochrana
Plnˇe homomorfn´ı ˇsifrov´an´ı – FHE (Fully homomorphic encryption)[5][6][7] je zaloˇzeno na principu, kdy datab´aze obsahuje kompletnˇe zaˇsifrovan´a data, ke kter´ ym se nem˚ uˇze majitel nebo provozovatel platformy dostat. Uˇzivatel´e se mohou k datab´azi pˇripojit a zad´avat j´ı dotazy, u ´koly, vyhled´avat v n´ı nebo pˇr´ıpadnˇe zad´avat v´ ypoˇcty. Datab´aze takov´e zad´an´ı pˇrijme a u ´kol provede. Jako v´ ysledek vr´at´ı data, kter´a jsou uˇzivateli srozumiteln´a, ale samotn´a datab´aze, a tedy i vlastn´ık nebo provozovatel platformy, nem´a moˇznost takov´emu v´ ypoˇctu, pˇr´ıpadnˇe uloˇzen´ ym dat˚ um porozumˇet nebo k nim pˇristupovat. Jde o jeden z pokroˇcil´ ych princip˚ u ˇsifrov´an´ı, kter´ y je zat´ım ve v´ yvoji. V souˇcasnosti existuje nˇekolik pilotn´ıch a v´ yzkumn´ ych projekt˚ u, kter´e se snaˇz´ı plnˇe homomorfn´ı ˇsifrov´an´ı implementovat, ale zat´ım jeˇstˇe nen´ı moˇzno jej nasadit do praktick´eho provozu. Z pohledu proces˚ u a poˇzadavk˚ u na bezpeˇcnost pˇri pr´aci s biomedic´ınsk´ ymi daty jde o jednu
Dvouvrstevn´ e ˇsifrov´ an´ı Dvouvrstevn´a architektura ˇsifrov´an´ı je zamˇeˇrena oproti v´ yˇse uveden´emu z´akladn´ımu ˇsifrov´an´ı na ochranu proti vlastn´ıkovi platformy. Data jsou ˇsifrovan´a tzv. endto-end. Z toho plyne, ˇze i kdyby vlastn´ık nebo provozovatel platformy chtˇel k dat˚ um pˇristupovat, nem´a k tomu prostˇredky, jelikoˇz data jsou ˇsifrovan´a kl´ıˇcem, kter´ ym nedisponuje. To lze povaˇzovat za v´ yhodu, ale oproti tomu nev´ yhodou je nemoˇznost vyhled´avat v datech. Principi´alnˇe je tento zp˚ usob nastaven tak, ˇze existuj´ı dva kl´ıˇce, z nichˇz jeden drˇz´ı Key Management Server (KMS), obsluhovan´ y majitelem nebo provozovatelem platformy, druh´ y drˇz´ı uˇzivatel. Jeden bez druh´eho tedy nem˚ uˇze k dat˚ um pˇristupovat.
V´ıcevrstevn´ e ˇsifrov´ an´ı
V´ıcevrstevn´e ˇsifrov´an´ı – MPC (Multi party computation)[3][4] je podobn´e principu d˚ uvˇeryhodn´eho subjektu, kter´emu jednotliv´ı uˇzivatel´e d˚ uvˇeˇruj´ı a pˇred´avaj´ı svoje vstupy. Tento subjekt poˇc´ıt´a v´ ystupy s vyuˇzit´ım definovan´eho algoritmu. Takov´ y zp˚ usob ˇsifrov´an´ı se ale plnˇe obejde bez d˚ uvˇeryhodn´eho subjektu. Jednotliv´e servery si vymˇen ˇuj´ı data zaˇsifrovan´a v´ıcevrstevn´ ym ˇsifrov´an´ım, avˇsak kaˇzd´ y z nich m´a pˇr´ıstup jen ke sv´ ym vstup˚ um a v´ ystup˚ um. Servery spoleˇcnˇe pouze d´ıky pos´ıl´an´ı zaˇsifrovan´ ych zpr´av spoˇc´ıtaj´ı v´ ystup m´ısto d˚ uvˇeryhodn´eho subjektu. Tento zp˚ usob je moˇzno vyuˇz´ıt v pˇr´ıpadˇe, ˇze m´ame k dispozici v´ıce server˚ u, ale hroz´ı riVyuˇ zit´ı ˇsifrov´ an´ı v biomedic´ınˇ e ziko, ˇze nˇekter´e z nich budou kompromitov´any. Algoritmus je moˇzno konfigurovat pomoc´ı parametr˚ u t a n tak, aby a zdravotnictv´ı kompromitace maxim´aln´ıho poˇctu t server˚ u z celkov´eho poˇ c tu n server˚ u nezp˚ u sobila ˇ z a ´ dn´ y u ´ nik dat. Vlastn´ı ˇsifrov´ an´ı r˚ uzn´ ymi metodami pˇrin´aˇs´ı sv´e v´ yhody i nev´ yhody a zat´ım neexistuje jednotn´a metoda, kter´a by byla vhodn´ a pro vˇsechny aplikace. Plnˇ e homomorfn´ı ˇsifrov´ an´ı
Z´akladn´ı ochranu lze popsat jako mnoˇzinu u ´rovn´ı pˇr´ıstupov´ ych pr´av, kter´ a jsou nastavena nad daty tak, aby kaˇzd´ y uˇzivatel mˇel pˇr´ıstup k poˇzadovan´e podmnoˇzinˇe dat, kterou m˚ uˇze plnˇe pouˇz´ıvat – prohled´ avat, analyzovat apod. Tento princip nejbl´ıˇze odpov´ıd´ a nastaven´ı pr´av napˇr´ıklad v operaˇcn´ım syst´emu UNIX. Superuˇzivatel vid´ı vˇsechna data, standardn´ı uˇzivatel´e vid´ı jen to, co jim dovoluje u ´roveˇ n jejich pˇr´ıstupu. V´ yhodou je moˇznost prov´adˇet jak´ekoliv v´ ypoˇcty a vyhled´ av´ an´ı za pomoci aplikac´ı, kter´e maj´ı vyˇsˇs´ı pr´ava neˇz standardn´ı uˇzivatel a mohou definovat u ´roveˇ n zobecnˇen´ı v´ ysledku do takov´e m´ıry, aby standardn´ı uˇzivatel z´ıskal jen obecn´ a, generalizovan´a, agregovan´a nebo odpov´ıdaj´ıc´ım zp˚ usobem anonymizovan´a data. Nev´ yhodou tohoto pˇr´ıstupu je neexistuj´ıc´ı ochrana proti vlastn´ıkovi platformy. Tento zp˚ usob se vyuˇz´ıv´a pˇri zprac
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z cest, kter´a by se v budoucnosti mohla sv´ ymi vlastnostmi uk´azat jako jedna z vhodn´ ych platforem. Autoˇri ˇcl´ anku se domn´ıvaj´ı, ˇze v mnoha konkr´etn´ıch u ´loh´ ach nad daty pacient˚ u je tento zp˚ usob vyhled´ av´ an´ı preferovan´ ym zp˚ usobem, u nˇejˇz pˇrevaˇzuj´ı klady nad z´ apory.
ˇ asteˇ C´ cnˇ e homomorfn´ı ˇsifrov´ an´ı
zav´adˇet pokroˇcilejˇs´ı algoritmy, z nichˇz nevhodnˇejˇs´ı je patrnˇe v´ıcevrstevn´e ˇsifrov´an´ı, kter´emu lze v bl´ızk´e budoucnosti predikovat zv´ yˇsen´ı pod´ılu. Z teoretick´eho hlediska se ukazuje, ˇze c´ılov´ ym stavem bude postupn´a konvergence k jednomu ze dvou popsan´ ych zp˚ usob˚ u homomorfn´ıho ˇsifrov´an´ı nebo ekvivalentu, kter´ y se z nich postupnˇe vyvine, jelikoˇz z pohledu bezpeˇcnostn´ıch proces˚ u splˇ nuj´ı tyto zp˚ usoby ˇsifrov´an´ı poˇzadavky kladen´e na bezpeˇcnost a ochranu biomedic´ınsk´ ych dat nejl´epe. Jejich v´ yzkum a implementace do f´aze praktick´eho vyuˇzit´ı vˇsak dle pˇredpoklad˚ u bude trvat minim´alnˇe nˇekolik let. Teprve pot´e budou m´ıt ˇsanci zav´est v oblasti ˇsifrov´an´ı biomedic´ınsk´ ych dat nov´ y standard.
ˇ asteˇcnˇe homomorfn´ı ˇsifrov´ C´ an´ı – SHE (Somewhat homomorphic encryption)[3][8][7] je ve sv´em principu zp˚ usob ˇsifrov´an´ı podobn´ y v´ yˇse popsan´emu pˇr´ıstupu FHE. Stejnˇe jako v pˇr´ıpadˇe FHE, prov´ ad´ı datab´ aze v´ ypoˇcty nad daty, kter´ ym nijak nerozum´ı. Hlavn´ı odliˇsnost´ı od FHE je nemoˇznost v datab´ azi, kter´ a pouˇz´ıv´ a ˇc´ asteˇcnˇe homomorfn´ı ˇsifrov´an´ı, vyhled´ avat. Typickou u ´lohou pro Podˇ ekov´ an´ı SHE jsou r˚ uzn´e statistick´e anal´ yzy a v´ ystupy, a proto je ide´aln´ım n´astrojem, kter´ y chr´ an´ı konkr´etn´ı data jak Pr´ace byla ˇc´asteˇcnˇe podpoˇrena projektem SVV-2016pˇred vlastn´ıkem nebo provozovatelem platformy, tak pˇred 260267 Univerzity Karlovy. koncov´ ym uˇzivatelem. Aˇc se tento pˇr´ıstup m˚ uˇze zd´at m´enˇe hodnotn´ y neˇz FHE, existuje ˇrada u ´loh, pro kter´e Reference m˚ uˇze b´ yt tento zp˚ usob ˇsifrov´ an´ı v´ yhodn´ y nebo dokonce ˇz´adouc´ı z pohledu zad´ an´ı a z pohledu procesn´ıho nasta[1] Berger J, Beyr K. Safety of Private Data in Big Data and ven´ı bezpeˇcnostn´ıch pravidel. Autoˇri ˇcl´ anku se domn´ıvaj´ı, Biomedicine. IJBH 2015; 3(1):2-5. ˇze pro potˇreby statistick´ ych v´ ystup˚ u patˇr´ı tento zp˚ usob [2] Boussi Rahmouni H, Solomonides T, Casassa Mont M, Shiu S. ˇsifrov´an´ı k nejbezpeˇcnˇejˇs´ım z pohledu ochrany uloˇzen´ ych Modelling and Enforcing Privacy for Medical Data Disclosure dat. across Europe. In Adlassnig KP, editor. Medical Informatics in
ˇ Sifrov´ an´ı ˇr´ızen´ e pacientem Z pohledu pacientsk´ ych dat (nikoliv vˇsak obecnˇe biomedic´ınsk´ ych) se v posledn´ı dobˇe jako vhodn´ y zp˚ usob zabezpeˇcen´ı a ˇsifrov´an´ı prosazuje ˇsifrov´ an´ı ˇr´ızen´e pacientem – PCE (Patient controlled encryption)[9]. Z´ akladn´ı premisou je na rozd´ıl od pˇredchoz´ıch pˇr´ıstup˚ u kontrola nad ˇsifrov´an´ım dat na stranˇe pacienta. Data maj´ı hierarchickou strukturu a pacient urˇcuje, kter´e uzly t´eto struktury mohou ˇc´ıst kteˇr´ı l´ekaˇri. Z pohledu bˇeˇzn´ ych ˇzivotn´ıch situac´ı se tento pˇr´ıstup m˚ uˇze zd´ at efektivn´ı a nav´ıc i etick´ y, jelikoˇz s´am pacient si rozhoduje o tom, kter´ a data poskytuje. Na druhou stranu vˇsak v mnoha pˇr´ıpadech neznalost na prvn´ı pohled nesouvisej´ıc´ıch informac´ı (napˇr. o u ´razu, operaci atd.) m˚ uˇze v´est l´ekaˇre ke stanoven´ı odliˇsn´e diagn´ozy, neˇz by urˇcil v pˇr´ıpadˇe, ˇze by rozhodoval v pln´em kontextu. Dalˇs´ım z problematick´ ych bod˚ u je urgentn´ı p´eˇce o pacienta v okamˇziku, kdy nen´ı schopen z d˚ uvodu sv´eho akutn´ıho stavu rozhodovat o poskytnut´ı dostateˇcn´eho pˇr´ıstupu pro l´ekaˇre.
a United and Healthy Europe - Proceedings of. Sarajevo: IOS Press; 2009. p. 695-699. [3] Damgaard I, Pastro V, Smart N, Zakarias S. Multiparty Computation from Somewhat Homomorphic Encryption. [Internet] Dostupn´ e 27. 7. 2016 na: https://eprint.iacr.org/2011/ 535.pdfhttps://eprint.iacr.org/2011/535.pdf [4] Pinkas YL. Secure Multiparty Computation for PrivacyPreserving Data Mining. The Journal of Privacy and Confidentiality. 2009. p. 59-98. [5] Lauter K, Naehrig M, Vaikuntanathan V. Can Homomorphic Encryption be Practical? [Internet] Dostupn´ e 27. 7. 2016 na: https://www.microsoft.com/en-us/research/ publication/can-homomorphic-encryption-be-practical/ [6] Chatterjee A, Sengupta I. Searching and Sorting of Fully Homomorphic Encrypted Data on Cloud. [Internet] Dostupn´ e 27. 7. 2016 na: https://eprint.iacr.org/2015/981. pdfhttps://eprint.iacr.org/2015/981.pdf [7] Wu D, Boneh D. Practical Somewhat Homomorphic Encryption. [Internet] Dostupn´ e 27. 7. 2016 na: https://crypto. stanford.edu/~dwu4/talks/SecurityLunch0214.pdf
Z´ avˇ ereˇ cn´ e zhodnocen´ı
[8] Bos JW, Lauter K, Michael Naehrig M. Private Predictive Analysis on Encrypted Medical Data. [Internet] Dostupn´ e 27. 7. 2016 na: https://eprint.iacr.org/2014/336. pdfhttps://eprint.iacr.org/2014/336.pdf
V souˇcasnosti je patrnˇe nejpouˇz´ıvanˇejˇs´ı z´ akladn´ı ochrana, kter´a je z pohledu pouˇzit´ı velmi praktick´a, a to i pˇresto, ˇze tento zp˚ usob znamen´ a mnoˇzstv´ı bezpeˇcnostn´ıch rizik. S rostouc´ım pod´ılem digitalizace biomedic´ınsk´ ych informac´ı lze pˇredpokl´ adat, ˇze bude nutn´e
[9] Benaloh J, Chase M, Horvitz E, Lauter K. Patient Controlled Encryption: Ensuring Privacy of Electronic Medical Records. [Internet] Dostupn´ e 27. 7. 2016 na: http://research.microsoft.com/en-us/um/people/ horvitz/ccsw_2009_benaloh_chase_horvitz_lauter.pdf
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Poruchy gen˚ u kolagenu typ I u ˇ cesk´ ych pacient˚ u s osteogenesis imperfecta Lucie Hruˇskov´ a1 , Ivan Mazura1 1
Klinika dˇetsk´eho a dorostov´eho l´ekaˇrstv´ı 1. l´ekaˇrsk´e fakulty Univerzity Karlovy a Vˇseobecn´e fakultn´ı nemocnice, ˇ a republika. Praha, Cesk´
Abstrakt Kolagen typ I, hlavn´ı sloˇzka pojivov´e tk´anˇe, je heterotrimer k´odovan´y geny COL1A1 a COL1A2. Porucha tvorby tohoto proteinu m´a za n´asledek onemocnˇen´ı zvan´e osteogenesis imperfecta (OI). C´ılem studie byla identifikace mutac´ı gen˚ u COL1A1 a COL1A2 u ˇcesk´ych pacient˚ u s diagn´ozou OI. Molekul´arnˇe genetick´e anal´yzy odhalily devˇet mutac´ı, z nichˇz ˇsest nebylo dˇr´ıve pops´ano zahraniˇcn´ı literaturou.
Jedna z tˇechto mutac´ı vede v substituci glycinu, tˇri jsou um´ıstˇeny v tzv. major ligand binding“ (MLBR) oblastech, ” ˇctyˇri maj´ı za n´asledek pˇredˇcasn´e ukonˇcen´ı transkripce. Dalˇs´ı z identifikovan´ych zmˇen vede v posun tzv. ˇctec´ıho r´amce a posledn´ı z nalezen´ych zmˇen je tichou mutac´ı glycinu.
Kl´ıˇ cov´ a slova Kolagen typ I, osteogenesis imperfecta, COL1A1, COL1A2, mutace
Kontakt: Lucie Hruˇskov´ a 1. l´ ekaˇrsk´ a fakulta Univerzity Karlovy Adresa: Kateˇrinsk´ a 32, 128 08 Praha 2 E–mail: [email protected]
IJBH 2016; 4(2):7–9 zasl´ ano: 15. ˇ cervence 2016 pˇrijato: 15. srpna 2016 publikov´ ano: 20. z´ aˇr´ı 2016
´ Uvod
Materi´ al a Metody
Molekula kolagenu typ I, heterotrimeru k´odovan´eho geny COL1A1 a COL1A2, m´ a charakter trojˇsroubovice sloˇzen´e ze dvou alpha 1 ˇretˇezc˚ u a jednoho alpha 2 ˇretˇezce. Poruchy tohoto proteinu jsou spojov´ any s fenotypem osteogenesis imperfecta typu I-IV, Ehlers-Danlos syndromu (typu klasick´eho typu, kardio-vulv´ arn´ıho a typ˚ u VIIA a VIIB) a Caffey syndrome [1].
Do studie bylo zahrnuto 34 pacient˚ u (11 muˇz˚ u a 23 ˇzen) ve vˇeku 7 aˇz 57 let. 19 pacient˚ u bylo postiˇzeno OI typem IA, v ˇsesti pˇr´ıpadech byl diagnostikov´an tˇret´ı typ OI, u 4 jedinc˚ u byla klasifikov´ana OI typu IVA a 5 pacient˚ u vykazovalo klinicko-radiologick´e znaky typu IVB. Tato studie byla pˇripravena v souladu s Deklarac´ı Helsinky a schv´alena etickou komis´ı Vˇseobecn´e fakultn´ı nemocnice v Praze (projekt 83/14). Analyzovan´ı pacienti podepsali informovan´ y souhlas se zaˇrazen´ım do t´eto studie.
Osteogenesis imperfecta (OI) je onemocnˇen´ı pojivov´e tk´anˇe s v´ yskytem 1 : 15-20000 ˇzivˇe narozen´ ych. Klinick´e znaky pacient˚ u zahrnuj´ı kˇrehk´e kosti, zv´ yˇsen´e riziko zlomenin, modr´e skl´ery, nedosl´ ychavost a poruchu dentice (dentinogenesis imperfecta (DI)). V souˇcasnosti je na z´akladˇe klinick´ ych a genetick´ ych znak˚ u rozliˇsov´ano ˇctrn´act forem OI. Prvn´ı ˇctyˇri typy (OI I-IV) maj´ı sv˚ uj p˚ uvod v dominantn´ıch mutac´ıch gen˚ u COL1A1 a COL1A2. Zb´ yvaj´ıc´ı typy (OI V-XIV) vznikaj´ı n´asledkem mutac´ı recesivn´ıch gen˚ u IFITM5, SERPINF1, CRTAP, P3H1, PPIB, SERPINH1, FKBP10, SP7, BMP1 nebo TMEMB38B [2, 3, 4].
Genetick´e anal´ yzy byly zamˇeˇreny na k´oduj´ıc´ı (a pˇrilehl´e nek´oduj´ıc´ı) oblasti gen˚ u COL1A1 a COL1A2, zahrnuj´ıc´ıch 51 (COL1A1), resp. 52 (COL1A2) exon˚ u. Genomick´a DNA byla izolov´ana z leukocyt˚ u perifern´ı krve. Anal´ yza genu COL1A1 byla uskuteˇcnˇena ve spolupr´aci s Centre of Medical Genetics, Antwerp University and University Hospital, Antwerpy, Belgie. Prvn´ım krokem byla tzv. high resolution melting“ (HRM) me” toda, jej´ımˇz c´ılem je detekce vzork˚ u s pravdˇepodobn´ ym v´ yskytem zmˇeny DNA. Vybran´e vzorky byly amplifikov´any prostˇrednictv´ım polymer´azov´e ˇretˇezov´e reakce (PCR) a n´aslednˇe sekvenov´any metodou dle Sangera. Anal´ yza genu COL1A2 byla provedena ve spolupr´aci s Klinikou dˇetsk´eho a dorostov´eho l´ekaˇrstv´ı 1. l´ekaˇrsk´e
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fakulty Univerzity Karlovy a Vˇseobecn´e fakultn´ı nemocˇ a republika a zahrnovala PCR kompletn´ı nice, Praha, Cesk´ datab´aze DNA ve vˇsech k´ oduj´ıc´ıch oblastech s n´ asledn´ ym sekvenov´an´ım dle Sangera. Z´ıskan´a data byla porovn´ ana s referenˇcn´ı sekvenc´ı uvedenou v datab´azi Ensembl, ref.ˇc.. ENST00000225964 (COL1A1 gen) a ref.ˇc. ENST00000297268 (COL1A2 gen). Nov´e mutace byly identifikov´ any na z´ akladˇe absence tˇechto zmˇen v datab´az´ıch Osteogenesis Imperfecta Variant Database, Human Genome Mutation Database a Ensembl.
V´ ysledky Molekul´arnˇe genetick´e anal´ yzy odhalily 8 mutac´ı genu COL1A1, z nichˇz ˇctyˇri vedly v tvorbu stopkodon˚ u (c.141C>A p.Tyr47X, c.391C>T p.Arg131X, c.1243C>T p.Arg415X, c.4021C>T p.Gln1341X), a tud´ıˇz k pˇredˇcasn´emu ukonˇcen´ı transkripce genu. Dvˇe identifikovan´e zmˇeny vedly v substituci aminokyseliny (c.182G>T p.Cys61Phe, c.182G>T p.Pro1186Ala) a jedna z nalezen´ ych mutac´ı se nach´ azela v pˇrilehl´e intronov´e oblasti genu (c.1057-1G>T). V posledn´ım pˇr´ıpadˇe se jedn´ a o tzv. tichou mutaci glycinu na pozici 794 [5]. Anal´ yzou genu COL1A2 byla identifikov´ ana substituce glycinu c.2440G>T p.Gly814Trp [6].
Diskuze
v oblasti N-propeptidu, kter´ y je d˚ uleˇzit´ y pro bezchybn´e skl´ad´an´ı alpha ˇretˇezc˚ u do trojˇsroubovice. [5]. Posledn´ı identifikovanou zmˇenou byla tzv. tich´a mutace glycinu na pozici 794 genu COL1A1. Protoˇze tato zmˇena nevede v posun ˇctec´ıho r´amce alpha 1 ˇretˇezce, jej´ı vliv na tvorbu RNA ˇci dalˇs´ı posttranslaˇcn´ı modifikace nen´ı doposud jasn´ y [5]. Zahraniˇcn´ı literaturou nebylo doposud pops´ano celkem ˇsest z dev´ıti identifikovan´ ych mutac´ı (p.Tyr47X, p.Arg415X, p.Gly794Gly, p.Gly814Trp, p.Gln1341X, c.1057-1G>T). Substituce p.Cys61Phe a mutace p.Arg131X byly jiˇz dˇr´ıve pops´any v pˇr´ıpadech ˇc´ınsk´eho a italsk´eho pacienta [8, 9]. Tak´e zmˇena p.Pro1186Ala byla pops´ana zahraniˇcn´ı literaturou, nicm´enˇe data o t´eto substituci nejsou k dispozici.
Z´ avˇ er Prostˇrednictv´ım molekul´arnˇe genetick´ ych anal´ yz byly identifikov´any kandid´atn´ı mutace pacient˚ u s diagn´ozou jednoho z prvn´ıch ˇctyˇr typ˚ u OI. Za u ´ˇcelem odhalen´ı genetick´e podstaty OI u ostatn´ıch pacient˚ u s neporuˇsenou synt´ezou kolagenu typ I je vhodn´e uvaˇzovat o anal´ yz´ach recesivn´ıch gen˚ u spojovan´ ych s fenotypy OI V-XIV.
Prohl´ aˇsen´ı Bˇehem t´eto studie nedoˇslo k ˇz´adn´emu stˇretu z´ajm˚ u.
ekov´ an´ı Pro pacienty s prvn´ı formou OI je typick´ a sn´ıˇzen´a Podˇ produkce (tzv. haploinsuficience, tzn. nedostateˇcnost) koTato studie byla podpoˇrena granty SVV-2016-260267, lagenu typ I. Tato porucha synt´ezy proteinu je zp˚ usobena PRVOUK P24/1LF/3 a UNCE 204011 Univerzity Karmutacemi vedouc´ımi v pˇredˇcasn´e ukonˇcen´ı transkripce [2]. lovy. Mutace vedouc´ı v tvorbu stopkodon˚ u byly v r´ amci t´eto studie identifikov´any pouze u pacient˚ u postiˇzen´ ych pr´avˇe prvn´ım typem OI. Reference V r´amci struktury molekuly kolagenu typ I byly [1] Marini JC, Rajpar MH. Osteogenesis imperfecta. In: Thakvyˇclenˇeny tˇri tzv. major ligand binding“ oblasti se ” ker RV, White MP, Eisman JA, Igarashi T, editors. Genetics zv´ yˇsenou koncentrac´ı vazebn´ ych m´ıst s dalˇs´ımi molekuof Bone Biology and Skeletal Disease. New York: Academic lami extracelul´arn´ı matrix (COMP, integriny, fibronectin Press 2013; 257-273. a dalˇs´ı). Prostˇrednictv´ım tˇechto vazeb je doc´ıleno vyˇsˇs´ı [2] Forlino A, Cabral WA, Barnes AV, Marini JC. 2011. New pevnosti a elasticity kost´ı [7]. Tˇri z identifikovan´ ych muperspectives on osteogenesis imperfecta. Nat Rev Endocrinol tac´ı jsou um´ıstˇeny v MLBR regionech. Konkr´etnˇe se 2011;7:540–557 jedn´a o zmˇeny c.1057-1G>T - MLBR1 COL1A1 genu; [3] Endotext [homepage on the Internet]. MDText.com, p.Gly814Trp – MLBR2 COL1A2 genu; a p.Pro1186Ala – Inc. Available from: http://www.endotext.org/chapter/ MLBR3 COL1A1 genu. Uveden´e zmˇeny DNA byly identiosteogenesis-imperfecta/7/. Accessed April 15, 2015. fikov´any u pacient˚ u postiˇzen´ ych prvn´ım (p.Pro1186Ala), Accessed June 16, 2014. tˇret´ım (p.Gly814Trp) a ˇctvrt´ ym (c.1057-1G>T) typem OI [4] Van Dijk FS, Sillence DO. Osteogenesis imperfecta: clinical [5, 6, 7]. diagnosis, nomenclature and severity assessment. Am J Med Mutace vedouc´ı v substituci glycinu - p.Gly814Trp Genet Part A 2014;164A:1470-1481. (COL1A2), byla identifikov´ ana u pacienta s tˇret´ım typem [5] Hruskova L, Fijalkowski I, Van Hul W, Marik I, Mortier G, OI. Glycin je kl´ıˇcovou aminokyselinou alpha ˇretˇezc˚ u a vyMartasek P, Mazura I. Eight mutations including 5 novel ones skytuje se v 338 repetitivn´ıch motivech Gly-X-Y. Jeho in the COL1A1 gene in Czech patients with osteogenesis imperfecta. Biomed Pap Med Fac Univ Palacky Olomouc Czech v´ yznam spoˇc´ıv´a v zajiˇstˇen´ı formace helixu bˇehem synt´ezy Repub 2016 [Ahead of Print]. Available from: http://biomed. kolagenu. [6]. papers.upol.cz/corproof.php?tartkey=bio-000000-1119& Substituce p.Cys61Phe byla nalezena v pˇr´ıpadˇe paback=%2Fsearch.php%3Fquery%3DHruskova%2Bin%253Aauth% cienta s diagn´ozou OI typ III. Tato mutace se nal´ez´a 2Bname%2Bkey%2Babstr%26sfrom%3D0%26spage%3D30 IJBH – Volume 4 (2016), Issue 2
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[6] Hruˇskov´ a L, Maˇr´ık I, Mazurov´ a S, Mart´ asek P, Mazura I. COL1A2 gene analysis in a Czech osteogenesis imperfecta patient: a candidate novel mutation in a patient affected by osteogenesis imperfecta type 3. Advances in Genomics and Genetics 2015;5:275-281.
[8] Zhang ZL, Zhang H, Ke Y, Yue H, Xiao WJ, Yu JB, Gu JM, Hu WW, Wang Ch, He JW, Fu WZ. The identification of novel mutations in COL1A1, COL1A2, and LEPRE1 genes in Chinese patients with osteogenesis imperfecta. J Bone Miner Metab 2012;30:69-77.
[7] Sweeney, SM, Orgel JP, Fertala A, McAuliffe JD, Turner KR, Di Lullo GA, Chen S, Antipova O, Perumal S, Ala-Kokko L, Forlino A, Cabral WA, Barnes AM, Marini JC, San Antonio JD. Candidate cell and matrix interaction domains on the collagen fibril, the predominant protein of vertebrates. J Biol Chem 2008;283:21187-21197.
[9] University of Leicester. Osteogenesis Imperfecta Variant
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Database (2008). https://oi.gene.le.ac.uk/variants.php? select_db=COL1A1&action=view&view=0001207%2C0000492% 2C21&order=Variant%2FProtein%2CASC. Accessed 5 March 2016.
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Stanoven´ı pohlav´ı plodu pomoc´ı testov´ an´ı voln´ e DNA Martin Hynek1,2 , Jana Zv´ arov´ a2,3 , Filip Zembol1 , Ivona Mareˇsov´ a1 , David Stejskal1 1 2 3
Gennet, Centrum pro fet´ aln´ı medic´ınu a reprodukˇcn´ı genetiku, Praha
´ Ustav hygieny a epidemiologie, 1. l´ekaˇrsk´ a fakulta, Univerzita Karlova, Praha
´ ˇ Praha EuroMISE, Evropsk´e centrum pro medic´ınskou informatiku, statistiku a epidemiologii, Ustav informatiky Akademie vˇed CR,
Abstrakt C´ıl: C´ılem t´eto studie bylo posoudit stanoven´ı pohlav´ı plodu pomoc´ı neinvazivn´ıho testov´an´ı voln´e DNA (cfDNA). Pro tento c´ıl jsme pouˇzili nov´y postup, kdy jsme hodnotili poˇcet obsazen´ych bin˚ u na chromozomu Y a srovn´avali jsme ho s tradiˇcn´ım postupem, kter´y hodnot´ı pod´ıl unik´atn´ıch read˚ u poch´azej´ıc´ıch z chromozomu Y. Metodika: Soubor pro studii vzeˇsel z rozs´ahlejˇs´ı prospektivn´ı cfDNA studie, ve kter´e n´aˇs vlastn´ı cfDNA test byl integrov´an kontingenˇcn´ı formou do rutinn´ıho prenat´aln´ıho screeningu v Centru fet´aln´ı medic´ıny a reprodukˇcn´ı genetiky, Gennet, Praha. CfDNA test byl prov´adˇen u tˇehotn´ych, kter´e mˇely riziko pro z´akladn´ı trizomie na z´akladˇe prvotrimestr´aln´ıho kombinovan´eho testu mezi 1/100–1/500. V kaˇzd´em vzorku byla cfDNA izolov´ana z mateˇrsk´e plazmy a bylo provedeno celogenomov´e sekvenov´an´ı pomoc´ı sekven´atoru Ion Proton System. Pro stanoven´ı pohlav´ı plodu byly pouˇzity dva n´astroje: poˇcet obsazen´ych bin˚ u na chromozomu Y a procento unik´atn´ıch read˚ u poch´azej´ıc´ıch z chromozomu Y. V´ ysledky: Celkem jsme od ledna 2015 do kvˇetna 2016 zpracovali 1 669 prenat´aln´ıch vzork˚ u. Z toho bylo pro navrˇzenou anal´yzu dostupn´ych 1 588 vzork˚ u.
V pˇr´ıpadˇe poˇctu obsazen´ych bin˚ u na Y chromozomu byl rozd´ıl mezi plody muˇzsk´eho a ˇzensk´eho pohlav´ı v´yraznˇe vˇetˇs´ı a umoˇznil jednoznaˇcn´e stanoven´ı pohlav´ı plodu ve srovn´an´ı s hodnocen´ım pomoc´ı procenta unik´atn´ıch read˚ u z Y chromozomu, kde se i v jednom pˇr´ıpadˇe rozdˇelen´ı obou pohlav´ı pˇrekr´yvala. Kromˇe toho, hodnocen´ı poˇctu obsazen´ych bin˚ u umoˇznilo zachycen´ı tˇr´ı atypick´ych patologick´ych pˇr´ıpad˚ u. Naproti tomu, v pˇr´ıpadˇe procenta read˚ u z Y chromozomu se hodnoty tˇechto tˇr´ı patologick´ych pˇr´ıpad˚ u pohybovaly v rozmez´ı hodnot, kter´e jsme pozorovali u norm´aln´ıch ˇzensk´ych plod˚ u. Z´ avˇ er: Navrhovan´e stanoven´ı pohlav´ı pomoc´ı hodnocen´ı poˇctu obsazen´ych bin˚ u na Y chromozomu m´a vynikaj´ıc´ı senzitivitu i specificitu dosahuj´ıc´ı 100 % a je pouˇziteln´e od 10. t´ydne tˇehotenstv´ı. Kromˇe toho, v´yhodou tohoto n´astroje je, ˇze umoˇzn ˇuje zachytit atypick´e pˇr´ıpady, kter´e maj´ı vysok´e riziko pˇridruˇzen´e patologie.
Kl´ıˇ cov´ a slova Testov´an´ı voln´e DNA; neinvazivn´ı prenat´aln´ı testov´an´ı; pohlav´ı plodu; prenat´aln´ı diagnostika
Kontakt: Martin Hynek Gennet, Centrum pro fet´ aln´ı medic´ınu a reprodukˇ cn´ı genetiku Adresa: Kosteln´ı 9, 170 00 Praha 7 E–mail: [email protected]
´ Uvod Testov´an´ı voln´e DNA (cell-free DNA, cfDNA) je zaloˇzeno na pˇr´ıtomnosti fragment˚ u voln´e DNA v mateˇrsk´e plazmˇe. Tyto fragmenty poprv´e popsal v roce 1997 Lo a kol. [1] a tento objev otevˇrel zcela nov´e moˇznosti pro nein´ vazivn´ı prenat´aln´ı diagnostiku. Ulomky DNA poch´ azej´ıc´ı od plodu tvoˇr´ı ale pouze malou ˇc´ ast vˇsech u ´lomk˚ u DNA, kter´e jsou pˇr´ıtomn´e mateˇrsk´e plazmˇe [2]. N´ asledn´ y rozvoj metod masivn´ı paraleln´ı sekvenace (MPS), kter´e dok´aˇz´ı pˇreˇc´ıst a kvantifikovat mili´ ony tˇechto DNA fragment˚ u (read˚ u) v mateˇrsk´e plazmˇe, vˇcetnˇe mal´eho pod´ılu poch´azej´ıc´ıho od plodu, umoˇznil zachytit mal´ y vzestup v zastoupen´ı pˇr´ısluˇsn´eho chromozomu, kter´ y zp˚ usobuje IJBH – Volume 4 (2016), Issue 2
IJBH 2016; 4(2):10–14 zasl´ ano: 15. ˇ cervence 2016 pˇrijato: 15. srpna 2016 publikov´ ano: 20. z´ aˇr´ı 2016
plod s aneuploidi´ı [3]. Prvn´ı komerˇcnˇe dostupn´ y cfDNA test pomoc´ı MPS byl uveden v roce 2011 [4] a v souˇcasn´e dobˇe je na trhu cel´a ˇrada r˚ uzn´ ych cfDNA test˚ u. V souˇcasn´e dobˇe pouˇz´ıv´a vˇetˇsina cfDNA test˚ u strategie celogenomov´eho sekvenov´an´ı, c´ılen´eho sekvenov´an´ı (kdy se sekvenuj´ı pouze fragmenty poch´azej´ıc´ı z chromozom˚ u 13, 18 a 21) anebo strategie zaloˇzen´e na SNParray´ıch [5, 6]. Ve srovn´an´ı s invazivn´ımi technikami jako jsou odbˇer choriov´ ych klk˚ u nebo amniocent´eza, kter´e jsou zat´ıˇzeny sice mal´ ym, ale nezanedbateln´ ym rizikem potratu [7], je hlavn´ı v´ yhodou cfDNA testov´an´ı jeho neinvazivita. Posledn´ı metaanal´ yza na z´akladˇe 117 studi´ı uk´azala, ˇze u jednoˇcetn´ ych gravidit dosahuje cfDNA testov´an´ı senc
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zitivity a specificity 0,994 a 0,999, pro trizomii 18 0,977 a 0,999 a pro trizomii 13 0,906 a 1,00 [8]. Kromˇe jin´eho lze cfDNA testy vyuˇz´ıt nejenom pro detekci aneuploidi´ı, ale tak´e pro stanoven´ı pohlav´ı u plodu. ˇ Casn´ e prenat´aln´ı urˇcen´ı pohlav´ı je ˇz´ adouc´ı u rodin s rizikem pˇrenosu X-v´ azan´e choroby. Zlat´ ym standardem diagnostiky pohlav´ı u plodu je invazivn´ı vyˇsetˇren´ı, avˇsak za cenu urˇcit´eho rizika potratu, kter´e invaze pˇredstavuje. Z neinvazivn´ıch moˇznost´ı je schopen pohlav´ı s vysokou pˇresnost´ı stanovit ultrazvuk. V 11. t´ ydnu je stanoven´ı pohlav´ı sice chybn´e aˇz v 40–50 % pˇr´ıpad˚ u [9], ale v 13. t´ ydnu je jiˇz spolehlivost bl´ızk´ a 100 % [10]. CfDNA testov´an´ı pˇredstavuje dalˇs´ı neinvazivn´ı moˇznost, kter´a pohlav´ı plodu zjiˇst’uje s vysokou pˇresnost´ı. Colmant a kol. [10] popisuj´ı pro cfDNA senzitivitu a specificitu t´emˇeˇr 100 % poˇc´ınaje 8. t´ ydnem tˇehotenstv´ı. Metaanal´ yzy proveden´e v letech 2012 a 2016 dospˇely k senzitivitˇe a specificitˇe 0,966 a 0,989 (na z´ akladˇe 10 587 vyˇsetˇren´ı) [11] a 0,989 a 0,996 (na z´akladˇe 11 179 vyˇsetˇren´ı) [8]. Mackie a kol. [8] uv´adˇej´ı v z´avˇeru druh´e zm´ınˇen´e metaanal´ yzy, ˇze v pˇr´ıpadˇe stanoven´ı pohlav´ı m˚ uˇze b´ yt cfDNA testov´ an´ı povaˇzov´ano za diagnostick´ y test, zat´ımco pro stanoven´ı trizomi´ı 21, 18 a 13 je nutn´e cfDNA testy povaˇzovat za screeningov´e z d˚ uvodu niˇzˇs´ı senzitivity, specificity a prevalence tˇechto nemoc´ı v kombinaci s biologick´ ymi omezen´ımi, jakou je napˇr. placent´arn´ı mozaicismus. V roce 1997 Lo a kol. [1] publikovali, ˇze u tˇehotenstv´ı s plodem muˇzsk´eho pohlav´ı obsahuje mateˇrsk´a plazma ready poch´azej´ıc´ı z fet´ aln´ıho chromozomu Y. Nicm´enˇe, protoˇze i u tˇehotenstv´ı s ˇzensk´ ym plodem je urˇcit´e mnoˇzstv´ı sekvenc´ı, kter´e poch´ azej´ı z mateˇrsk´e DNA, chybnˇe namapov´ ano na Y chromozom [12], prost´a pˇr´ıtomnost sekvenc´ı Y chromozomu nen´ı dostateˇcn´a pro diagnostiku plodu muˇzsk´eho pohlav´ı. Obvykl´ ym postupem je proto posoudit pod´ıl read˚ u, kter´e jsou mapov´any na Y chromozom a stanovit mez oddˇeluj´ıc´ı muˇzsk´e a ˇzensk´e plody [13] nebo vyj´ adˇrit poˇcet Y read˚ u statisticky pomoc´ı Z-sk´ore [14]. Tyto postupy maj´ı v´ yhodu, ˇze informaci z´ısk´av´ame pˇr´ımo ze sekvenaˇcn´ıch dat bez nutnosti proveden´ı dalˇs´ıho laboratorn´ıho testu. Dalˇs´ı moˇznost´ı je pouˇzit´ı real-time kvantitativn´ı PCR [15] nebo konvenˇcn´ı PCR [16], ale v tˇechto pˇr´ıpadech je nezbytn´e proveden´ı dalˇs´ıho samostatn´eho testu. C´ılem t´eto studie bylo posoudit stanoven´ı pohlav´ı plodu pomoc´ı neinvazivn´ıho cfDNA testu. Pro tento c´ıl jsme pouˇzili nov´ y postup, kdy hodnot´ıme poˇcet obsazen´ ych bin˚ u na chromozomu Y, a srovnali jsme ho s tradiˇcn´ım postupem, kter´ y hodnot´ı pod´ıl unik´atn´ıch read˚ u poch´azej´ıc´ıch z chromozomu Y.
prenat´aln´ıho screeningu [19] v Centru fet´aln´ı medic´ıny a reprodukˇcn´ı genetiky, Gennet, Praha. CfDNA test byl prov´adˇen u tˇehotn´ ych, kter´e mˇely riziko pro z´akladn´ı trizomie na z´akladˇe prvotrimestr´aln´ıho kombinovan´eho testu mezi 1/100–1/500. Kromˇe toho, abychom bl´ıˇze zhodnotili v´ ytˇeˇznost cfDNA testu, zaˇcleˇ novali jsme do vyˇsetˇren´ı i vzorky tˇehotenstv´ı se zn´amou trizomi´ı, u nichˇz byla trizomie potvrzena karyotypizac´ı. P˚ uvodn´ı cfDNA studie se zamˇeˇrovala na detekci trizomie 13, trizomie 18 a trizomie 21 u plodu. Nicm´enˇe, pˇredmˇetem tohoto sdˇelen´ı jsou pouze v´ ysledky t´ ykaj´ıc´ı se stanoven´ı pohlav´ı plodu pomoc´ı cfDNA testov´an´ı. Vˇsechny tˇehotn´e poskytly p´ısemn´ y informovan´ y souhlas s vyˇsetˇren´ım. V obdob´ı od ledna 2015 do kvˇetna 2016 jsme odeb´ırali 10 ml mateˇrsk´e ven´ozn´ı krve (zkumavky Streck cell-free DNA BCTTM ) mezi 10. a 24. t´ ydnem tˇehotenstv´ı. Pohlav´ı plodu bylo potvrzeno pomoc´ı ultrazvukov´eho vyˇsetˇren´ı v druh´em a tˇret´ım trimestru, pomoc´ı invazivn´ıho vyˇsetˇren´ı a karyotypizace plodu nebo po porodu. Pˇr´ıpady, u nichˇz nebyly tyto u ´daje k dispozici, byly ze studie vyˇrazeny. Zpracov´ an´ı vzorku a sekvenace DNA Abychom oddˇelili plazmu od krevn´ıch bunˇek, byly z´ıskan´e vzorky krve nejprve centrifugov´any pˇri 1 600 g po dobu 10 minut pˇri pokojov´e teplotˇe. Oddˇelen´a plazma byla peˇclivˇe pˇrenesena do nov´ ych steriln´ıch 15 ml zkumavek a znovu centrifugov´ana pˇri 3 200 g pˇri pokojov´e teplotˇe. N´asledn´a extrakce plazmatick´e DNA byla prov´adˇena pomoc´ı QIAamp Circulating Nucleic Acid Kitu (Qiagen, Hilden, Nˇemecko) a od u ´nora 2016 pomoc´ı QIASymphony SP instrument, Circulating DNA Kitu (Qiagen, Hilden, Nˇemecko). DNA knihovna byla pˇripravov´ana z extrahovan´e plazmatick´e DNA pomoc´ı Ion Plus Fragment Library Kitu (Life Technologies, USA). Pot´e, co byly z´ıskan´e knihovny ekvimol´arnˇe sm´ıch´any, prov´adˇeli jsme celogenomov´e sekvenov´an´ı z jednoho konce na sekven´atoru Ion ProtonTM System (Life Technologies, USA). Bioinformatick´ e zpracov´ an´ı
Ready, z´ıskan´e pˇreˇcten´ım sekvenc´ı plazmatick´ ych DNA molekul z jednoho konce, byly mapov´any na refereˇcn´ı sekvenci lidsk´eho genomu (hg19) pomoc´ı Torrent Mapping Alignment Program v4.0-R77189 (Life Technologies, USA). Ready s mapovac´ı kvalitou menˇs´ı neˇz 50, neMetodika namapovan´e ready, duplik´aty a ready s d´elkou kratˇs´ı neˇz 35 bp nebo delˇs´ı neˇz 180 bp byly odstranˇeny. Pro kaˇzd´ y Soubor chromozom jsme spoˇc´ıtali poˇcet unik´atn´ıch read˚ u v 60 kb u ´sec´ıch (binech) spoleˇcnˇe s pr˚ umˇern´ ym obsahem GC. Biny Soubor pro tuto studii vzeˇsel z rozs´ ahlejˇs´ı prospek- s GC obsahem vyˇsˇs´ım neˇz 0,60 nebo niˇzˇs´ım neˇz 0,30 tivn´ı cfDNA studie, ve kter´e n´ aˇs vlastn´ı cfDNA test byly odfiltrov´any. Minim´aln´ı poˇzadovan´ y poˇcet unik´atn´ıch [17, 18] byl integrov´ an kontingenˇcn´ı formou do rutinn´ıho read˚ u jsme stanovili na 2 000 000. c
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Obr´ azek 1: Poˇcet obsazen´ ych bin˚ u na Y chromozomu u plod˚ u muˇzsk´eho a ˇzensk´eho pohlav´ı spoleˇcnˇe se tˇremi atypick´ ymi pˇr´ıpady.
Statistick´ e zpracov´ an´ı a stanoven´ı pohlav´ı plodu Pro statistick´e zpracov´ an´ı jsme vyuˇzili statistick´e programovac´ı prostˇred´ı R [20]. Pohlav´ı plodu jsme hodnotili pomoc´ı dvou n´ astroj˚ u. Prvn´ım z nich byl n´aˇs novˇe navrˇzen´ y poˇcet obsazen´ ych bin˚ u na Y chromozomu. Necht’ n je poˇcet bin˚ u na Y chromozomu a Yi je i-t´ y bin na Y chromozomu pro i = (1, . . . , n). Potom NYi je poˇcet read˚ u v Yi a poˇcet obsazen´ ych bin˚ u na Y chromozomu NY bin je definov´ an jako
NY bin =
n X
[NYi > 0]
i=1
1 0
pro NYi > 0; pro NYi = 0.
(1)
Jak jsme jiˇz zm´ınili, pod´ıl sekvenc´ı poch´ azej´ıc´ıch z Y chromozomu se liˇs´ı u muˇzsk´ ych a ˇzensk´ ych plod˚ u. Proto druh´ ym n´astrojem, kter´ y jsme pouˇzili, bylo procento unik´atn´ıch read˚ u poch´ azej´ıc´ıch z Y chromozomu %chrY , tedy
%chrY =
poˇcet read˚ u z Y chromozomu · 100 poˇcet read˚ u z autozom˚ u
(2)
Oba n´astroje, NY bin and %chrY , jsme porovnali se zn´am´ ym pohlav´ım plodu.
V´ ysledky Celkem jsme od ledna 2015 do kvˇetna 2016 zpracovali 1 669 prenat´aln´ıch vzork˚ u. Z nich 17 (1.0 %) mˇelo celkov´ y poˇcet read˚ u niˇzˇs´ı neˇz 2 000 000, 58 (3.5 %) vzork˚ u nesplnilo poˇzadovan´a krit´eria kvality a v´ ysledek tˇehotenstv´ı nebyl zn´am u ˇsesti (0.4 %) pˇr´ıpad˚ u. Pro navrˇzenou anal´ yzu bylo tedy dostupn´ ych 1 588 vzork˚ u. Mezi nimi bylo IJBH – Volume 4 (2016), Issue 2
823 (51.8 %) vzork˚ u poch´azej´ıc´ıch z tˇehotenstv´ı s plodem fenotypovˇe muˇzsk´eho pohlav´ı a 765 (48.2 %) vzork˚ u z tˇehotenstv´ı s plodem fenotypovˇe ˇzensk´eho pohlav´ı. Rozloˇzen´ı poˇctu obsazen´ ych bin˚ u na Y chromozomu NY bin je zn´azornˇeno na Obr´azku 1. M˚ uˇzeme rozliˇsit tˇri zˇreteln´e z´ony. Vˇsechny plody muˇzsk´eho pohlav´ı mˇely hodnoty NY bin mezi 89–254 s pr˚ umˇerem 201. Na druh´e stranˇe, vˇsechny plody ˇzensk´eho pohlav´ı aˇz na tˇri pˇr´ıpady mˇely hodnoty NY bin v´ yraznˇe niˇzˇs´ı s rozpˇet´ım 5–21 a s pr˚ umˇernou hodnotou 11. Mezi tˇemito dvˇema oddˇelen´ ymi z´onami jsou pouze tˇri pˇr´ıpady, ve kter´ ych byl plod fenotypovˇe ˇzensk´eho pohlav´ı, ale poˇcet obsazen´ ych bin˚ u na Y chromozomu byl pˇr´ıliˇs vysok´ y ve srovn´an´ı s ostatn´ımi ˇzensk´ ymi plody a z´aroveˇ n pˇr´ıliˇs n´ızk´ y ve srovn´an´ı s plody muˇzsk´ ymi. Nicm´enˇe, jestliˇze se na tyto pˇr´ıpady pod´ıv´ame bl´ıˇze, vˇsechny tyto pˇr´ıpady poch´azely z patologick´ ych tˇehotenstv´ı. V prvn´ı pˇr´ıpadˇe, s NY bin = 78, se jednalo o 37letou tˇehotnou s vyˇsˇs´ım rizikem z prvotrimestr´aln´ıho kombinovan´eho testu (PAPP-A 0.42 MoM, free-βhCG 2.21 MoM a NT 1.2 mm). Druhotrimetr´aln´ı plazmatick´ y AFP byl v´ yraznˇe zv´ yˇsen (5.31 MoM) a ultrazvukov´e vyˇsetˇren´ı v 18. t´ ydnu uk´azalo tˇeˇzkou intrauterinn´ı r˚ ustovou retardaci a oligohydramnion. Pacientka byla referov´ana na vyˇsˇs´ı pracoviˇstˇe, kde jiˇz diagnostikovali fetus mortus a tˇehotenstv´ı bylo ukonˇceno. Pˇri pitvˇe byl pops´an tˇeˇzce hypotrofick´ y plod v´aˇz´ıc´ı pouh´ ych 75 g s fenotypovˇe ˇzensk´ ym genit´alem. Bohuˇzel, pro pokroˇcilou autol´ yzu pitva org´an˚ u provedena nebyla. M˚ uˇzeme spekulovat, zda v tomto pˇr´ıpadˇe nemohla b´ yt pˇr´ıˇcinou pˇr´ıpadn´a triploidie, karyotyp plodu ale vyˇsetˇren nebyl. Druh´ ym pˇr´ıpadem byla 33-let´a tˇehotn´a s rizikem z prvotrimetr´aln´ıho kombinovan´eho testu pro trizomii 21 1/90 a pro trizomii 13 1/120. Integrovan´ y test v 17. t´ ydnu uk´azal vysok´e riziko pro trizomii 18 1/2, c
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Obr´ azek 2: Procento read˚ u poch´ azej´ıc´ıch z Y chromozomu u plod˚ u muˇzsk´eho a ˇzensk´eho pohlav´ı spoleˇcnˇe se tˇremi atypick´ ymi pˇr´ıpady.
pro trizomii 13 1/140, pro defekt neur´ aln´ı trubice 1/14 a pro Smith-Lemli-Opitz˚ uv syndrom 1/5. Ultrazvukov´e vyˇsetˇren´ı v 18. t´ ydnu odhalilo asymetrickou intrauterinn´ı r˚ ustovou retardaci, oligohydramnion, mikromandibulu a srdeˇcn´ı vadu. CfDNA test byl negativn´ı na bˇeˇzn´e trizomie, ale hodnota NY bin = 62 byla atypick´a. Proveden´a amniocent´eza nakonec odhalila triploidii 69,XXY. Posledn´ı tˇret´ı pˇr´ıpad se t´ ykal 28-let´e primigravidy. Proveden´ y cfDNA test byl negativn´ı pro bˇeˇzn´e trizomie, ale hodnota NY bin = 36 byla cca dvojn´asobn´a, neˇz pozorujeme u plod˚ u ˇzensk´eho pohlav´ı. Ultrazvukov´e vyˇsetˇren´ı v 16. t´ ydnu pouk´ azalo na hraniˇcn´ı ventrikulomegalii a srdeˇcn´ı vadu. Pacientka souhlasila s proveden´ım amniocent´ezy a vyˇsetˇren´ y karyotyp plodu odhalil monozomii X chromozomu (Turner˚ uv syndrom, 45,X). Protoˇze jedn´ım z moˇzn´ ych mechanizm˚ u vzniku Turnerova syndromu je i mitotick´ a ztr´ ata Y chromozomu [21], moˇzn´ ym vysvˇetlen´ım atypick´eho poˇctu read˚ u podch´ azej´ıc´ıch z Y chromozomu m˚ uˇze b´ yt placent´ arn´ı mozaicismus zahrnuj´ıc´ı Y chromozom (45,X / 46,XY).
Obr´ azek 3: Krabicov´e diagramy poˇctu obsazen´ ych bin˚ u na Y chromozomu a procenta unik´ atn´ıch read˚ u poch´ azej´ıc´ıch z Y chromozomu u muˇzsk´ ych a ˇzensk´ ych plod˚ u (bez tˇr´ı patologick´ ych pˇr´ıpad˚ u).
V´ ysledky druh´eho n´ astroje, procenta unik´ atn´ıch read˚ u poch´azej´ıc´ıch z Y chromozomu %chrY , jsou prezentov´any na Obr´azku 2. Aˇckoli je rozd´ıl mezi muˇzsk´ ymi a ˇzensk´ ymi plody patrn´ y, mezera je v´ yraznˇe menˇs´ı neˇz v pˇr´ıpadˇe poˇctu obsazen´ ych bin˚ u, a dokonce v jednom pˇr´ıpadˇe se rozdˇelen´ı obou pohlav´ı pˇrekr´ yvaj´ı. Rozpˇet´ı hodnot pro tˇehotenstv´ı s muˇzsk´ ymi plody bylo 0,00765– 0,06307 s pr˚ umˇerem 0,02389, zat´ımco rozpˇet´ı hodnot pro tˇehotenstv´ı s ˇzensk´ ymi plody bylo 0,00246–0,00773 s pr˚ umˇernou hodnotou 0,00421. Kromˇe toho, hodnoty v´ yˇse popsan´ ych tˇr´ı patologick´ ych pˇr´ıpad˚ u se pohybovaly v rozmez´ı hodnot, kter´e jsme pozorovali u norm´aln´ıch ˇzensk´ ych plod˚ u, a nebylo moˇzn´e je tedy od nich odliˇsit.
Srovn´an´ı poˇctu obsazen´ ych bin˚ u na Y chromozomu a procenta unik´atn´ıch read˚ u z Y chromozomu u muˇzsk´ ych a ˇzensk´ ych plod˚ u (mimo tˇri patologick´e pˇr´ıpady) je shrnuto pomoc´ı krabicov´ ych diagram˚ u na Obr´azku 3. Je zˇreteln´e, ˇze mezera mezi plody muˇzsk´eho a ˇzensk´eho pohlav´ı je v´ yraznˇe ˇsirˇs´ı v pˇr´ıpadˇe poˇctu obsazen´ ych bin˚ u a umoˇzn ˇuje jednoznaˇcn´e urˇcen´ı pohlav´ı plodu. Kromˇe toho, hodnocen´ı poˇctu obsazen´ ych bin˚ u umoˇzn ˇuje zachycen´ı atypick´ ych pˇr´ıpad˚ u, kter´e spadnou do z´ony mezi obˇema pohlav´ımi a kter´e maj´ı vysok´e riziko pˇridruˇzen´e patologie. Pokud vyjmeme tˇri patologick´e pˇr´ıpady, umoˇzn ˇuje n´am poˇcet obsazen´ ych bin˚ u na Y chromozomu stanovit pohlav´ı plodu se 100% senzitivitou i specificitou.
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Hynek M. a kol.– Stanoven´ı pohlav´ı plodu pomoc´ı testov´an´ı voln´e DNA
Z´ avˇ er V pr´aci jsme prezentovali naˇse zkuˇsenosti s neinvazivn´ım stanoven´ım pohlav´ı plodu pomoc´ı cfDNA testov´an´ı. Navrhovan´e stanoven´ı pohlav´ı pomoc´ı hodnocen´ı poˇctu obsazen´ ych bin˚ u na Y chromozomu m´a vynikaj´ıc´ı senzitivitu i specificitu dosahuj´ıc´ı 100 % a je pouˇziteln´e od 10. t´ ydne tˇehotenstv´ı. Kromˇe toho, podstatnou v´ yhodou tohoto n´ astroje je, ˇze umoˇzn ˇuje zachytit atypick´e pˇr´ıpady, kter´e maj´ı vysok´e riziko pˇridruˇzen´e patologie.
Podˇ ekov´ an´ı Tato pr´ace byla podpoˇrena grantem Univerzity Karlovy ˇc. SVV-2016-260267.
Reference [1] Lo YMD, Corbetta N, Chamberlain P, et al. Presence of fetal DNA in maternal plasma and serum. Lancet 1997;350:485–487. [2] Lo YMD, Tein MS, Lau TK, et al. Quantitative analysis of fetal DNA in maternal plasma and serum: implications for noninvasive prenatal diagnosis. Am J Hum Genet 1998;62:768–75. [3] Fan HC, Blumenfeld YJ, Chitkara U, Hudgins L, Quake SR. Noninvasive diagnosis of fetal aneuploidy by shotgun sequencing DNA from maternal blood. Proc Natl Acad Sci U S A 2008;105(42):16266–71. [4] Agarwal A, Sayres LC, Cho MK, et al. Commercial landscape of noninvasive prenatal testing in the United States. Prenat Diagn 2013;33:521–531. [5] Mersy E, Smits LJ, van Winden LA, et al. Noninvasive detection of fetal trisomy 21: systematic review and report of quality and outcomes of diagnostic accuracy studies performed between 1997 and 2012. Hum Reprod Update 2013;19(4):318– 29. [6] Boon EMJ, Faas BHW. Benefits and limitations of whole genome versus targeted approaches for noninvasive prenatal testing for fetal aneuploidies. Prenat Diagn 2013;33:700–706. [7] Tabor A, Alfirevic Z. Update on procedure-related risks for prenatal diagnosis techniques. Prenat Diagn 2010; 27: 1–7. [8] Mackie FL, Hemming K, Allen S, Morris RK, Kilby MD. The accuracy of cell-free fetal DNA-based non-invasive prenatal testing in singleton pregnancies: a systematic review and bivariate meta-analysis. BJOG 2016; DOI 11.1111/14710528.14050.
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[9] Odeh M, Granin V, Kais M, et al. Sonographic fetal sex determination. Obstet Gynecol Surv 2009;64(1):50–57. [10] Colmant C, Morin-Surroca M, Fuchs F, et al. Non-invasive prenatal testing for fetal sex determination: is ultrasound still relevant? Eur J Obstet Gynecol Reprod Biol 2013;171(2):197– 204. [11] Wright CF, Wei Y, Higgins JP, Sagoo GS. Non-invasive prenatal diagnostic test accuracy for fetal sex using cell-free DNA a review and meta-analysis. BMC Res Notes 2012;5:476. [12] Chiu RW, Chan KC, Gao Y, et al. Noninvasive prenatal diagnosis of fetal chromosomal aneuploidy by massively parallel genomic sequencing of DNA in maternal plasma. Proc Natl Acad Sci U S A. 2008;105(51):20458–63. [13] Jiang F, Ren J, Chen F, et al. Noninvasive Fetal Trisomy (NIFTY) test: an advanced noninvasive prenatal diagnosis methodology for fetal autosomal and sex chromosomal aneuploidies. BMC Med Genomics 2012;5:57. [14] Liao C, Yin AH, Peng CF, et al. Noninvasive prenatal diagnosis of common aneuploidies by semiconductor sequencing. Proc Natl Acad Sci U S A 2014;111(20):7415–20. [15] Fern´ andez-Mart´ınez FJ, Galindo A, Garcia-Burguillo A, et al. Noninvasive fetal sex determination in maternal plasma: a prospective feasibility study. Genet Med 2012;14(1):101–6. [16] Liu FM, Wang XY, Feng X, et al. Feasibility study of using fetal DNA in maternal plasma for non-invasive prenatal diagnosis. Acta Obstet Gynecol Scand 2007;86(5):535–41. [17] Hynek M, Zembol F, Putzova M, Maresova I, Horackova S, Zvarova J, Stejskal D. MoM-based approach to noninvasive prenatal testing using exponentially weighted moving average chart and chromosomal fingerprint. Intern J Biomed Healthcare 2015;3(2):12–15. [18] Hynek M, Zembol F, Maresova I, Horackova S, Putzova M, Stejskal D. MoM-based approach to non-invasive prenatal testing using exponentially weighted moving average chart and chromosomal fingerprint. Ultrasound Obstet Gynecol 2015;46(Suppl.1):9. [19] Hynek M, Zembol F, Maresova I, Horackova S, Bitoova M, Koudova M, Stejskal D. Contingent cell-free DNA test in routine prenatal aneuploidy screening. Paediatr Croat 2016; 60(Suppl.2):25. [20] R Core Team (2014). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/ [accessed 20 June 2016]. [21] Yorifugi T, Muroi J, Mamada M, Uematsu A, Kawai M, Momoi T, Kaji M, Yamanaka C, Nakahata T. Analysis of the SRY gene in Turner syndrome patients with Y chromosomal material. J Med Genet 2001;38(11):E41.
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Abstrakt
Identifikace fenotypu PAS asociovan´ eho s fol´ aty na z´ akladˇ e rozd´ıln´ ych expres´ı mezi muˇ zi a ˇ zenami ˇ arek2 Daniel Krsiˇ cka1 , Radka Pourov´ a1 , Milan S´ 1
´ ˇ a republika Ustav biologie a l´ekaˇrsk´e genetiky FN Motol a 2. l´ekaˇrsk´e fakulty UK, Praha, Cesk´ 2
ˇ a republika EuroMISE Mentor Association, Praha, Cesk´
Kontakt: Daniel Krsiˇ cka ´ Ustav biologie a l´ ekaˇrsk´ e genetiky FN Motol a 2. l´ ekaˇrsk´ e fakulty UK ´ ˇ a Republika Adresa: V Uvalu 84, 150 06 Praha 5, Cesk´
IJBH 2016; 4(2):15 zasl´ ano: 15. ˇ cervence 2016 pˇrijato: 15. srpna 2016 publikov´ ano: 20. z´ aˇr´ı 2016
E–mail: [email protected]
Abstrakt
pozornost pak budeme vˇenovat tˇem gen˚ um, kter´e jsou specificky v´az´any na chromozomy X a Y, pˇr´ıpadnˇe takov´ ym, kter´e se pod´ıl´ı na biosynt´ez´ach steroidn´ıch hormon˚ u. K identifikaci suspektn´ıch gen˚ u pouˇzijeme data zdrav´ ych jedinc˚ u. Nalezen´e geny se n´aslednˇe pokus´ıme srovnat s daty pacient˚ u s PAS dostupn´ ymi na vlastn´ım pracoviˇsti i ve veˇrejn´ ych datab´az´ıch a popsat fenotyp PAS souvisej´ıc´ı s fol´aty.
Ned´avn´e publikace naznaˇcuj´ı souvislost mezi naruˇsen´ım metabolismu fol´ at˚ u a rozvojem poruchy autistick´eho spektra (PAS). C´ılem naˇs´ı pr´ ace je identifikovat fenotyp PAS souvisej´ıc´ı s fol´ aty pomoc´ı heuristick´ ych datov´ ych anal´ yz veˇrejn´ ych i vlastn´ıch datov´ ych b´az´ı. C´ılem aktu´aln´ıho experimentu je identifikovat rozd´ıly v expres´ıch gen˚ u souvisej´ıc´ı s utilizac´ı fol´ at˚ u, kter´e by ıˇ cov´ a slova mohly vysvˇetlovat nevyv´ aˇzen´ y pomˇer v prevalenci PAS Kl´ mezi chlapci a d´ıvkami pˇribliˇznˇe 4,5 : 1. Pˇredpokl´ad´ame, Autismus, Syndrom deficitu cerebr´aln´ıho fol´atu, Fol´at, ˇze m´ıra exprese gen˚ u, jejichˇz v´ ysledn´e proteiny katalyGenov´ a exprese zuj´ı reakce s fol´atov´ ymi kofaktory nebo s l´ atkami syntetizovan´ ymi pomoc´ı fol´ at˚ u, m˚ uˇze vypov´ıdat o celkov´e katalytick´e aktivitˇe tˇechto enzym˚ u. Pokud bychom nalezli Podˇ ekov´ an´ı v´ yznamn´ y rozd´ıl v expres´ıch mezi vzorky male a female, mohlo by to v´est k identifikaci kl´ıˇcov´ ych patologick´ ych mePr´ace byla podpoˇrena projektem SVV-2016-260267 chanism˚ u fenotypu PAS souvisej´ıc´ıho s fol´ aty. Speci´aln´ı Univerzity Karlovy.
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P˚ uvodn´ı ˇ cl´ anek
Poˇ c´ıtaˇ cov´ e modelov´ an´ı dyssynchronn´ıho srdce Miroslav Loˇ zek1,2 , Jan Janouˇsek1 , Lucie Riedlbauchov´ a3 , Lenka Lhotsk´ a4 1
ˇ a republika Dˇetsk´e kardiocentrum 2. LF UK a Fakultn´ı nemocnice v Motole, Praha, Cesk´ 2
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ˇ a republika Kardiologick´ a klinika 2. LF UK a Fakultn´ı nemocnice v Motole, Praha, Cesk´ 4
ˇ e vysok´e uˇcen´ı technick´e v Praze, Cesk´ ˇ a republika Cesk´
Abstrakt Tento ˇcl´anek se zab´yv´a pokroˇcilou anal´yzou dyssynchronn´ı kardiomyopatie. Precizn´ı popis nesynchronn´ı kontrakce srdeˇcn´ıch stˇen je stˇeˇzejn´ı pro nastaven´ı u ´spˇeˇsn´e resynchronizaˇcn´ı terapie. Poˇc´ıtaˇcov´e modelov´an´ı kardiovaskul´arn´ıho syst´emu pˇrin´aˇs´ı nov´e moˇznosti optimalizace l´eˇcby tohoto typu kardiomyopatie.
Kl´ıˇ cov´ a slova Blok Tawarova ram´enka, Dyssynchronn´ı kardiomyopatie, Poˇc´ıtaˇcov´e modelov´an´ı srdce, Srdeˇcn´ı resynchronizaˇcn´ı terapie
Kontakt: Miroslav Loˇ zek Dˇ etsk´ e kardiocentrum a Oddˇ elen´ı biomed. inˇzen´ yrstv´ı Fakultn´ı nemocnice v Motole ´ Adresa: V Uvalu 84, 150 06 Praha 5
IJBH 2016; 4(2):16–19 zasl´ ano: 15. ˇ cervence 2016 pˇrijato: 15. srpna 2016 publikov´ ano: 20. z´ aˇr´ı 2016
E–mail: [email protected]
´ Uvod Srdeˇcn´ı komorov´a elektromechanick´ a dyssynchronie je zapˇr´ıˇcinˇena aktivaˇcn´ım zpoˇzdˇen´ım v pˇrevodn´ım syst´emu srdeˇcn´ım na u ´rovni srdeˇcn´ıch komor. Blok´ ada lev´eho nebo prav´eho Tawarova ram´enka (LBBB nebo RBBB) je nejbˇeˇznˇejˇs´ı pˇr´ıˇcina dyssynchronn´ı kardiomyopatie. Dyssynchronn´ı kontrakce srdeˇcn´ıch komor sniˇzuje efektivitu srdeˇcn´ı pr´ace, zp˚ usobuje patologickou remodelaci komorov´eho myokardu a vede k fat´ aln´ımu srdeˇcn´ımu selh´an´ı. [2, 3, 4] Tento projekt je zamˇeˇren na pokroˇcilou anal´ yzu srdeˇcn´ı dyssynchronie s pouˇzit´ım klinick´ ych zobrazovac´ıch metod a poˇc´ıtaˇcov´eho modelov´ an´ı hemodynamiky kardiovaskul´arn´ıho syst´emu a mechaniky srdeˇcn´ıch svalov´ ych vl´aken. C´ılem projektu je optimalizace souˇcasn´e l´eˇcebn´e metody tzv. srdeˇcn´ı resynchronizaˇcn´ı terapie (CRT).
Metody Z´akladem modelov´ an´ı srdeˇcn´ı dyssynchronie u konkr´etn´ıho postiˇzen´eho srdce jsou preciznˇe zmˇeˇren´e a zanalyzovan´e parametry srdce, jeˇz lze pouˇz´ıt jako vstupn´ı parametry poˇc´ıtaˇcov´eho modelu. Speci´ aln´ı pokroˇcil´a poˇc´ıtaˇcov´a anal´ yza klinicky namˇeˇren´ ych dat pˇrin´aˇs´ı lepˇs´ı popis dyssynchronn´ı kardiomyopatie. IJBH – Volume 4 (2016), Issue 2
Anal´ yza srdeˇ cn´ı dyssynchronie Zobrazov´an´ı pomoc´ı magnetick´e rezonance (MRI) je jednou z nejvhodnˇejˇs´ıch metod pro urˇcen´ı morfologie a celkov´e funkce srdce. Objemov´a mˇeˇren´ı srdeˇcn´ıch komor jsou velmi d˚ uleˇzit´ ym aspektem pro stanoven´ı z´avaˇznosti srdeˇcn´ı dyssynchronie. Alternativn´ı metodou m˚ uˇze b´ yt prim´arnˇe echokardiografie, pˇr´ıpadnˇe kontrastn´ı angiokardiografie. [5, 6] Hemodynamick´ y popis dyssynchronn´ıho srdce je tvoˇren z mˇeˇren´ı tlakov´ ych a pr˚ utokov´ ych pomˇer˚ u v srdci. Toho lze uspokojivˇe doc´ılit kombinac´ı konvenˇcn´ı katetrizace srdce a pouˇzit´ım zobrazovac´ıch metod (MRI nebo echokardiografie). Pokroˇcil´a anal´ yza tlakov´ ych kˇrivek dopom´ah´a k ovˇeˇren´ı aktu´aln´ı u ´ˇcinnosti l´eˇcby. [5, 6] Anal´ yza mechaniky dyssynchronn´ı kontrakce srdce je zaloˇzena na kvalitn´ım zaznamen´av´an´ı pohybu srdeˇcn´ıch stˇen (tzv. strain a strain rate kˇrivky). Speci´aln´ı poˇc´ıtaˇcov´e zpracov´an´ı dat (napˇr. modifikovan´ y Internal Stretch Fraction parametr – ISF) pˇrin´aˇs´ı informaci jak o z´avaˇznosti dyssynchronie, tak o u ´ˇcinku l´eˇcebn´eho postupu. Mˇeˇren´ı strain kˇrivek lze prov´adˇet pomoc´ı MRI nebo echokardiografie (viz Obr´azek 1). [7] Elektrofyziologick´a katetrizaˇcn´ı studie pˇrid´av´a informaci o sekvenci elektrick´e aktivity v srdci. Zaznamen´av´an´ı intrakardi´aln´ıho elektrogramu umoˇzn ˇuje vytvoˇrit 3D mapu srdce popisuj´ıc´ı elektrick´e aktivaˇcn´ı zpoˇzdˇen´ı v r˚ uzn´ ych segmentech myokardu. [8] c
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Loˇzek M. a kol.– Poˇc´ıtaˇcov´e modelov´an´ı dyssynchronn´ıho srdce
Obr´ azek 1: Anal´ yza pohybov´ ych parametr˚ u prav´e komory u pacienta s RBBB za pouˇzit´ı modifikovan´eho ISF algoritmu [7].
Obr´ azek 2: Pˇr´ıklad poˇc´ıtaˇcov´e simulace popisuj´ıc´ı hustotu mechanick´e pr´ ace voln´e stˇeny prav´e srdeˇcn´ı komory (RV) ve vztahu k pravokomorov´e dyssynchronii a pulmon´ aln´ı insuficienci (PV regurgitation fraction). Nulov´ a hodnota aktivaˇcn´ıho zpoˇzdˇen´ı a regurgitaˇcn´ı frakce pulmon´ aln´ı chlopnˇe pˇredstavuje simulaci zdrav´eho srdce. N´ ar˚ ust aktivaˇcn´ıho zpoˇzdˇen´ı zp˚ usobuje sn´ıˇzen´ı celkov´e hustoty pr´ ace vykonan´e pravou volnou stˇenou srdeˇcn´ı. Zpˇetn´ y n´ avrat krve do komory srdeˇcn´ı (chlopenn´ı insuficience) vyˇzaduje vyˇsˇs´ı energetick´e n´ aroky na kontrakci srdce.
Srdeˇ cn´ı resynchronizaˇ cn´ı l´ eˇ cba
Poˇ c´ıtaˇ cov´ e modelov´ an´ı
CRT je klinick´ a terapeutick´ a metoda pouˇz´ıvan´a k l´eˇcbˇe dyssynchronn´ı kardiomyopatie. CRT zvyˇsuje efektivitu komorov´e kontrakce a vede k reverzn´ı remodelaci myokardu srdeˇcn´ıch komor. Principem CRT je pˇremostˇen´ı pˇreruˇsen´e ˇc´asti pˇrevodn´ıho syst´emu pomoc´ı elektrick´e stimulace pozdnˇe aktivovan´ ych segment˚ u myokardu postiˇzen´e srdeˇcn´ı komory. V´ ysledkem optimalizovan´e resynchronizaˇcn´ı terapie by mˇelo b´ yt obnoven´ı synchronn´ı kontrakce myokardu.
C´ılem poˇc´ıtaˇcov´eho modelov´an´ı je nasimulovat hemodynamick´e a mechanick´e dˇeje v dyssynchronn´ım srdci. Kardiovaskul´arn´ı model CircAdapt je d´ıky moˇznosti segmentace srdeˇcn´ıch stˇen vhodn´ ym n´astrojem pro simulov´an´ı heterogenn´ıho pohybu myokardu. [11] CircAdapt je matematick´ y model, kter´ y byl navrˇzen za u ´ˇcelem simulov´an´ı hemodynamiky a mechaniky srdce. Vstupn´ım nastaven´ım modelu jsou pˇredevˇs´ım morfologick´e a funkˇcn´ı parametry srdce (napˇr. rozmˇery chlopn´ı, tlouˇst’ka stˇen, aktivaˇcn´ı zpoˇzdˇen´ı, srdeˇcn´ı frekvence,
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Loˇzek M. a kol.– Poˇc´ıtaˇcov´e modelov´an´ı dyssynchronn´ıho srdce
Tabulka 1: Srdeˇcn´ı parametry u ´spˇeˇsn´e dlouhodob´e resynchronizace selh´ avaj´ıc´ı prav´e srdeˇcn´ı komory [1]. Doplnˇeno o parametry z´ıskan´e pomoc´ı poˇc´ıtaˇcov´eho modelov´ an´ı. EDVi/ESVi je end-diastolick´ y/end-systolick´ y indexovan´ y objem; EF je ejekˇcn´ı frakce; NYHA je kardiologick´ a klasifikace podle New York Heart Association; LV je lev´ a komora srdeˇcn´ı; RV je prav´ a komora; VO2 max je maxim´ aln´ı spotˇreba kysl´ıku bˇehem z´ atˇeˇzov´eho testu; MWD je hustota myokardi´ aln´ı pr´ ace (RW/SW/LW je prav´ a voln´ a stˇena/septum/lev´ a voln´ a stˇena); PW je hemodynamick´ a pr´ ace.
Parametr – zmˇ eˇ reno REDVi/RESVi [mL/m2 ] LEDVi/LESVi [mL/m2 ] EF RV/LV [%] NYHA VO2 max [mL/kg/min] Parametr – simulace MWD RW/SW/LW [kJ/m3 /beat] PW RV/LV [J/beat]
vaskul´arn´ı rezistence apod.). V´ ystupem modelu jsou hemodynamick´e (tlakov´e a pr˚ utokov´e) a mechanick´e (strain a stress) kˇrivky, kter´e je moˇzno d´ ale analyzovat pomoc´ı pokroˇcil´ ych algoritm˚ u (napˇr. ISF nebo v´ ypoˇcet myokardi´aln´ı pr´ace). CircAdapt je navrˇzen jako voln´ y zdrojov´ y k´od v programovac´ım jazyce Matlab, kter´ y je moˇzno d´ale pˇrizp˚ usobovat. [9, 10, 11, 12] Teoretick´ y obraz mechanismu dyssynchronie je moˇzno vytvoˇrit pomoc´ı souboru obecn´ ych simulac´ı, kter´e vyjadˇruj´ı vztah jednotliv´ ych patologi´ı ke sledovan´e veliˇcinˇe (viz Obr´azek 2). Simulace konkr´etn´ıho dyssynchronn´ıho srdce je z´avisl´a na vstupn´ıch parametrech modelu, kter´e mus´ı b´ yt spr´avnˇe z´ısk´any z klinicky namˇeˇren´ ych dat. Takto proveden´ a simulace odr´aˇz´ı individu´aln´ı stav kardiovaskul´ arn´ıho syst´emu pacienta v dobˇe mˇeˇren´ı. Modifikac´ı individu´ aln´ı simulace lze napodobit u ´ˇcinnost r˚ uzn´ ych druh˚ u terapie (CRT, v´ ymˇena chlopnˇe atd.).
Limitace Kvalitn´ı sbˇer a zpracov´ an´ı klinick´ ych dat je d˚ uleˇzitou podm´ınkou spr´avn´eho stanoven´ı z´ avaˇznosti srdeˇcn´ı dyssynchronie. R˚ uzn´e fyziologick´e podm´ınky pˇri mˇeˇren´ı klinick´ ych dat (tepov´a frekvence, anestezie apod.) mohou b´ yt pˇr´ıˇcinou nepˇresnost´ı v popisu a simulaci dyssynchronie.
Shrnut´ı Pokroˇcil´e poˇc´ıtaˇcov´e zpracov´ an´ı klinicky mˇeˇren´ ych dat a jejich n´asledn´e poˇc´ıtaˇcov´e modelov´ an´ı pˇrin´ aˇs´ı sofistikovan´ y popis mechanick´e srdeˇcn´ı dyssynchronie. Poˇc´ıtaˇcov´e modelov´ an´ı pˇrin´ aˇs´ı moˇznost v´ ypoˇctu specifick´ ych, klinicky nemˇeˇriteln´ ych parametr˚ u (napˇr. myokardi´aln´ı pr´ace), ˇc´ımˇz rozˇsiˇruje konvenˇcn´ı popis dyssynchronn´ı kardiomyopatie. Podle v´ ysledk˚ u simulac´ı lze tak´e podat teoretick´ y odhad efektivity n´ asledn´e l´eˇcby v akutn´ım st´adiu. IJBH – Volume 4 (2016), Issue 2
pˇ red CRT 212/172 80/46 19/41 II 21.0 pˇ red CRT 3.4/6.5/2.2 0.2/0.8
CRT (6 mˇ es´ıc˚ u) 141/87 63/28 38/56 I 30.4 CRT (6 mˇ es´ıc˚ u) 5.6/9.3/7.4 0.5/1.0
Tabulka 1 ukazuje publikovan´ y [1] pˇr´ıklad u ´spˇeˇsn´e resynchronizaˇcn´ı l´eˇcby u konkr´etn´ıho pacienta s korigovanou srdeˇcn´ı vadou (Fallotova tetralogie).
Podˇ ekov´ an´ı Tento projekt je podpoˇren z programov´eho projektu ˇ s reg. ˇc. 15 - 28029A a 15 Ministerstva zdravotnictv´ı CR 31398A. Tento rukopis byl d´ale podpoˇren projektem Specifick´eho vysokoˇskolsk´eho v´ yzkumu Ministerstva ˇskolstv´ı, ml´adeˇze a tˇelov´ ychovy s reg. ˇc. 260 267/2016.
Reference [1] P. Kubuˇs, O. Materna, P. Tax, V. Tomek, and J. Janouˇsek, Successful Permanent Resynchronization for Failing Right ” Ventricle After Repair of Tetralogy of Fallot“, Circulation, vol. 130, no. 22, pp. e186-e190, Nov. 2014. [2] W. Hui, C. Slorach, A. Dragulescu, L. Mertens, B. Bijnens, and M. K. Friedberg, Mechanisms of Right Ventricular Electrome” chanical Dyssynchrony and Mechanical Inefficiency in Children After Repair of Tetralogy of Fallot“, Circulation: Cardiovascular Imaging, vol. 7, no. 4, pp. 610-618, Jul. 2014. [3] J. Janouˇsek, P. Vojtoviˇ c, B. Huˇ c´ın, T. Tl´ askal, R. A. Gebauer, R. Gebauer, T. Matˇ ejka, J. Marek, and O. Reich, Resynchro” nization pacing is a useful adjunct to the management of acute heart failure after surgery for congenital heart defects“, The American Journal of Cardiology, vol. 88, no. 2, pp. 145-152, 2001. [4] A. M. Dubin, Electrical Resynchronization: A Novel Therapy ” for the Failing Right Ventricle“, Circulation, vol. 107, no. 18, pp. 2287-2289. [5] J. Gorcsan, T. Abraham, D. A. Agler, J. J. Bax, G. Derumeaux, R. A. Grimm, R. Martin, J. S. Steinberg, M. S. J. Sutton, and C. M. Yu, Echocardiography for Cardiac Resyn” chronization Therapy: Recommendations for Performance and Reporting A Report from the American Society of Echocardiography Dyssynchrony Writing Group Endorsed by the Heart Rhythm Society“, Journal of the American Society of Echocardiography, vol. 21, no. 3, pp. 191–213, 2008. [6] B. W. L. De Boeck, B. Kirn, A. J. Teske, R. W. Hummeling, P. A. Doevendans, M. J. Cramer, and F. W. Prinzen, Three”
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Loˇzek M. a kol.– Poˇc´ıtaˇcov´e modelov´an´ı dyssynchronn´ıho srdce
dimensional mapping of mechanical activation patterns, contractile dyssynchrony and dyscoordination by two-dimensional strain echocardiography: Rationale and design of a novel software toolbox“, Cardiovascular Ultrasound, vol. 6, no. 1, p. 22-, 2008.
[10] R. C. P. Kerckhoffs, J. Lumens, K. Vernooy, J. H. Omens, L. J. Mulligan, T. Delhaas, T. Arts, A. D. McCulloch, and F. W. Prinzen, Cardiac resynchronization: Insight from experimen” tal and computational models“, Progress in Biophysics and Molecular Biology, vol. 97, no. 2-3, pp. 543-561, 2008.
[7] B. Kirn, A. Jansen, F. Bracke, B. van Gelder, T. Arts, and F. W. Prinzen, Mechanical discoordination rather than dyssyn” chrony predicts reverse remodeling upon cardiac resynchronization“, AJP: Heart and Circulatory Physiology, vol. 295, no. 2, pp. H640-H646, Jun. 2008.
[11] J. Lumens, T. Delhaas, B. Kirn, and T. Arts, Three-Wall ” Segment (TriSeg) Model Describing Mechanics and Hemodynamics of Ventricular Interaction“, Annals of Biomedical Engineering, vol. 37, no. 11, pp. 2234-2255, 2009.
[8] C. Knackstedt, P. Schauerte, and P. Kirchhof, Electro” anatomic mapping systems in arrhythmias“, Europace, vol. 10, no. Supplement 3, pp. iii28-iii34, Nov. 2008. [9] T. Arts, K. Reesink, W. Kroon, and T. Delhaas, Simulation of ” adaptation of blood vessel geometry to flow and pressure: Implications for arterio-venous impedance“, Mechanics Research Communications, vol. 42, pp. 15-21, 2012.
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[12] J. Lumens, S. Ploux, M. Strik, J. Gorcsan, H. Cochet, N. Derval, M. Strom, C. Ramanathan, P. Ritter, M. Haissaguerre, P. Jais, T. Arts, T. Delhaas, F. W. Prinzen, and P. Bordachar, Comparative Electromechanical and Hemodynamic Effects of ” Left Ventricular and Biventricular Pacing in Dyssynchronous Heart Failure“, Journal of the American College of Cardiology, vol. 62, no. 25, pp. 2395-2403, 2013.
IJBH – Volume 4 (2016), Issue 2
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Kr´ atk´ y p˚ uvodn´ı ˇ cl´ anek
Standardizace sbˇ eru dat o bezpeˇ cnosti l´ eˇ civ´ ych pˇr´ıpravk˚ u v pˇredregistraˇ cn´ıch klinick´ ych studi´ıch a jeho optimalizace Radka Montoniov´ a1,2 , Jana Zv´ arov´ a2 1 2
´ ˇ a republika St´ atn´ı u ´stav pro kontrolu l´eˇciv (SUKL), Praha, Cesk´
´ Ustav hygieny a epidemiologie, 1. l´ekaˇrsk´ a fakulta, Univerzita Karlova a Vˇseobecn´ a fakultn´ı nemocnice v Praze
Abstrakt Bˇehem farmaceutick´eho v´yvoje l´eˇciv´ych pˇr´ıpravk˚ u doch´az´ı ke sbˇeru dat ohlednˇe jejich u ´ˇcinnosti a bezpeˇcnosti. Nasb´ıran´e u ´daje jsou n´aslednˇe farmaceutick´ymi firmami pˇredkl´ad´any k posouzen´ı pˇr´ısluˇsn´ym org´an˚ um, kter´e jsou zapojeny do hodnocen´ı a n´asledn´e registrace l´eˇciv´ych pˇr´ıpravk˚ u. Posledn´ım krokem v tomto hodnocen´ı je stanoven´ı tzv. pomˇeru pˇr´ınos˚ u (ve vˇetˇsinˇe pˇr´ıpad˚ u u ´daje o u ´ˇcinnosti) a rizik (´ udaje t´ykaj´ıc´ı se bezpeˇcnosti). V pˇr´ıpadˇe, ˇze je vyhodnocen pozitivn´ı pomˇer pˇr´ınos˚ u a rizik, m˚ uˇze b´yt doporuˇcena registrace l´eku.
Pro co nejpˇresnˇejˇs´ı urˇcen´ı tohoto pomˇeru je nutn´e m´ıt k dispozici data co nejlepˇs´ı kvality. Ne vˇsichni zkouˇsej´ıc´ı v klinick´ych studi´ıch pouˇz´ıvaj´ı standardizovan´e n´astroje urˇcen´e k pˇresn´e charakteristice bezpeˇcnostn´ıho profilu l´eˇciva, a proto by d˚ usledky nestandardizovan´eho sbˇeru dat o bezpeˇcnosti l´eˇciv mˇely b´yt zkoum´any.
Kl´ıˇ cov´ a slova Neˇz´adouc´ı u ´ˇcinky, klinick´e studie, sbˇer dat, l´eˇciv´e pˇr´ıpravky, regulaˇcn´ı org´any, data o bezpeˇcnosti.
Kontakt: Radka Montoniov´ a St´ atn´ı u ´stav pro kontrolu l´ eˇ civ ˇ arova 48, 100 41 Praha 10 Adresa: Srob´ E–mail: [email protected]
IJBH 2016; 4(2):20–21 zasl´ ano: 15. ˇ cervence 2016 pˇrijato: 15. srpna 2016 publikov´ ano: 20. z´ aˇr´ı 2016
C´ıle v´ yzkumu
trac´ı l´eˇciv´ ych pˇr´ıpravk˚ u, a to jiˇz od preklinick´ ych f´az´ı v´ yvoje, a stejnˇe tak i ve vˇsech f´az´ıch klinick´ ych studi´ı [5].
Pl´anujeme prov´est pilotn´ı studii, ve kter´e budeme zkoumat potˇreby pouˇz´ıv´ an´ı standardizovan´ ych n´ astroj˚ u pro sbˇer dat t´ ykaj´ıc´ıch se bezpeˇcnosti l´eku. Bude provedeno posouzen´ı bˇeˇzn´e praxe sbˇeru dat o bezpeˇcnosti v klinick´ ych studi´ıch a srovn´ an´ı forem tohoto sbˇeru pouˇz´ıvan´ ych v pˇredregistraˇcn´ı a poregistraˇcn´ı f´azi ˇzivotn´ıho cyklu l´eˇciv´eho pˇr´ıpravku.
´ Udaje o bezpeˇcnosti z´ıskan´e z klinick´ ych studi´ı mohou b´ yt ovlivnˇeny vn´ım´an´ım ze strany pacienta, zkouˇsej´ıc´ıho nebo jin´ ych z´ uˇcastnˇen´ ych stran. Hl´aˇsen´ı neˇz´adouc´ıch u ´ˇcink˚ u m˚ uˇze b´ yt zejm´ena ovlivnˇeno v pˇr´ıpadˇe multicentrick´ ych klinick´ ych studi´ı, kdy jsou do tˇechto studi´ı zapojeni zkouˇsej´ıc´ı z r˚ uzn´ ych zem´ı s odliˇsn´ ymi zvyky v klinick´e praxi a jinou l´ekaˇrskou slovn´ı z´asobou.
Druh´a ˇc´ast naˇseho v´ yzkumu se zamˇeˇr´ı na v´ yvoj n´astroj˚ u pro standardizovan´ y sbˇer dat t´ ykaj´ıc´ıch se bezpeˇcnosti l´eˇciv´ ych pˇr´ıpravk˚ u v klinick´ ych studi´ıch (pˇredregistraˇcnˇe). Bude zkoum´ ana nejen aplikovatelnost jiˇz vyvinut´ ych metod ˇsiroce pouˇz´ıvan´ ych v poregistraˇcn´ım obdob´ı (tj. CIOMS [1] , MedDRA [2]), ale i jin´e dostupn´e n´astroje pouˇz´ıvan´e pro standardizovan´ y z´ aznam l´ekaˇrsk´e dokumentace, jako napˇr. SNOMED CT [3] a jin´e [4].
Vzhledem k tomu, ˇze jednotliv´ı zkouˇsej´ıc´ı mohou pouˇz´ıvat r˚ uzn´e v´ yrazy k pops´an´ı urˇcit´eho neˇz´adouc´ıho u ´ˇcinku, doporuˇcuje se, aby sponzoˇri studi´ı zajistili, aby vˇsechny doslovn´e term´ıny pouˇz´ıvan´e zkouˇsej´ıc´ımi byly k´odov´any jako standardizovan´e, preferovan´e term´ıny uveden´e v k´odovac´ıch syst´emech nebo slovn´ıku (napˇr. MedDRA) [6]. Tento pˇr´ıstup byl tak´e doporuˇcen nˇekter´ ymi regulaˇcn´ımi autoritami [6, 7].
V minulosti bylo zah´ajeno nˇekolik iniciativ (napˇr. CIOMS-1 [1] a CIOMS-VI [7]), jejichˇz u ´ˇcelem bylo sjednoSouˇ casn´ y stav pozn´ an´ı tit definice a terminologii pro zajiˇstˇen´ı jednotn´eho hl´aˇsen´ı dat o bezpeˇcnosti a vytvoˇrit tak standardizovan´e n´astroje ubˇehu klinick´ ych studi´ı. Uplatnˇen´ı Hodnocen´ı bezpeˇcnosti je u ´stˇredn´ım prvkem ve vˇsech pro sbˇer tˇechto dat v pr˚ f´az´ıch ˇzivotn´ıho cyklu v´ yvoje nov´ ych l´eˇciv. Detailn´ı sle- tˇechto n´astroj˚ u se vˇsak naˇslo pˇrev´aˇznˇe v tzv. poregisdov´an´ı bezpeˇcnostn´ıho profilu je poˇzadov´ ano pˇred regis- traˇcn´ı f´azi, tj. u l´eˇciv´ ych pˇr´ıpravk˚ u jiˇz uveden´ ych na trh. IJBH – Volume 4 (2016), Issue 2
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Montoniov´a R., Zv´arov´a J.– Standardizace sbˇeru dat o bezpeˇcnosti l´eˇciv´ych pˇr´ıpravk˚ u
V dneˇsn´ı dobˇe jsou jiˇz tyto metody povaˇzov´ any za stan- Podˇ ekov´ an´ı dardn´ı a osvˇedˇcen´e n´ astroje farmakovigilance. Podle n´am dostupn´ ych informac´ı nebyl tento poPr´ace byla ˇc´asteˇcnˇe podpoˇrena projektem SVV 260267 ˇ a republika stup dosud implementov´ an vˇsemi sponzory pod´ılej´ıc´ımi Univerzity Karlovy, Cesk´ se na prov´adˇen´ı klinick´ ych studi´ı bˇehem pˇredregistraˇcn´ıho v´ yvoje l´eˇciv´ ych pˇr´ıpravk˚ u, a lze se tedy domn´ıvat, ˇze je Reference ˇz´adouc´ı zav´est tyto postupy tak´e pro pˇredregistraˇcn´ı obdob´ı v´ yvoje l´ek˚ u a vytvoˇrit standardizovan´ y pˇr´ıstup pro [1] International Reporting of Adverse Drug Reactions, Final Resbˇer u ´daj˚ u o bezpeˇcnosti v klinick´ ych studi´ıch. port of CIOMS Working Group. Council for International Organizations of Medical Sciences, Geneva, 1990. (CIOMS-I)
Uplatnˇ en´ı v biomedic´ınˇ e a zdravotnictv´ı Informace t´ ykaj´ıc´ı se bezpeˇcnostn´ıho profilu l´eku hraje kl´ıˇcovou roli nejen pˇri rozhodov´ an´ı regulaˇcn´ıch org´an˚ u v pr˚ ubˇehu registrace l´eˇciv´ ych pˇr´ıpravk˚ u, ale i pro oˇsetˇruj´ıc´ı l´ekaˇre, kteˇr´ı si maj´ı b´ yt vˇedomi moˇzn´ ych neˇz´adouc´ıch u ´ˇcink˚ u zp˚ usoben´ ych pod´ an´ım dan´e l´eˇcby. Optim´aln´ı bezpeˇcnost sbˇeru dat (a jejich anal´ yza) by tedy mˇely zajistit moˇznost amplifikace slab´ ych bezpeˇcnostn´ıch sign´ al˚ u nebo toxicity l´eku (napˇr. kombinace duˇsnosti, kaˇsle, s´ıp´ an´ı nebo pleuritidy m˚ uˇze poskytnout citlivˇejˇs´ı zhodnocen´ı plicn´ı toxicity neˇz kter´ ykoliv z tˇechto neˇz´adouc´ıch u ´ˇcink˚ u hl´ aˇsen´ ych samostatnˇe) [6]. Kromˇe toho, standardizace definic a terminologie m˚ uˇze v´est k moˇznosti sdruˇzov´ an´ı dostupn´ ych u ´daj˚ u o bezpeˇcnosti, jeˇz poskytuje pˇresnˇejˇs´ı informace t´ ykaj´ıc´ı se bezpeˇcnostn´ıho profilu urˇcit´eho l´eˇciv´eho pˇr´ıpravku.
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[2] Medical Dictionary for Regulatory Activities (MedDRA).Available online: http://www.meddra.org/ (accessed 25 July 2016) [3] Systemized Nomenclature of Medicine, Clinical Terms (SNOMED CT). Available online: http://www.ihtsdo.org/ snomed-ct/what-is-snomed-ct/history-of-snomed-ct (accessed 25 July 2016) [4] Electronic Health Record for Clinical Research. Available online: www.ehr4cr.eu (accessed 25 July 2016) [5] Yao, Bin et al. ”Safety Monitoring in Clinical Trials.” Pharmaceutics 5.1 (2013): 94-106. PMC. Web. 25 July 2016. [6] United States Food and Drug Administration. Guidance for Industry, Premarketing Risk Assessment, 2005. Available online: http://www.fda.gov/downloads/RegulatoryInformation/ Guidances/ucm126958.pdf (accessed 25 July 2016) [7] Management of Safety Information from Clinical Trials Report of CIOMS Working Group VI. Council for International Organizations of Medical Sciences, Geneva, 2005. (CIOMS-VI)
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P˚ uvodn´ı ˇ cl´ anek
Komunitn´ı v´ yvoj a pokroˇ cil´ e funkce nositeln´ e elektroniky v oblasti self-managementu diabetu Miroslav Muˇ zn´ y1 , Eirik Arsand2 , Jan Muˇ z´ık1,3 1
Centrum podpory aplikaˇcn´ıch v´ystup˚ u a spin-off firem, 1. l´ekaˇrsk´ a fakulta, ˇ a republika Univerzita Karlova, Praha, Cesk´ 2
3
Norsk´e centrum pro eHealth v´yzkum, UNN, Tromso, Norsko
Katedra informaˇcn´ıch a kom. tech. v l´ekaˇrstv´ı, Fakulta biomedic´ınsk´eho inˇzen´yrstv´ı, ˇ e vysok´e uˇcen´ı technick´e v Praze, Cesk´ ˇ a republika Cesk´
Abstrakt Aplikace Diabetesdagboka je implementac´ı diabetick´eho den´ıku s pokroˇcil´ymi funkcemi v chytr´em mobiln´ım telefonu. V r´amci naˇseho v´yzkumu v oblasti self-managementu diabetu jsme nˇekter´e funkce aplikace Diabetesdagboka integrovali do chytr´ych hodinek Pebble, kter´e mohou usnadnit kaˇzdodenn´ı pouˇz´ıv´an´ı elektronick´eho diabetick´eho den´ıku. Kromˇe postupn´eho vylepˇsov´an´ı na z´akladˇe technologick´eho pokroku nositeln´e elektroniky z´aroveˇ n sledujeme v´yvoj v oblasti komunitn´ıho v´yvoje (DIY).
Na z´akladˇe nov´ych poznatk˚ u z tˇechto oblast´ı jsme navrhli nˇekolik nov´ych funkc´ı u kter´ych vyhodnocujeme jejich pouˇzitelnost a zvaˇzujeme moˇznosti jejich dalˇs´ı integrace.
Kl´ıˇ cov´ a slova mHealth, chytr´e hodinky, sn´ımaˇc fyzick´e aktivity, kontinu´aln´ı glukometr, DIY
Kontakt: Miroslav Muˇ zn´ y Centrum podpory aplikaˇ cn´ıch v´ ystup˚ u a spin-off firem 1. l´ ekaˇrsk´ a fakulta, Univerzita Karlova Adresa: Studniˇ ckova 7, 128 08 Praha 2 E–mail: [email protected]
IJBH 2016; 4(2):22–24 zasl´ ano: 15. ˇ cervence 2016 pˇrijato: 15. srpna 2016 publikov´ ano: 20. z´ aˇr´ı 2016
´ Uvod
die jsou shrnuty v ˇcl´anku v ˇcasopise Journal of Diabetes Science and Technology [3]. Nˇekter´e z pokroˇcil´ ych funkc´ı, kter´e bylo moˇzn´e integrovat na z´akladˇe v´ yvoje v oblasti Dosavadn´ım v´ ysledkem naˇseho v´ yzkumu v oblasti self- nositeln´e technologie, jsou pops´any v konferenˇcn´ım absmanagementu diabetu je n´ avrh a v´ yvoj aplikace Diabete- traktu [4]. sdagboka pro chytr´e telefony, kter´ a je dostupn´ a jak pro platformu Google Android tak Apple iOS [1]. Jsme si z´aroveˇ n vˇedomi rostouc´ı d˚ uleˇzitosti nositeln´e elektroniky Metody v oblasti eHealth, a proto se zab´ yv´ ame t´ım, jak vyuˇz´ıt tato zaˇr´ızen´ı (napˇr. chytr´e hodinky, sn´ımaˇce pohybov´e Kromˇe v´ yvoje v oblasti nositeln´ ych technologi´ı lze aktivity, senzory), kter´e mohou b´ yt cenn´ ym zdrojem me- tak´e pozorovat pomˇernˇe velkou aktivitu v oblasti Do-Itdic´ınsk´ ych dat a tak´e mohou slouˇzit ke zprostˇredkov´an´ı Yourself (DIY, v pˇrekladu Udˇelej si s´am‘), coˇz jsou z velk´e ’ lepˇs´ıho pˇr´ıstupu k funkc´ım aplikace pro chytr´ y telefon. ˇc´asti komunitn´ı projekty, kter´e si kladou za c´ıl vytvoˇrit V srpnu 2014 jsme vydali doplˇ nkovou aplikaci – Dia- soubor n´avod˚ u, postup˚ u a programov´eho vybaven´ı (aplibetesdagboka pro chytr´e hodinky Pebble [2]. Po t´emˇeˇr kac´ı). Vˇsechny tyto v´ ystupy jsou poskytov´any zdarma, 2 letech kdy byla aplikace zdarma dostupn´ a v obchodˇe nicm´enˇe nen´ı moˇzn´e zaruˇcit jejich funkˇcnost, robustnost Pebble Store evidujeme pˇres 500 staˇzen´ı. Jelikoˇz zpˇetn´a a dostateˇcn´e zabezpeˇcen´ı, jelikoˇz tyto komunitn´ı projekty vazba od uˇzivatel˚ u t´eto aplikace je pozitivn´ı (uˇzivatel´e se ˇcasto udrˇzuj´ı rozpracovan´e. V oblasti self-managentu shled´avaj´ı aplikaci uˇziteˇcnou a praktickou), zkoum´ame diabetu se DIY projekty ˇcasto zamˇeˇruj´ı na probl´em exdalˇs´ı moˇznosti jej´ıho rozˇs´ıˇren´ı a integrace na z´ akladˇe trakce a pˇrenosu dat z r˚ uzn´ ych zdravotnick´ ych pˇr´ıstroj˚ u, v´ yvoje v oblasti nositeln´ ych technologi´ı. Aspekty n´ avrhu kter´ y bˇeˇznˇe ˇreˇs´ı pouˇzit´ım metod reverzn´ıho inˇzen´ yrstv´ı. naˇs´ı aplikace pro chytr´e hodinky a v´ ysledky n´ asledn´e stu- V r´amci naˇseho v´ yzkumu se snaˇz´ıme sledovat jejich v´ yvoj IJBH – Volume 4 (2016), Issue 2
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a pˇr´ıpadnˇe je testovat ve snaze objektivnˇe zhodnotit jejich pouˇzitelnost. T´ımto zp˚ usobem tak´e m˚ uˇzeme posoudit, zda je v´ ystup DIY projektu dostateˇcnˇe vhodn´ y pro integraci do n´astroj˚ u, kter´e m´ ame v naˇsem portfoliu. Pˇr´ıkladem takov´eho DIY projektu je open-source aplikace Nightscout [5]. Z´ akladn´ım u ´ˇcelem t´eto aplikace je umoˇznit kolekci dat z kontinu´ aln´ıho glukometru (napˇr. kontinu´ aln´ı glukometr Dexcom) a zpˇr´ıstupnit tato data pro vzd´ alen´e monitorov´ an´ı skrze s´ıt’ internet. Pˇrenos dat z kontinu´ aln´ıho glukometru je zprostˇredkov´an bud’ kabelov´ ym propojen´ım s mobiln´ım telefonem, nebo bezdr´atovˇe, za pouˇzit´ı zaˇr´ızen´ı jm´enem xDrip [6]. Toto zaˇr´ızen´ı, jehoˇz dokumentace a n´ avod pro sestaven´ı jsou volnˇe dostupn´e na s´ıti internet, je pˇr´ıkladem dalˇs´ıho DIY projektu, kter´e navrhla komunita okolo projektu Nightscout. Nightscout byl p˚ uvodnˇe vytv´ aˇren skupinou rodiˇc˚ u dˇet´ı s diabetem I. typu. V ˇcervnu roku 2016 mˇela komunita okolo tohoto projektu v´ıce neˇz 40 ˇclen˚ u staraj´ıc´ıch se o jeho spr´ avu a vylepˇsov´ an´ı. Poˇcet koncov´ ych uˇzivatel˚ u neust´ ale roste (d´ ano tak´e popularitou kontinu´aln´ıho glukometru Dexcom), nicm´enˇe mnoho potenci´aln´ıch uˇzivatel˚ u m˚ uˇze b´ yt odrazeno pomˇernˇe velkou technickou n´aroˇcnost´ı zprovoznˇen´ı cel´eho syst´emu.
dobnˇe jako pouˇz´ıv´an´ı projektu Nightscout mohou b´ yt tak´e alarmy velmi uˇziteˇcn´e pro rodiˇce dˇet´ı s diabetem I. typu – rodiˇce mohou pouˇz´ıvat aplikaci pro vzd´alen´e monitorov´an´ı jejich dˇet´ı a alarmy si mohou nechat vyvol´avat na vlastn´ıch hodink´ach. Funkce alarm˚ u m˚ uˇze b´ yt d´ale vylepˇsena tak´e d´ıky v´ yvoj´aˇrsk´e knihovnˇe Pebble Health, kter´a umoˇzn ˇuje automaticky detekovat sp´anek uˇzivatele hodinek. Algoritmus pro detekci sp´anku, kter´ y je v t´eto knihovnˇe implementov´an, pracuje s daty z akcelerometrick´eho senzoru a z´aroveˇ n s daty z ˇcidla osvˇetlen´ı. Jako dalˇs´ı krok proto zvaˇzujeme vyuˇzit´ı detekce sp´anku pro povolen´ı/zak´az´an´ı (pˇr´ıpadnˇe zeslaben´ı ˇci zes´ılen´ı) notifikace n´ami novˇe implementovan´ ych alarm˚ u indikuj´ıc´ıch vysokou/n´ızkou hladinu glyk´emie.
V´ ysledky Jelikoˇz projekt Nightscout poskytuje unik´ atn´ı funkci (zpˇr´ıstupnˇen´ı hodnot glyk´emie v re´ aln´em ˇcase z velmi rozˇs´ıˇren´eho kontinu´ aln´ıho glukometru), rozhodli jsme se jej integrovat v r´ amci naˇs´ı aplikace pro chytr´e hodinky Pebble. D´ıky tomu, ˇze projekt je open-source (zdrojov´e k´ody jsou volnˇe pˇr´ıstupn´e na s´ıti internet), jsme byli schopni upravit naˇsi aplikaci pro chytr´e hodinky Pebble tak, aby uˇzivatel mohl pohodlnˇe sledovat aktu´aln´ı hodnotu glyk´emie z kontinu´ aln´ıho glukometru spoleˇcnˇe s jej´ım trendem na displeji hodinek. Souˇcasn´a podoba hlavn´ı obrazovky aplikace pro chytr´e hodinky Pebble je zobrazena na Obr´ azku 1. Uˇzivateli jsou prezentov´any hodnoty posledn´ıch registrac´ı inzul´ınu a karbohydr´at˚ u. D´ale je moˇzn´e sledovat aktu´ aln´ı poˇcet krok˚ u detekovan´ ych pomoc´ı integrovan´eho akcelerometru, stav baterie v horn´ım panelu obrazovky a aktu´ aln´ı hodnotu glyk´emie spoleˇcnˇe se ˇsipkou indikuj´ıc´ı jej´ı trend v lev´em horn´ım rohu. Dalˇs´ı oblast´ı, kde mohou b´ yt data z kontinu´aln´ıho glukometru zpˇr´ıstupnˇen´ a v re´ aln´em ˇcase velmi uˇziteˇcn´a, jsou upozornˇen´ı a alarmy. V naˇs´ı aplikaci Diabetesdagboka pro chytr´e hodinky Pebble jsme implementovali konfigurovateln´e upozornˇen´ı na nadch´ azej´ıc´ı potˇrebu mˇeˇren´ı glyk´emie. V nadch´ azej´ıc´ı aktualizaci aplikace pl´anujeme tuto funkci rozˇs´ıˇrit o upozornˇen´ı na aplikaci inzulinu a pˇr´ıjem karbohydr´ at˚ u. Upozornˇen´ı je zobrazeno na hlavn´ı obrazovce aplikace a uˇzivatel je z´ aroveˇ n informov´an pomoc´ı vibrace hodinek. Integrac´ı projektu Nightscout jsme byli schopni spektrum upozornˇen´ı a alarm˚ u nad´ale rozˇs´ıˇrit o alarmy indikuj´ıc´ı vysokou/n´ızkou hladinu glyk´emie. Poc
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Obr´ azek 1: Hlavn´ı obrazovka aplikace pro chytr´e hodinky Pebble zobrazuj´ıc´ı aktu´ aln´ı hodnotu glyk´emie a ˇsipku zn´ azorˇ nuj´ıc´ı jej´ı trend v lev´em horn´ım rohu.
Dalˇs´ım vylepˇsen´ım, kter´e je moˇzn´e implementovat na z´akladˇe zpˇr´ıstupnˇen´ı detekce sp´anku, je noˇcn´ı reˇzim ´ celem tohoto noˇcn´ıho reˇzimu je de(m´od) hodinek. Uˇ tailn´ı interpretace glykemick´ ych dat z kontinu´aln´ıho glukometru v podobˇe grafu zobrazen´eho na displeji hodinek. Tento m´od m˚ uˇze b´ yt inicializov´an kombinac´ı detekovan´e sp´ankov´e aktivity a orientac´ı hodinek poloˇzen´ ych na stole ˇci noˇcn´ım stolku. Kromˇe integrace aplikace Nightscout se snaˇz´ıme vyuˇz´ıt tak´e jin´e technologie, kter´e jsou zahrnuty v nov´e generaci hodinek Pebble Time (uvedena na trh v roce 2015). Tato verze chytr´ ych hodinek Pebble m´a na sv´e zadn´ı stranˇe integrov´an dedikovan´ y konektor pro pˇripojen´ı extern´ıch modul˚ u (napˇr. senzor˚ u), kter´ ym se ˇr´ık´a smartstrap“. ” K v´ yvoji tˇechto modul˚ u je moˇzn´e vyuˇz´ıt volnˇe dostupn´ ych sch´emat pro 3D tisk´arny. V r´amci naˇseho v´ yzkumu zvaˇzujeme vytvoˇrit 2 takov´eto moduly za u ´ˇcelem pˇredveden´ı demonostrace t´eto technologie: IJBH – Volume 4 (2016), Issue 2
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• Modul integruj´ıc´ı LED diodu indikuj´ıc´ı aktu´ aln´ı hladinu glyk´emie pomoc´ı zmˇeny jej´ı barvy; • Modul poskytuj´ıc´ı bezdr´ atov´ y pˇrenos dat z kontinu´aln´ıho glukometru ˇci inzul´ınov´e pumpy (tzn. modul integruj´ıc´ı podobnou funkcionalitu jako zaˇr´ızen´ı xDrip, kter´e vˇsak mus´ı uˇzivatel nosit samostatnˇe). Nov´a generace chytr´ ych hodinek Pebble tak´e integruje mikrofon. V´ yvoj´aˇri aplikac´ı mohou vyuˇz´ıt dod´ avan´eho programov´eho rozhran´ı pro nahr´ an´ı audio z´ aznamu, kter´ y je n´aslednˇe automaticky zpracov´ an na vzd´ alen´ ych serverech spoleˇcnosti Pebble, jeˇz zpˇetnˇe poskytuj´ı jeho textov´ y pˇrepis v pˇredem nastaven´em jazyce. Vyuˇzili jsme t´eto funkce ke zprostˇredkov´ an´ı hlasov´eho zad´ av´ an´ı nov´ ych registrac´ı. Uˇzivatel nen´ı omezen na zad´ av´ an´ı jednoduch´ ych registrac´ı, ale m˚ uˇze specifikovat jej´ı dalˇs´ı parametry (napˇr. ˇcas registrace, typ inzulinu, pˇr´ıpadnˇe libovoln´ y koment´aˇr). Tento zp˚ usob pak m˚ uˇze b´ yt rychlejˇs´ı ve srovn´ an´ı s klasick´ ym zad´av´an´ım registrac´ı pomoc´ı aplikace v mobiln´ım telefonu. Rozpozn´av´an´ı ˇreˇci je poskytov´ ano v nˇekolika r˚ uzn´ ych jazyc´ıch (v souˇcasn´e dobˇe vˇsak nepodporuje ˇceˇstinu) a m˚ uˇze b´ yt nastaveno pomoc´ı aplikace na chytr´em telefonu.
Diskuze Na z´akladˇe v´ yˇse popsan´ ych poznatk˚ u vˇeˇr´ıme, ˇze nositeln´a zaˇr´ızen´ı maj´ı pomˇernˇe velk´ y potenti´al vyuˇzit´ı v r´amci self-managementu diabetu a jejich v´ yvoj vyb´ız´ı k dalˇs´ımu v´ yzkumu v t´eto oblasti. Kromˇe iterativn´ıho vylepˇsov´an´ı, kter´e je umoˇznˇeno d´ıky rapidn´ımu v´ yvoji v t´eto oblasti, je n´aˇs v´ yzkum tak´e ovlivnˇen v´ yvojem v oblasti Do-It-Yourself. Jak bylo zm´ınˇeno, komunitn´ı projekty ˇcasto poskytuj´ı velmi zaj´ımav´e moˇznosti vyuˇzit´ı zdravotnick´ ych pˇr´ıstroj˚ u, nicm´enˇe nesplˇ nuj´ı specifika komerˇcn´ıch, certifikovan´ ych produkt˚ u. V budoucnu pl´anujeme implementovat a vyhodnotit pouˇzitelnost vˇetˇsiny popsan´ ych funkcionalit a nad´ale publikovat v´ ysledky v´ yzkumu v t´eto oblasti.
Podˇ ekov´ an´ı Tento v´ yzkum byl podpoˇren projektem SVV 260 267 Karlovy univerzity a projektem OP VK: Mezin´arodn´ı spoˇ lupr´ace na Fakultˇe biomedic´ınsk´eho inˇzen´ yrstv´ı CVUT, reg. ˇc. projektu: CZ.1.07/2.3.00/20.0093.
Reference [1] Arsand E, Skrovseth SO, Joakimsen RM, Hartvigsen G. Design of an Advanced Mobile Diabetes Diary Based on a Prospective 6-month Study Involving People with Type 1 Diabetes. The 6th International Conference on Advanced Technologies and Treatments for Diabetes, February 27. - March 2. 2013, Paris. France. [2] Pebble, 2016, Available from: https://getpebble.com. CA. USA.
Obr´ azek 2: Postup pˇri hlasov´em zad´ av´ an´ı nov´e registrace inzulinu.
Postup pˇri hlasov´em zad´ av´ an´ı nov´e registrace inzulinu je ilustrov´an na Obr´ azku 2. Po pˇrepnut´ı do naslouchac´ıho m´odu (Obr´azek 2B), hlasov´em zad´ an´ı a potvrzen´ı je uˇzivateli na n´ asleduj´ıc´ı obrazovce zobrazen textov´ y pˇrepis (Obr´azek 2C). V tomto konkr´etn´ım pˇr´ıpadˇe uˇzivatel zadal aplikaci inzulinu, kter´ a jiˇz probˇehla pˇred dvaceti minutami. V´ ysledek operace je opˇet vidˇet na hlavn´ı obrazovce (Obr´ azek 2D).
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[3] Arsand E, Muzny M, Bradway M, Muzik J, Hartvigsen G. Performance of the First Combined Smartwatch to Smartphone Diabetes Diary Application Study, Journal of diabetes science and technology, 2015, 1932296814567708. [4] Muzny M, Bradway M, Muzik J, Hartvigsen G, Arsand E. Wearable Computing in Diabetes – Advanced Functions Enabled by Smartwatches. The 9th International Conference on Advanced Technologies and Treatments for Diabetes, February 2. - 6. 2016, Milano. Italy. [5] Nightscout, 2016. Available from: http://www.nightscout. info. [6] xDrip, 2016. Available from: http://stephenblackwasalreadytaken.github.io/xDrip/.
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Abstrakt
ˇ a v zahraniˇ Elektronick´ y zdravotn´ı z´ aznam v CR c´ı Anna Schlenker1,2 , Tom´ aˇs Hr˚ uza3 1 2
´ ˇ Ustav hygieny a epidemiologie 1. LF a VFN, 1. l´ekaˇrsk´ a fakulta, Univerzita Karlova, Praha, CR
ˇ e vysok´e uˇcen´ı technick´e v Praze, Kladno, CR ˇ Katedra biomedic´ınsk´e informatiky, Fakulta biomedic´ınsk´eho inˇzen´yrstv´ı, Cesk´ 3
ˇ e vysok´e uˇcen´ı technick´e v Praze, Kladno, CR ˇ Katedra biomedic´ınsk´e techniky, Fakulta biomedic´ınsk´eho inˇzen´yrstv´ı, Cesk´
Kontakt: IJBH 2016; 4(2):25–27 Anna Schlenker ´ Ustav hygieny a epidemiologie 1. LF a VFN, ˇ a republika Univerzita Karlova, Cesk´
zasl´ ano: 15. ˇ cervence 2016 pˇrijato: 15. srpna 2016 publikov´ ano: 20. z´ aˇr´ı 2016
Adresa: Studniˇ ckova 7, 128 00 Praha 2 E–mail: [email protected]
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prostˇredc´ıch (ZP). Evidence tˇechto prostˇredk˚ u je d˚ uleˇzit´a nejen z d˚ uvodu bezpeˇcnosti pacienta, ale tak´e pro evidenci V oblasti zdravotnictv´ı bylo v pr˚ ubˇehu let nahro- ˇcetnosti pouˇzit´ı konkr´etn´ıho zdravotn´ıho prostˇredku, ych ZP prostˇredk˚ u na dan´em madˇeno velk´e mnoˇzstv´ı dat. V dneˇsn´ı dobˇe je poˇr´ad popˇr´ıpadˇe vyuˇzit´ı jednotliv´ oddˇ e len´ ı. vˇetˇsina pacientsk´e dokumentace v pap´ırov´e podobˇe. Postupnˇe se vˇsak pˇrech´ az´ı k dokumentaci elektronick´e Tato data jsou v souˇcasn´e dobˇe obvykle zaznaa doch´az´ı tak i ke konverzi pap´ırov´e formy do elektronick´e men´av´ana na pap´ırov´ y arch, tzv. Ordinaˇcn´ı list denn´ı“. ” podoby. Zde se ovˇsem dost´ av´ ame ke zpracov´ av´ an´ı velk´eho Zde jsou kaˇzdou hodinu zaznamen´ av´any potˇrebn´e u ´daje objemu dat [1] z elektronick´ ych zdravotn´ıch z´aznam˚ u, o pacientovi. Po vyplnˇen´ı archu jsou data pˇrepisov´ana do kter´ ych neust´ale pˇrib´ yv´ a [2]. nemocniˇcn´ıho informaˇcn´ıho syst´emu (NIS). Tato ˇcinnost Zdravotnick´a dokumentace je systematick´ y z´aznam, je ˇcasovˇe n´aroˇcn´a a m˚ uˇze b´ yt zat´ıˇzen´a nemalou chybou. kter´ y obsahuje informace k jednotliv´emu pacientovi od Jedn´a se napˇr´ıklad o v´ yskyt neˇciteln´ ych znak˚ u nebo zkrapoˇc´atku l´eˇcby po jej´ı ukonˇcen´ı. Zdravotnick´ a dokumen- tek (kter´e pouˇz´ıv´a kaˇzd´e pracoviˇstˇe jin´e) nebo o pˇreklepy tace se vede a uchov´ av´ a pro kaˇzd´eho pacienta samostatnˇe. vznikl´e pˇri pˇrepisu z´aznamu do NIS. Informuje o zdravotn´ım stavu a intervenc´ıch proveden´ ych u pacienta. N´asledn´a anal´ yza nasb´ıran´ ych dat m˚ uˇze tak´e zlepˇsit Velk´ a Brit´ anie kvalitu zdravotn´ı p´eˇce. M˚ uˇze pomoct pˇri podpoˇre rozhodov´an´ı, pˇri anal´ yze rizik ˇci pˇri n´ akladov´e anal´ yze ve zdraVelk´a Brit´anie m´a jeden z nejlepˇs´ıch n´arodn´ıch zdravotnictv´ı. Dalˇs´ı moˇznost´ı je pouˇzit´ı pˇri v´ yvoji l´ekaˇrsk´ ych u NHS Choices (www.nhs.uk). Tento doporuˇcen´ı [2]. Statistick´ a anal´ yza m˚ uˇze tak´e pomoct votn´ıch port´al˚ um moˇznost pˇres jednu webovou s lepˇs´ı interpretac´ı dat shrom´ aˇzdˇen´ ych v nemocniˇcn´ıch in- port´al poskytuje pacient˚ str´anku naj´ıt veˇsker´e potˇrebn´e informace z oblasti zdrav´ı formaˇcn´ıch syst´emech [3]. a soci´aln´ı p´eˇce, porovnat jednotliv´e l´ekaˇre ˇci zaˇr´ızen´ı a vybrat pro sebe to nejvhodnˇejˇs´ı a poskytuj´ı tak´e moˇznost ˇ Srovn´ an´ı e-health syst´ em˚ u v CR rychlejˇs´ı a efektivnˇejˇs´ı on-line komunikace.
a v zahraniˇ c´ı ˇ Cesk´ a republika ˇ e republice jsou informace, kter´e mus´ı obsaV Cesk´ hovat zdravotnick´ a dokumentace, specifikov´ any v legislativˇe. K tˇemto povinn´ ym u ´daj˚ um jsou pak pˇrid´av´any dalˇs´ı v z´avislosti na dan´em zdravotnick´em zaˇr´ızen´ı ˇci konkr´etn´ım oddˇelen´ı. Kromˇe informac´ı o pacientovi a jeho zdravotn´ım stavu jsou v dokumentaci tak´e informace o absolvovan´ ych vyˇsetˇren´ıch a pouˇzit´ ych zdravotnick´ ych c
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ˇ edsko Sv´ ˇ edsku je (stejnˇe jako v CR) ˇ Ve Sv´ nˇekolik r˚ uzn´ ych nemocniˇcn´ıch informaˇcn´ıch syst´em˚ u, kter´e nejsou vz´ajemnˇe propojen´e. Existuje vˇsak moˇznost objednat se k l´ekaˇri nebo poˇz´adat o radu na d´alku pˇres webovou str´anku V˚ ardguiden (The health care guide) (www.1177.se), kter´a shromaˇzd’uje vˇsechny potˇrebn´e informace na jednom m´ıstˇe. IJBH – Volume 4 (2016), Issue 2
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D´ ansko V D´ansku velmi dobˇre funguje eHealth port´ al Sundˇ edsku hed (www.sundhed.dk), kter´ y obdobnˇe jako ve Sv´ poskytuje pacient˚ um moˇznost vyhledat potˇrebn´e informace na jednom m´ıstˇe a prohl´ıˇzet zdravotnickou dokumentaci ˇci v´ ysledky laboratorn´ıch vyˇsetˇren´ı.
Finsko ˇ Ve Finsku je (podobnˇe jako v CR) v´ yvoj zamˇeˇren na rozliˇsen´ı pr´av v nemocniˇcn´ım informaˇcn´ım syst´emu pro l´ekaˇre, zdravotn´ı sestry a pacienty. Nav´ıc je pak zamˇeˇren´ı na rozd´ıln´ y vzhled NIS. L´ekaˇri obvykle upˇrednostˇ nuj´ı holistick´ y pohled, kter´ y jim dok´ aˇze nast´ınit celkov´ y stav pacienta. Z pohledu zdravotn´ı sestry je pak NIS celkovˇe jednoduˇsˇs´ı, protoˇze nepotˇrebuje zn´ at tolik informac´ı o paciˇ zcela chyb´ı. entovi. A nakonec n´ahled pacienta, kter´ y v CR Pacienta zaj´ım´a jen srozumiteln´e pod´ an´ı informac´ı o jeho zdravotn´ım stavu popˇr´ıpadˇe medikaci ˇci pl´ anovan´ ych n´avˇstˇev´ach zdravotnick´eho zaˇr´ızen´ı. V ide´ aln´ım pˇr´ıpadˇe by se mˇelo jednat o jeden z´ aznam, kter´ y by byl interpretov´an kaˇzd´emu z u ´ˇcastn´ık˚ u zdravotnick´eho procesu individu´alnˇe, coˇz je i snaha Finsk´eho zdravotnictv´ı [4]. ˇ edsko Finsko disponuje (stejnˇe jako jiˇz zm´ınˇen´e Sv´ a D´ansko) webovou str´ ankou/port´ alem (www.kanta.fi), kde se daj´ı naj´ıt vˇsechny potˇrebn´e informace na jednom m´ıstˇe. Pacienti si zde m˚ uˇzou tak´e prohl´ıˇzet svou zdravotnickou dokumentaci a pracovat s elektronick´ ym receptem.
Nˇ emecko ˇ V Nˇemecku je (na rozd´ıl od CR) snaha o zlepˇsen´ı elektronick´eho zdravotnick´eho z´ aznamu a jeho pˇred´ av´ an´ı mezi jednotliv´ ymi zdravotnick´ ymi zaˇr´ızen´ımi. Proto mezi lety 2011 a 2016 probˇehl program, kter´ y mˇel za u ´kol pouk´azat na nedostatky a moˇzn´ a vylepˇsen´ı. V prvn´ı f´ azi (20112012) ˇslo o sbˇer potˇrebn´ ych dat, ve druh´e f´ azi (do roku 2014) ˇslo o zamˇeˇren´ı na klinick´e procesy a v posledn´ı tˇret´ı f´azi (do roku 2016) zhodnocen´ı v´ ysledk˚ u. Z tohoto popudu vznikla zpr´ava EHR-2020, kter´ a nastavuje c´ıle, na jak´e by se mˇelo zdravotnictv´ı zamˇeˇrit. Hlavn´ım bodem je urychlit pˇrenos dat mezi jednotliv´ ymi oddˇelen´ımi a zaˇr´ızen´ımi. Pak zajiˇstˇen´ı transparentnosti pˇri veˇrejn´ ych zak´azk´ach. Nem´enˇe d˚ uleˇzit´e je i objasnˇen´ı a zjednoduˇsen´ı postup˚ u certifikace a smyslupln´e vyuˇzit´ı pˇredpis˚ u. D´ale pak zlepˇsen´ı v´ ymˇeny dat, zv´ yˇsen´ı interoperability jednotliv´ ych syst´em˚ u, z ˇcehoˇz plyne i sn´ıˇzen´ı potˇreby opˇetovn´eho zad´av´an´ı dat [5].
Nizozem´ı ˇ ale stejnˇe jako napˇr. V Nizozem´ı je (na rozd´ıl od CR, v Nˇemecku) snaha o sjednocen´ı zdravotnick´e dokumentace, aby mohla b´ yt jednoduˇse pˇrenositeln´ a data o pacientovi z jedn´e nemocnice do druh´e. Elektronick´ a zdravotnick´a dokumentace je zde na vzestupu, a to hlavnˇe IJBH – Volume 4 (2016), Issue 2
u mlad´e generace, kter´a je dennˇe ve styku s informaˇcn´ımi technologiemi. Je zde kladen tak´e d˚ uraz na bezpeˇcnostn´ı rizika, zejm´ena na lidsk´ y faktor. Zapojen´ı informaˇcn´ıch syst´em˚ u do zdravotn´ı p´eˇce, pˇredevˇs´ım t´e prim´arn´ı, vede k nutnosti ovˇeˇren´ı d˚ uvˇeryhodnosti a kvality poskytovan´ ych informac´ı na webov´ ych str´ank´ach. To vedlo k zaveden´ı syst´emu Health-on-the-Net“, kter´ y m´a za u ´kol po” soudit spolehlivost l´ekaˇrsk´ ych webov´ ych str´anek [6]. V Nizozem´ı nav´ıc existuje port´al vˇenuj´ıc´ı se elektronick´emu zdravotnictv´ı a standardizaci (www.nictiz.nl). ˇ edsku a ve Tento port´al (podobnˇe jako napˇr´ıklad ve Sv´ Finsku) poskytuje uˇzivatel˚ um pˇr´ıstup k informac´ım na jednom m´ıstˇe.
Slovensk´ a republika Na Slovensku je tak´e snaha o vytvoˇren´ı funguj´ıc´ıho n´arodn´ıho eHealth port´alu (www.ezdravotnictvo.sk), kter´ y bude kromˇe poskytov´an´ı z´akladn´ıch informac´ı disponovat i aplikacemi jako napˇr´ıklad elektronick´a zdravotn´ı kn´ıˇzka, ePreskripce, eAlokace, a bude podporovat tak´e integraci informaˇcn´ıch syst´em˚ u. Do sv´e elektronick´e zdravotn´ı kn´ıˇzky se pacient dostane pomoc´ı sv´eho eID nebo pomoc´ı elektronick´eho zdravotn´ıho pr˚ ukazu povinn´eho od roku 2017.
Mad’arsko V Mad’arsku zaˇcal v roce 2014 v´ yvoj n´arodn´ıho zdravotnick´eho syst´emu. V roce 2016 by mˇel b´ yt testov´an a do roku 2018 plnˇe spuˇstˇen. Jedn´a se o syst´em propojuj´ıc´ı jednotliv´e informaˇcn´ı syst´emy d´ıky cloudu, kter´ y je vˇsechny pˇrekryje, a zachov´a tak jejich m´ıstn´ı vzhled.
Rumunsko V Rumunsku zaˇcala zdravotn´ı p´eˇce podporovan´a elektronick´ ymi procesy a komunikac´ı spolu s rozvojem integrovan´eho informaˇcn´ıho syst´emu (Sistem Informatic Unic Integrat – SIUI) v roce 2002. Schv´alen byl v roce 2008 a zah´ajen v roce 2010. Jako souˇc´ast integrovan´eho informaˇcn´ıho syst´emu, byl v letech 2008-2010 vyvinut eHealth strategick´ y pl´an ministerstva zdravotnictv´ı, elektronick´ y recept v roce 2012 a n´arodn´ı pr˚ ukaz zdravotn´ıho pojiˇstˇen´ı v roce 2013 (povinn´ y od roku 2014).
Z´ avˇ er ˇ e republice je vidˇet znaˇcn´e zpoˇzdˇen´ı ve v´ V Cesk´ yvoji a modernizaci elektronick´eho zdravotnictv´ı v porovn´an´ı s jin´ ymi st´aty Evropy. Vzhledem k dobˇre funguj´ıc´ım syst´em˚ um e-governmentu se vˇsak urˇcitˇe nejedn´a o to, ˇze ˇ a republika nebyla schopna elektronizace nˇejak´eho by Cesk´ syst´emu, nebo ˇze by o tuto inovaci uˇzivatel´e nemˇeli z´ajem, ˇci ji nepouˇz´ıvali. Zde se tedy mus´ıme pt´at, proˇc to v oblasti zdravotnictv´ı nejde: je ˇcesk´ y pacient natolik jin´ y neˇz pacient jinde v Evropˇe? Nebo je snad ˇcesk´ y l´ekaˇr jin´ y neˇz c
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l´ekaˇr jinde v Evropˇe? A nebo proˇc je legislativa v oblasti zdravotnictv´ı natolik striktn´ı, ˇze nˇekter´e vˇeci nedovoluje, ˇci dokonce zakazuje, a t´ım tlum´ı rozvoj elektronizace zdravotnictv´ı, kter´a by nepochybnˇe mohla zlepˇsit, zpˇr´ıstupnit a urˇcitˇe i zlevnit zdravotn´ı p´eˇci?
Kl´ıˇ cov´ a slova Elektronick´a zdravotnick´ a dokumentace; eHealth
Podˇ ekov´ an´ı Tato pr´ace byla podpoˇrena projektem SVV-2016260267 Univerzity Karlovy v Praze a grantem Studentsk´e ˇ grantov´e soutˇeˇze CVUT ˇc. SGS16/117/OHK4/1T/17.
c
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Reference [1] Schlenker A., Bohunˇ c´ ak A.: Keystroke Dynamics for Security Enhancement in Hospital Information Systems. International Journal on Biomedicine and Healthcare 2015; 3(1):41–44. [2] Raghupathi W., Raghupathi V.: Big data analytics in healthcare: promise and potential. Health Information Science and Systems 2014, 2(3). doi:10.1186/2047-2501-2-3 [3] Kalina J.: Statistical Challenges of Big Data Analysis in Medicine. International Journal on Biomedicine and Healthcare 2015; 3(1):24–27. [4] Nyk¨ anen P.: Health Care Documentation - Could we Integrate Medical Doctors and Nurses Documentation? International Journal on Biomedicine and Healthcare 2016;4(1): 50–51. [5] Blobel B.: EHR/PHR Systems Today and in the Future International Journal on Biomedicine and Healthcare 2016;4(1):9–16. [6] van Bemmel J.H.: Developments in Medical Informatics - Can the Future be Predicted from the Past? International Journal on Biomedicine and Healthcare 2016;4(1):3–8.
IJBH – Volume 4 (2016), Issue 2
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Kr´ atk´ y p˚ uvodn´ı ˇ cl´ anek
Coancestry koeficient Dalibor Slov´ ak1,2 , Jana Zv´ arov´ a2 1
´ ˇ a republika Ustav zdravotnick´ych informac´ı a statistiky, Praha, Cesk´ 2
´ ˇ a republika Ustav hygieny a epidemiologie 1. LF UK, Praha, Cesk´
Kontakt: Dalibor Slov´ ak ´ Ustav zdravotnick´ ych informac´ı a statistiky Adresa: Palack´ eho n´ amˇ est´ı 4, 128 01 Praha 2
IJBH 2016; 4(2):28–30 zasl´ ano: 15. ˇ cervence 2016 pˇrijato: 15. srpna 2016 publikov´ ano: 20. z´ aˇr´ı 2016
E–mail: [email protected]
´ Uvod Kriminalistika ve sv´e pr´ aci pouˇz´ıv´ a metody z mnoha vˇedeck´ ych obor˚ u. Obecnˇe zn´ am´ ym pˇr´ınosem rozvoje vˇedy v posledn´ıch desetilet´ıch je vyuˇzit´ı genetick´eho materi´alu k identifikaci osob, zejm´ena pˇri urˇcov´ an´ı otcovstv´ı nebo hled´an´ı pachatele zloˇcinu. Proces identifikace v sobˇe zahrnuje nejen postupy a znalosti z nejr˚ uznˇejˇs´ıch obor˚ u chemie a biologie, ale tak´e matematick´e vyhodnocen´ı s´ıly z´ıskan´ ych d˚ ukaz˚ u. V souladu s myˇslenkou, ˇze je lepˇs´ı osvobodit pachatele neˇz odsoudit nevinn´eho, se postupuje velmi konzervativnˇe, tedy se snahou co nejpeˇclivˇeji zapoˇc´ıtat vˇse, co by mohlo hovoˇrit ve prospˇech obvinˇen´eho. Proces stanoven´ı v´ ahy z´ıskan´e evidence by mˇel zahrnout parametry pˇrich´azej´ıc´ı z nˇekolika smˇer˚ u. V´ ysledek je ovlivnˇen kvalitou policejn´ı pr´ ace – dod´ an´ım vˇsech relevantn´ıch okolnost´ı a korektn´ım odbˇerem i uchov´ av´an´ım biologick´ ych vzork˚ u. Druh´ ym potenci´ aln´ım m´ıstem vzniku chyb a nejistoty je laboratorn´ı zpracov´ an´ı, kde ani pˇri nejlepˇs´ı v˚ uli nelze vylouˇcit stochastick´e jevy jako dropout ˇci drop-in. Tˇret´ı skupina parametr˚ u, kter´e ovlivˇ nuj´ı v´ ypoˇcet, vych´az´ı z vlastnost´ı populace, do n´ıˇz spad´a vyˇsetˇrovan´a osoba. Je nutn´e zn´ at napˇr. velikost populace, v n´ıˇz hled´ame pachatele, nebo populaˇcn´ı frekvence alel na lokusech pouˇz´ıvan´ ych ve forenzn´ı praxi. Pr´avˇe do skupiny parametr˚ u vztahuj´ıc´ıch se k populaci spad´a tak´e informace o populaˇcn´ı struktuˇre. Jestliˇze populace nen´ı homogenn´ı, ale jsou v n´ı rozeznateln´e urˇcit´e subpopulace (vymezen´e rasovˇe, geograficky, n´aboˇzensky atd.), uvnitˇr kter´ ych doch´ az´ı ke sˇ natk˚ um a rozmnoˇzov´an´ı ve vˇetˇs´ı m´ıˇre neˇz mezi jednotliv´ ymi subpopulacemi, pak takov´ ato informace v´ yraznˇe ovlivˇ nuje pˇredpokl´adan´e rozloˇzen´ı jednotliv´ ych alel v r´ amci cel´e populace. I kdyˇz osoby uvnitˇr jedn´e subpopulace nemus´ı b´ yt ve zn´am´em pˇr´ıbuzensk´em vztahu, d´ıky spoleˇcn´emu v´ yvoji pravdˇepodobnˇe sd´ılej´ı vyˇsˇs´ı mnoˇzstv´ı alel neˇz jedinci z r˚ uzn´ ych subpopulac´ı. V subpopulaci se totiˇz mohou celkovˇe vz´ acn´e alely vyskytovat s pomˇernˇe vysokou frekvenc´ı, nˇekter´e mohou naopak zcela chybˇet, coˇz mj. zvyˇsuje v´ yskyt homozygotn´ıch IJBH – Volume 4 (2016), Issue 2
genotyp˚ u. M´ıra t´eto pˇr´ıbuznosti je obvykle vyj´adˇrena pomoc´ı coancestry koeficientu θ. Pro zahrnut´ı coancestry koeficientu do v´ ypoˇctu s´ıly evidence se pouˇz´ıv´a vzorec odvozen´ y v ˇcl´anku Baldinga a Nicholse [3]. Ignorov´an´ı vlivu populaˇcn´ı struktury svˇedˇc´ı v neprospˇech podezˇrel´eho, a proto v souladu s konzervativn´ım pˇr´ıstupem je tento parametr povaˇzov´an za d˚ uleˇzit´ y a Baldingova-Nicholsova formule pro v´ ypoˇcet pravdˇepodobnosti pozorov´an´ı homozygota, resp. heterozygota v subpopulaci je pˇredmˇetem podrobn´eho v´ yzkumu.
Coancestry koeficient Historie P˚ uvod coancestry koeficientu lze vystopovat aˇz do roku 1921, kdy Sewall Wright definoval koeficient f jako m´ıru korelace mezi homologn´ımi geny pˇri dan´em typu p´arov´an´ı jedinc˚ u [9]. O rok pozdˇeji pojmenoval f jako koeficient inbreedingu [10]. Posl´eze zaˇcal stejn´ y autor pouˇz´ıvat velk´eho p´ısmene F (resp. FST ) [11] a pojmenov´an´ı F-statistika je v nˇekter´ ych oborech dodnes dominantn´ı. Ve forenzn´ıch vˇed´ach vˇsak postupem ˇcasu pˇrevl´adlo pojmenov´an´ı coancestry koeficient a znaˇcen´ı θ. Tento term´ın poch´az´ı z poˇc´atku 70. let, kdy jej CavalliSforza a Bodmer [4] zaˇcali pouˇz´ıvat jako m´ıru genetick´e vzd´alenosti.
Definice Pouˇz´ıv´an´ı uveden´ ych term´ın˚ u v r˚ uzn´ ych oborech i jejich odliˇsn´e znaˇcen´ı vedly i pˇres jejich postupnou vz´ajemnou konvergenci k cel´e ˇradˇe odliˇsn´ ych definic. Napˇr. autoˇri Baldingovy-Nicholsovy formule ch´apou coancestry koeficient jako m´ıru korelace mezi dvˇema geny vybran´ ymi od r˚ uzn´ ych osob ze subpopulace [2]. Jako nejrozˇs´ıˇrenˇejˇs´ı a nejpˇresnˇejˇs´ı se n´am vˇsak jev´ı definice c
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pouˇz´ıvan´a v [5] a coancestry koeficient θ budeme d´ale ch´apat v n´asleduj´ıc´ım smyslu: Coancestry koeficient ud´ av´ a pravdˇepodobnost, ˇze dvˇe n´ ahodnˇe vybran´e alely ze subpopulace budou ibd (identical by descent, spoleˇcn´eho p˚ uvodu). Alely jsou oznaˇceny jako ibd, kdyˇz jsou kopi´ı nˇejak´e alely u spoleˇcn´eho pˇredka (pokud se nach´ azej´ı u dvou r˚ uzn´ych osob) nebo kdyˇz jsou kopi´ı nˇejak´e alely u spoleˇcn´eho pˇredka rodiˇc˚ u (pokud se nach´ azej´ı u jedn´e osoby). Lze algebraicky dok´ azat, ˇze pravdˇepodobnost nalezen´ı dvou identick´ ych alel (tj. alel se stejnou posloupnost´ı nukleotid˚ u), z nichˇz kaˇzd´ a je vybr´ ana od jin´eho jedince, je shodn´ a s pravdˇepodobnost´ı nalezen´ı homozygotn´ıho jedince v populaci [6]. Nez´ aleˇz´ı tedy na tom, kterou z moˇznost´ı v definici ibd alel uvaˇzujeme. Je zˇrejm´e, ˇze pokud vyb´ır´ ame alely ze subpopulace, pak v´ ysledek tohoto v´ ybˇeru je ovlivnˇen poˇctem existuj´ıc´ıch alel, jejich frekvenc´ı apod. Z uveden´e definice tedy plyne, ˇze hodnota θ se m˚ uˇze liˇsit jak mezi jednotliv´ ymi subpopulacemi t´eˇze populace, tak i mezi jednotliv´ ymi lokusy u t´eˇze subpopulace. V souladu s t´ım se p˚ uvodn´ı vn´ım´an´ı coancestry koeficientu souhrnnˇe pro celou populaci a pozdˇeji pro jednotliv´e subpopulace posunulo ke stanoven´ı hodnot θ zvl´ aˇst’ pro jednotliv´e lokusy. Nˇekdy je vˇsak z lokusov´ ych hodnot θ vypoˇc´ıt´ an pr˚ umˇer a tento v´ ysledek je pot´e pouˇz´ıv´ an pro celou subpopulaci napˇr´ıˇc jednotliv´ ymi lokusy. V posledn´ı dobˇe se objevuj´ı snahy stanovit coancestry koeficient zvl´ aˇst’ pro kaˇzdou dvojici osob, respektive pro kaˇzd´eho jedince (podle toho, zda v dan´em kontextu potˇrebujeme uvaˇzovat dvojici alel u jedn´e osoby nebo alely od dvou r˚ uzn´ ych osob). Napˇr. Zheng a Weir tak ve sv´em ˇcl´anku [12] pracuj´ı dokonce se tˇremi coancestry koeficienty: Fij pro stejnou osobu, θi pro r˚ uzn´e osoby ze stejn´e subpopulace a θij pro osoby z r˚ uzn´ ych subpopulac´ı. Implicitnˇe vˇsak pˇredpokl´ adaj´ı hodnoty θi a θij stejn´e na kaˇzd´em lokusu, tedy prov´ ad´ı pr´ avˇe ono pr˚ umˇerov´an´ı hodnot z´ıskan´ ych na jednotliv´ ych lokusech.
Odhad Taylor et al. [8] pˇredpokl´ adaj´ı, ˇze coancestry koeficient je n´ahodn´ a veliˇcina a jeho hodnota nemus´ı b´ yt pro kaˇzdou dvojici osob stejn´ a. Hledaj´ı proto zp˚ usob, jak jeho rozloˇzen´ı v subpopulaci popsat vhodn´ ym pravdˇepodobnostn´ım rozdˇelen´ım. Toto rozdˇelen´ı mus´ı b´ yt asymetrick´e, pozitivnˇe ˇsikm´e a nab´ yvat hodnot od nuly do jedn´e (dominantnˇe vˇsak pouze v lev´e ˇc´asti tohoto intervalu), kter´eˇzto poˇzadavky splˇ nuje rodina beta rozdˇelen´ı. Pomoc´ı simulace pak byly vybr´ any konkr´etn´ı hodnoty parametr˚ u beta rozdˇelen´ı a na jejich z´akladˇe vypoˇctena v´ ysledn´ a hodnota θ. Celkovˇe autoˇri doporuˇcuj´ı jako n´ızkou hodnotu θ = 1 % a jako vysokou hodnotu θ = 5 %. Ve sv´e studii australsk´e populace Ayres et al. [1] pro kaˇzdou subpopulaci a kaˇzd´ y lokus poˇc´ıtaj´ı coancestry koeficient pomoc´ı bayesovsk´e metody odvozen´e v ˇcl´anku [2]. Zastoupen´ı jednotliv´ ych alel v subpopulaci modeluj´ı poc
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moc´ı Dirichletova rozdˇelen´ı s parametry λpiP , kde λ = 1−θ θ . Pravdˇepodobnost pozorov´an´ı mi alel Ai ( i mi = n) je potom d´ana J Γ (λ) Y Γ (λpi + mi ) P(m1 , . . . , mJ ) = . Γ (λ + n) i=1 Γ (λpi )
V´ ysledky popsan´e v [1] odpov´ıdaj´ı oˇcek´av´an´ım: pro pˇrevaˇzuj´ıc´ı bˇeloˇsskou populaci jsou hodnoty θ n´ızk´e, naopak pro izolovan´e skupiny domorod´ ych Aborigini˚ u jsou bˇeˇznˇejˇs´ı vyˇsˇs´ı hodnoty θ, st´ale vˇsak pˇribliˇznˇe do 5 %. Autoˇri [6] ukazuj´ı, ˇze k odhadu parametru θ lze vyuˇz´ıt tak´e pravdˇepodobnost pozorov´an´ı homozygotn´ıho jedince. Oznaˇc´ıme-li tuto pravdˇepodobnost H a populaˇcn´ı frekvence pi , pak X H = θ + (1 − θ) p2i , kde sˇc´ıt´ame pˇres vˇsechny alely na uvaˇzovan´em lokusu. Odtud lze coancestry koeficient vyj´adˇrit jako P H − p2i P 2. θ= 1 − pi Protoˇze s klesaj´ıc´ı velikost´ı populace roste pravdˇepodobnost v´ yskytu homozygotn´ıch jedinc˚ u, je pravdˇepodobnost H pro kaˇzdou subpopulaci obvykle vyˇsˇs´ı, neˇz by napov´ıdaly alelick´e frekvence platn´e pro celou populaci. Vid´ıme tak´e, ˇze θ je nulov´e pr´avˇe tehdy, kdyˇz H je rovno souˇctu druh´ ych mocnin alelick´ ych frekvenc´ı, tedy pokud pˇredpokl´ad´ame, ˇze alelick´e frekvence v subpopulaci jsou stejn´e jako v cel´e populaci.
Pouˇ zit´ı Jak jsme jiˇz naznaˇcili, coancestry koeficient vstupuje do v´ ypoˇctu skrze Baldingovu-Nicholsovu formuli [3]. Jedn´ım z jej´ıch d˚ usledk˚ u je, ˇze u vz´acn´ ych genotyp˚ u m˚ uˇze hodnota θ v pomˇeru k alelick´ ym frekvenc´ım nab´ yvat relativnˇe vysok´ ych hodnot. S klesaj´ıc´ı velikost´ı zkouman´e subpopulace proto roste d˚ uleˇzitost spr´avn´eho v´ ybˇeru rozdˇelen´ı θ [8]. Rohlfs et al. [7] pod´avaj´ı pˇrehled ˇcl´ank˚ u, kter´e se zab´ yvaj´ı srovn´an´ım teoretick´ ych v´ ysledk˚ u pˇredpov´ıdan´ ych Baldingovou-Nicholsovou formul´ı a skuteˇcnˇe pozorovan´ ych frekvenc´ı. Aˇckoli zahrnut´ı populaˇcn´ı struktury ˇcin´ı test konzervativn´ım, nˇekter´e v´ ysledky naznaˇcuj´ı, ˇze t´eto konzervativnosti m˚ uˇze b´ yt aˇz pˇr´ıliˇs. Pozorovan´e hodnoty totiˇz ˇcasto leˇz´ı ponˇekud bl´ıˇze alelick´ ym frekvenc´ım pˇredpokl´adan´ ym pˇri nezahrnut´ı vlivu subpopulace, neˇz by napov´ıdala korekce vypoˇcten´a pomoc´ı Baldingova-Nicholsova vzorce. Rohlfs et al. [7] se pokouˇsej´ı tento rozd´ıl vyrovnat t´ım, ˇze do v´ ypoˇctu v menˇs´ı m´ıˇre zahrnuj´ı tak´e sd´ılen´ı alel mezi jedinci z r˚ uzn´ ych subpopulac´ı. Dle naˇseho n´azoru by vˇsak takov´a u ´prava nemˇela do v´ ypoˇctu v˚ ubec vstupovat a pozorovan´ y rozd´ıl je zˇrejmˇe d˚ usledkem nespr´avn´e konstrukce samotn´e Baldingovy-Nicholsovy formule. IJBH – Volume 4 (2016), Issue 2
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Slov´ak D., Zv´arov´a J.– Coancestry koeficient
Z´ avˇ er Zp˚ usob zahrnut´ı populaˇcn´ı struktury do v´ ypoˇctu s´ıly evidence proti podezˇrel´e osobˇe je st´ ale pˇredmˇetem zkoum´an´ı. Samotn´ y pojem coancestry koeficientu i po t´emˇeˇr stolet´ı st´ale jeˇstˇe proch´ az´ı v´ yvojem: postupnˇe se vyjasˇ nuje jeho ch´ap´an´ı a hled´ a nejvhodnˇejˇs´ı zp˚ usob jeho stanoven´ı. Pro sjednocen´ı je dle naˇseho n´ azoru potˇreba, aby do diskuze vstupovaly teoretick´e v´ ysledky z obor˚ u genetiky a matematiky, simulace jednotliv´ ych proces˚ u i dopady pouˇz´ıv´an´ı nov´ ych poznatk˚ u v praxi.
[3] Balding DJ, Nichols RA. DNA profile match probability calculation: how to allow for population stratification, relatedness, database selection and single bands. Forensic Science International (1994); 64: 125–140 [4] Cavalli-Sforza LL, Bodmer WF. The genetics of human populations. W.H.Freeman and Company, San Francisco (1971) [5] Evett IW, Weir BS. Interpreting DNA evidence. Sinauer (1998) [6] Pinto N, Gusm˜ ao, L, Silva PV, Amorim A. Estimating coancestry from genotypes using a linear regression method. Forensic Science International: Genetics Supplement (2011); 3: e373–e374
Podˇ ekov´ an´ı
[7] Rohlfs RV, Aguiar VRC, Lohmueller KE, Castro AM, Ferreira ACS, Almeida VCO, Louro ID, Nielsen R. Fitting the BaldingNichols model to forensic databases. Forensic Science International: Genetics (2014); 11: 56–63
Tato pr´ace byla podpoˇrena grantem Univerzity Karlovy ˇc. SVV-2016-260267.
[8] Taylor D, Bright J-A, Buckleton J, Curran J. An illustration of the effect of various sources of uncertainty on DNA likelihood ratio calculations. Forensic Science International: Genetics (2015); 19: 86–91 [9] Wright S. Systems of mating. Genetics (1921); 6: 111–178
Reference
[10] Wright S. Coefficients of inbreeding and relationship. The American Naturalist (1922); 56: 330–338
[1] Ayres KL, Chaseling J, Balding DJ. Implications for DNA identification arising from an analysis of Australian forensic databases. Forensic Science International (2002); 129: 90–98
[11] Wright S. The interpretation of population structure by Fstatistics with special regard to systems of mating. Evolution (1965); 19(3): 395–420
[2] Balding DJ, Greenhalgh M, Nichols RA. Population genetics of STR loci in Caucasians. International Journal of Legal Medicine (1996); 108: 300–305
[12] Zheng X, Weir BS. Eigenanalysis of SNP data with an identity by descent interpretation. Theoretical Population Biology (2016); 107: 65–76
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P˚ uvodn´ı ˇ cl´ anek
Identifikace kuˇr´ ak˚ u v nestrukturovan´ e l´ ekaˇrsk´ e dokumentaci Michaela Stonov´ a1 1
ˇ a republika 1. l´ekaˇrsk´ a fakulta Univerzity Karlovy, Praha, Cesk´
Abstrakt Syst´em pro automatizovan´e stanovov´an´ı diagn´oz na z´akladˇe vlastnost´ı pˇrirozen´eho jazyka (Natural Language Processing) je koneˇcn´ym v´ysledkem anal´yzy nestrukturovan´ych dat. Pˇri jeho v´yvoji byl vytvoˇren vedlejˇs´ı produkt – syst´em pro identifikaci kuˇr´ak˚ u v nestrukturovan´e l´ekaˇrsk´e dokumentaci. Standardn´ı modely klasifikace dat jsou zaloˇzeny na pˇresn´e shodˇe vyhled´avan´ych v´yraz˚ u. Kontakt: Michaela Stonov´ a ˇ a republika 1. l´ ekaˇrsk´ a fakulta Univerzity Karlovy, Praha, Cesk´ Adresa: Kateˇrinsk´ a 32, 121 08 Praha2 E–mail: [email protected]
C´ıle v´ yzkumu Kouˇren´ı poˇskozuje t´emˇeˇr vˇsechny lidsk´e org´any, je pˇr´ıˇcinou mnoha onemocnˇen´ı a celkovˇe sniˇzuje kvalitu ˇzivota kuˇr´ak˚ u. Existuje jen velmi omezen´a skupina pˇr´ıpad˚ u, kdy kouˇren´ı m˚ uˇze b´ yt prospˇeˇsn´e (napˇr. pˇri ulcer´ozn´ı kolitidˇe [1, 3]). Evidence Based Medicine“ [4] ” v´ yzkum by mohl b´ yt prov´ adˇen s vˇetˇs´ı efektivitou, pokud by existoval robustn´ı syst´em pro automatizovanou identifikaci kuˇr´ak˚ u. Elektronick´a l´ekaˇrsk´ a dokumentace (ELD) [6] se obvykle zapisuje volnou formou [7], a to v nestrukturovan´e podobˇe. Standardn´ı klasifikaˇcn´ı modely jsou zaloˇzeny na pˇresn´e shodˇe vyhled´ avan´ ych v´ yraz˚ u. Naˇs´ım c´ılem je nalezen´ı nov´e, pˇresnˇejˇs´ı metody a jej´ı n´ asledn´e porovn´an´ı s jiˇz existuj´ıc´ımi postupy.
N´aˇs model pro klasifikaci kuˇr´ak˚ u pracuje s kontextu´aln´ı anal´yzou l´ekaˇrsk´e dokumentace. Navrˇzen´y algoritmus byl otestov´an na 386 587, volnou formou zapsan´ych l´ekaˇrsk´ych z´aznamech, s chybovost´ı 1,25 %.
Kl´ıˇ cov´ a slova Elektronick´a l´ekaˇrsk´a dokumentace, Kontextu´aln´ı anal´yza, Natural Language Processing, Nestrukturovan´a data, Pravidla textov´e anal´yzy IJBH 2016; 4(2):31–34 zasl´ ano: 15. ˇ cervence 2016 pˇrijato: 15. srpna 2016 publikov´ ano: 20. z´ aˇr´ı 2016
Jednoduch´a statistick´a anal´ yza byla otestov´ana na 386 587 anonymizovan´ ych1 l´ekaˇrsk´ ych z´aznamech z ne´ redn´ı vomocniˇcn´ıho informaˇcn´ıho syst´emu (NIS) Ustˇ ´ jensk´e nemocnice (UVN) v Praze [12]. Z celkov´eho poˇctu 314 202 jedineˇcn´ ych z´aznam˚ u bylo oznaˇceno jako n´aleˇzej´ıc´ı kuˇr´aku 13 282.
Navrˇ zen´ y model
Voln´a forma ELD umoˇzn ˇuje l´ekaˇri t´emˇeˇr neomezenou moˇznost, jak popsat pacient˚ uv zdravotn´ı stav. Form´at ELD se tak neliˇs´ı jen v r´amci nemocnic a r˚ uzn´ ych l´ekaˇr˚ u, ale ˇcasto i ELD jednoho konkr´etn´ıho l´ekaˇre m˚ uˇze vykazovat znaˇcnou fluktuaci. Na z´akladˇe aktu´aln´ıho l´ekaˇrova rozpoloˇzen´ı tak m˚ uˇze b´ yt nekouˇr´ıc´ı pacient jednou oznaˇcen jako nekuˇr´ak“, po druh´e jako b´ yval´ y ” ” kuˇr´ak“ a v ostatn´ıch pˇr´ıpadech tˇreba jen za pomoc´ı sloves Souˇ casn´ y stav pozn´ an´ı kouˇril“ nebo kouˇr´ı 0“. Posledn´ı tˇri varianty pak bezpo” ” chyby pozmˇen´ı v´ ysledky JSA. Ot´azky t´ ykaj´ıc´ı se kvanL´ekaˇre a v´ yzkumn´e pracovn´ıky nejv´ıce zaj´ım´a titativn´ı a ˇcasov´e str´anky kouˇren´ı nemohou b´ yt pomoc´ı skuteˇcnost, zda je konkr´etn´ı pacient kuˇr´ ak, ˇci nikoliv. D´ale jednoduch´e statistiky postihnuty v˚ ubec. mohou tˇeˇzit z informac´ı typu kolik cigaret pacient kouˇr´ı“, ” jak dlouho kouˇr´ı“, pˇr´ıpadnˇe pokud se jedn´ a o exkuˇr´aka Sada testovac´ıch l´ekaˇrsk´ ych z´aznam˚ u byla z´ısk´ana ” ´ kdy pˇrestal kouˇrit“. Ot´ azka pacientova kuˇr´ ack´eho sta- z r˚ uzn´ ych ambulanc´ı UVN (pohotovost, kardiologie, koˇzn´ı ” tutu m˚ uˇze b´ yt v ˇcesk´e ELD vyˇreˇsena pomoc´ı jednoduch´e oddˇelen´ı, oˇcn´ı klinika atd.) a od rozd´ıln´ ych l´ekaˇr˚ u. Takto statistick´e anal´ yzy (JSA). Na z´ akladˇe v´ yskytu v´ yraz˚ u se vytvoˇril reprezentativn´ı vzorek ELD. Na z´akladˇe prspojen´ ych s kouˇren´ım, jak´ ymi jsou napˇr. kuˇra´k/kuˇraˇcka votn´ı anal´ yzy 7 252 z´aznam˚ u byly tˇri z´akladn´ı kategonebo kouˇrit (vˇcetnˇe inflexe), lze takto oznaˇcen´e z´aznamy rie (Kuˇr´aci, Nekuˇr´aci, Exkuˇr´aci) d´ale rozdˇeleny do dev´ıti pˇriˇradit kouˇr´ıc´ım pacient˚ um. podkategori´ı (Obr´azek 1).
1 Jm´ eno pacienta i jeho oˇsetˇruj´ıc´ıho l´ ekaˇre bylo smaz´ ano a rodn´ e ˇ c´ıslo nahrazeno jedineˇ cn´ ym ˇ c´ıseln´ ym identifik´ atorem.
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Obr´ azek 1: Navrˇzen´ y klasifikaˇcn´ı algoritmus.
Kaˇzd´a z nich reprezentuje slovo nebo kl´ıˇcovou vlastnost, pomoc´ı nichˇz je moˇzno z´ akladn´ı kategorii nejl´epe definovat. Pro kategorii Kuˇr´ aci bylo vybr´ ano jako nejvhodnˇejˇs´ı: • podstatn´e jm´eno kuˇr´ ak/kuˇraˇcka“, ” • sloveso kouˇr´ı“, ” • z´akaz nekouˇrit!“. ” Podkategorie Nekouˇrit! musela b´ yt doplnˇena z d˚ uvodu ˇcast´eho v´ yskytu z´aznamu, kdy l´ekaˇr v´ yslovnˇe neuv´ ad´ı pacient˚ uv vztah ke kouˇren´ı, ale pouze mu zakazuje kouˇrit. Konkludentnˇe jej tak ˇrad´ı mezi kuˇr´ aky. Zvl´ aˇstn´ı des´at´a podkategorie Kolik kouˇr´ı? byla pˇrid´ ana pro kvantitativn´ı anal´ yzu kouˇr´ıc´ıch pacient˚ u. Kategorie Nekuˇr´ aci n´ aslednˇe zahrnuje ELD, kter´a v sobˇe obsahuje: • podstatn´e jm´eno nekuˇr´ ak/nekuˇraˇcka“, ” • sloveso nekouˇr´ı“, ” • oznaˇcen´ı, ˇze pacient kouˇr´ı 0“. ” Posledn´ı kategorie Exkuˇr´ aci je rozezn´ ana na z´ akladˇe: • podstatn´eho jm´ena exkuˇr´ ak/exkuˇraˇcka“, ” • slovesa kouˇril“, ” • dalˇs´ıch moˇznost´ı, jak vyj´ adˇrit, ˇze pacient je b´ yval´ y ” kuˇr´ak / dˇr´ıve kouˇril“. Boolovsk´a logika (Obr´ azek 1 – ˇcerven´e vazby) byla pouˇzita z d˚ uvodu sd´ılen´ı nˇekter´ ych podkategori´ı mezi IJBH – Volume 4 (2016), Issue 2
Kuˇr´aky a Nekuˇr´aky. Ze z´akladn´ı kategorie Kuˇr´aci tak musely b´ yt vyˇclenˇeny z´aznamy spadaj´ıc´ı mezi podkategorie Kouˇr´ı 0 a B´ yval´ y kuˇr´ak. Z´aroveˇ n kategorie Nekuˇr´aci byla omezena jen na pˇr´ıpady, kdy se sloveso nekouˇr´ı“ nevy” skytuje v jednom z´aznamu spolu s tvarem kouˇril“. Na ” z´akladˇe aplikace Boolovsk´e logiky bylo moˇzno n´asleduj´ıc´ı z´aznam: [. . . Dyslipid´emie na dietˇe hypothyreoza v disp., ter. u ´razy: 0 GA: menopauza kolem 50. roku, 1 porod, prev. pravid., mammografie pravid. TAT: 21.2.2013 AA: neguje. Ab.: nekuˇraˇcka, kouˇrila 30 let 10-20 cig. dennˇe, alkohol v´ yjimeˇcnˇe SA . . . ] hodnotit jako l´ekaˇrsk´ y z´aznam exkuˇr´aka, a to i pˇresto, ˇze obsahuje kl´ıˇcov´e slovo pro kategorii Nekuˇr´aci nekuˇraˇcka“. ”
Praktick´ a realizace Navrˇzen´ y model nen´ı postaven na z´akladu pˇresn´e shody kl´ıˇcov´eho slova v textu, ale pracuje s celkov´ ym vyznˇen´ım v r´amci jednotliv´ ych gramatick´ ych vazeb. Pro zasazen´ı klasifikaˇcn´ıho algoritmu tak musela b´ yt vytvoˇrena vlastn´ı pravidla pro anal´ yzu obsahu textu. Pravidla pouˇz´ıvaj´ı regul´arn´ı v´ yrazy java.util.regex package [10]. Pro kaˇzdou kategorii a podkategorii bylo nutno vytvoˇrit minim´alnˇe dvˇe pravidla. Jejich pˇr´ıkladem (pravidlo Kolik kouˇr´ı?) je n´asleduj´ıc´ı xml soubor.
<m i c a t e g o r y=” $ . Abusus . Koureni . K ur ac i . Kouri . K o l i k k o u r i ” v a l u e=” $ { 2 . s t r } $ { 3 . l e x } c i g a r e t dennˇ e ”>
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<w i d=” 1 ” s t r=” ! / ˆ ( ( ne ) | ( Ne ) ) $/ ” /> <w i d=” 2 ” s t r=” / ˆ ( ( k o uˇr´ı ) | ( Kouˇr´ı ) ) $/ ” /> <w i d=” 3 ” pos=” numeral ” /> <mi c a t e g o r y=” $ . Abusus . Koureni . Kuraci . Kouri . K o l i k k o u r i ” v a l u e=” k o uˇr´ı $ { 2 . s t r } c i g a r e t dennˇ e ”> <w i d=” 1 ” l e x=” / ˆ ( ( kuˇra ´ k ) | ( kuˇr aˇ c ka ) | ( Kuˇra ´k ) | ( Kuˇraˇ c ka ) ) $/ ” /> <w i d=” 2 ” pos=” numeral ” /> <mi c a t e g o r y=” $ . Abusus . Koureni . Kuraci . Kouri . K o l i k k o u r i ” v a l u e=” $ { 2 . s t r } $ { 3 . l e x } $ { 4 . l e x e ”> } $ { 5 . l e x } c i g a r e t dennˇ <w i d=” 1 ” s t r=” ! / ˆ ( ( ne ) | ( Ne ) ) $/ ” /> <w i d=” 2 ” s t r=” / ˆ ( ( k o uˇr´ı ) | ( Kouˇr´ı ) ) $/ ” /> <w i d=” 3 ” pos=” numeral ” /> <w i d=” 4 ” pos=” r e s i d u a l ” /> <w i d=” 5 ” pos=” numeral ” /> <mi c a t e g o r y=” $ . Abusus . Koureni . Kuraci . Kouri . K o l i k k o u r i ” v a l u e=” k o uˇr´ı $ { 2 . s t r } $ { 3 . l e x } $ { 4 . l e x } c i g a r e t dennˇ e ”> <w i d=” 1 ” l e x=” / ˆ ( ( kuˇra ´ k ) | ( kuˇr aˇ c ka ) | ( Kuˇra ´k ) | ( Kuˇraˇ c ka ) ) $/ ” /> <w i d=” 2 ” pos=” numeral ” /> <w i d=” 3 ” pos=” r e s i d u a l ” /> <w i d=” 4 ” pos=” numeral ” /> 2
Vˇsech 386 587 soubor˚ u, o celkov´e velikosti 1,6 GB , bylo zpracov´ano v syst´emu IBM Watson Explorer Content Analytics, jenˇz vych´ az´ı z projektu Apache Lucene. V pr˚ ubˇehu zpracov´ an´ı bylo kaˇzd´e slovo jak lingvisticky rozpozn´ano, tak i pˇr´ısluˇsnˇe oznaˇceno, pokud bylo zaˇrazeno do jedn´e ze stanoven´ ych kategori´ı/podkategori´ı. Vedlejˇs´ı u ´daje byly n´aslednˇe uloˇzeny v 55 souborech do indexu [13] o celkov´e velikosti 3,81 GB. Pˇridan´e kategorie pro identifikaci kuˇr´ak˚ u i NLP zpracov´ an´ı nav´ yˇsilo index do t´e m´ıry, ˇze se p˚ uvodn´ı velikost v´ıce neˇz zdvojn´ asobila.
V´ ysledky Z proveden´e anal´ yzy je moˇzno vyvodit, ˇze z p˚ uvodn´ıch 314 202 jedineˇcn´ ych l´ekaˇrsk´ ych z´ aznam˚ u je jich s kouˇren´ım spjato 20 704. Z t´eto omezen´e mnoˇziny pak 5 614 n´aleˇz´ı kuˇr´ak˚ um, 11 912 nekuˇr´ ak˚ um a 3 178 exkuˇr´ ak˚ um. V r´amci kouˇr´ıc´ıch pacient˚ u v´ıce neˇz polovina (51,05 %) deklaruje, ˇze kouˇr´ı 10 nebo 20 cigaret dennˇe3 . L´ekaˇrsk´e z´aznamy vztahuj´ıc´ı se ke kouˇren´ı byly n´aslednˇe ruˇcnˇe vyhodnoceny. Automatizovan´ y klasifikaˇcn´ı syst´em se od lidsk´eho hodnotitele liˇsil ve zhruba 1,25 %. Toto je akceptovateln´ y v´ ysledek, zvl´ aˇstˇe s uv´aˇzen´ım, ˇze v nˇekter´ ych pˇr´ıpadech s´ am hodnot´ıc´ı obt´ıˇznˇe stanovoval, do kter´e kategorie jednotliv´ y z´ aznam n´ aleˇz´ı. O nˇeco vyˇsˇs´ı chybovosti (1,99 %) bylo dosaˇzeno v r´ amci stanoven´ı poˇctu kouˇren´ ych cigaret. V r´amci porovn´ an´ı jednoduch´e anal´ yzy s navrˇzen´ ym klasifikaˇcn´ım modelem zaloˇzen´ ym na obsahov´e anal´ yze doˇslo ke sn´ıˇzen´ı poˇctu z´ aznam˚ u n´ aleˇzej´ıc´ıch kuˇr´aku z 13 2 Velikost testovac´ ıch soubor˚ u se ve vstupn´ı mnoˇ zinˇ e pohybovala od nˇ ekolika byt˚ u aˇ z po nˇ ekolik kB. Stˇredn´ı velikost soubor˚ u se rovnala 727 B.
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282 na 5 614. Propad o 57,73 % prokazuje vhodnost t´eto metody.
Uplatnˇ en´ı v medic´ınˇ e a ve zdravotnictv´ı Navrˇzen´ y syst´em je schopen zpracovat terabyty l´ekaˇrsk´e dokumentace, aniˇz by byl z´avisl´ y na form´atu vstupn´ıch dat. Lze jej tak vyuˇz´ıt pro automatickou klasifikaci pacient˚ u bez nutnosti dalˇs´ıho pˇrizp˚ usoben´ı. Syst´em umoˇzn ˇuje automaticky rozˇrazovat nestrukturovan´e l´ekaˇrsk´e z´aznamy dle toho, zda je pacient kuˇr´ak, nekuˇr´ak ˇci exkuˇr´ak, a to s v´ıce jak 98% pˇresnost´ı. Dalˇs´ı zpˇresnˇen´ı bude pˇredmˇetem n´asleduj´ıc´ıho v´ yzkumu.
Podˇ ekov´ an´ı Tato pr´ace byla podpoˇrena projektem SVV 260 267 Univerzity Karlovy.
Reference [1] Bastida G, Beltr´ an B. Ulcerative colitis in smokers, nonsmokers and exsmokers. World J Gastroenterol 2011; 17: 2740–7. [2] Bl´ aha M, Janˇ ca D, Klika P, Muˇ z´ık J, Duˇsek L. Project ICOP – Architecture of Software Tool for Decision Support in Oncology. Data and Knowledge for Medical Decision Support. Proceedings of the EFMI Special Topic Conference. 2013; 130–134. [3] Calabrese E, Yanai H, Shuster D, et al. Low-dose smoking resumption in exsmokers with refractory ulcerative colitis. J Crohns Colitis 2012; 6: 756–62. [4] Cochrane Collaboration. cochrane.org.
Available
from:
https://www.
[5] Hartzband P, Groopman J. Untangling the Web –Patients, Doctors, and the Internet. N Engl J Med 2010; 362:1063–1066. [6] Holzinger A, Stocker C, Ofner B, Prochaska G, Brabenetz A, Hofmann-Wellenhof R. Combining HCI, Natural Language Processing, and Knowledge Discovery – Potential of IBM Content Analytics as an Assistive Technology in the Biomedical Field. Human-Computer Interaction and Knowledge Discovery in Complex, Unstructured, Big Data. 2013; 7947: 13–24. [7] Johnson SB, Bakken S, Dine D, Hyun S, Mendon¸ca E, Morrison F, Bright T, Van Vleck T, Wrenn J, Stetson P. An electronic health record based on structured narrative. J Am Med Inform Assoc. 2008; 15(1): 54–64. ˇ ıd R, Kub´ [8] Klimeˇs D, Sm´ asek M, Vyzula R, Duˇsek L. DIOS –Database of Formalized chemotherapeutic Regimens. Data and Knowledge for Medical Decision Support. Proceedings of the EFMI Special Topic Conference. 2013; 165–169. [9] Project UIMA, Apache UIMA. Available from: https://uima. apache.org/. [10] Regular expressions. Available from: https://docs.oracle. com/javase/7/docs/api/java/util/regex/Pattern.html. [11] Schiff GD, Bates DW. Can Electronic Clinical Documentation Help Prevent Diagnostic Errors NEJM 2010; 362: 1066–1069. [12] Stonov´ a M. Unstructured Data in Evidence-based MHealthcare. Semantic Inte- roperability in Biomedicine and Healthcare. IJBH 2015; 2(1): 47–49. 3 Jeden
z pacient˚ u uv´ ad´ı, ˇ ze kouˇril 120 cigaret dennˇ e.
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[13] Stonov´ a M. Unstructured Data in Healthcare. Semantic Interoperability in Biomedicine and Healthcare. IJBH 2014; 2(1): 34–36. [14] Walsh KE, Gurwitz JH. Medical abbreviations: writing little
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and communicating less. Arch. Dis. Child 2008; 93: 816–817. [15] Zvolsk´ y M. Automating the Use of Clinical Practice Guidelines in the Health Information Infrastructure. Semantic Interoperability in Biomedicine and Healthcare. IJBH 2014; 2(1): 51–52.
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Abstrakt
Softwarov´ a sada pro sbˇ er a zpracov´ an´ı dat v dlouhodob´ e p´ eˇ ci Sl´ avka V´ıteˇ ckov´ a1 , Radim Krupiˇ cka1 , Ondˇrej Klemp´ıˇr1 , Zolt´ an Szab´ o1 , Hana Vaˇ nkov´ a2 , Martina Kuckir2 , Iva Holmerov´ a2 1 2
ˇ e vysok´e uˇcen´ı technick´e v Praze, Kladno, Cesk´ ˇ a republika Fakulta biomedic´ınsk´eho inˇzen´yrstv´ı, Cesk´
ˇ a republika CELLO-ILC-CZ Fakulta humanitn´ıch studi´ı Univerzity Karlovy a Gerontologick´e centrum v Praze, Cesk´
Kontakt: IJBH 2016; 4(2):35 Sl´ avka V´ıteˇ ckov´ a
zasl´ ano: 15. ˇ cervence 2016
Fakulta biomedic´ınsk´ eho inˇzen´ yrstv´ı, ˇ e vysok´ Cesk´ e uˇ cen´ı technick´ e v Praze
pˇrijato: 15. srpna 2016 publikov´ ano: 20. z´ aˇr´ı 2016
Adresa: N´ am. S´ıtn´ a 3105, 272 01 Kladno E–mail: [email protected]
Abstrakt
softwarovou sadu pro sbˇer a dlouhodob´e skladov´an´ı dat, kter´a je navrˇzena tak, aby odpov´ıdala potˇreb´am instiˇ anek nejdˇr´ıve popisuje potˇrebu tuc´ı dlouhodob´e p´eˇce. Cl´ vytvoˇren´ı tohoto syst´emu a jeho roli v instituc´ıch dlouhodob´e p´eˇce. Pot´e je prezentov´ana samotn´a softwarov´a sada. N´asleduje popis klinick´eho vyuˇzit´ı novˇe vyvinut´eho softwaru. Z´avˇerem jsou diskutov´any siln´e a slab´e str´anky softwarov´e sady a smˇer dalˇs´ıho v´ yvoje.
Pro efektivn´ı ˇr´ızen´ı kvality instituc´ı dlouhodob´e p´eˇce ´ je vyhodnocov´an´ı dat rozhoduj´ıc´ı. Udaje nasb´ıran´e institucemi dlouhodob´e p´eˇce mohou b´ yt pouˇzity pro n´asledn´e anal´ yzy. Napˇr´ıklad pro vyhodnocen´ı sobˇestaˇcnosti pacient˚ u a kˇrehkosti a odpov´ıdaj´ıc´ıch rizikov´ ych faktor˚ u, nebo vyhodnocen´ı u ´ˇcinnosti a kvality poskytovan´e dlouˇ e repubhodob´e p´eˇce. Sbˇer dat (v dlouhodob´e p´eˇci v Cesk´ ıˇ cov´ a slova lice) se prov´ad´ı pomoc´ı pap´ırov´ ych dotazn´ık˚ u, ze kter´ ych Kl´ jsou u ´daje zpracov´ av´ any ruˇcnˇe. Avˇsak z hlediska zpraSyst´emy pro podporu rozhodov´an´ı, inovace zdravotn´ı cov´an´ı vˇetˇs´ıho mnoˇzstv´ı dat a jejich dlouhodob´eho skladov´an´ı nen´ı tento zp˚ usob vhodn´ y. Proto jsme vyvinuli p´eˇce a IT, webov´e zdravotnick´e syst´emy, kontrola kvality
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P˚ uvodn´ı ˇ cl´ anek
Vliv fitness n´ aramku na rizikov´ e faktory metabolick´ eho syndromu Martina Vlas´ akov´ a1 , Jan Muˇ z´ık1 1
ˇ a republika Centrum podpory aplikaˇcn´ıch v´ystup˚ u a spin-of firem, 1. l´ekaˇrsk´ a fakulta, Univerzita Karlova, Praha, Cesk´
Abstrakt Souˇ casn´ a situace: Nedostateˇcn´a fyzick´a aktivita spolu s dalˇs´ımi faktory p˚ usob´ı na vznik nadv´ahy, vysok´eho krevn´ıho tlaku a onemocnˇen´ı diabetes mellitus. Bylo prok´az´ano, ˇze pouˇzit´ı krokomˇeru m´a vliv na zv´yˇsen´ı fyzick´e aktivity a s t´ım spojen´e sn´ıˇzen´ı hmotnosti. Novou v´yzvu pro hodnocen´ı fyzick´e aktivity pˇrin´aˇs´ı fitness n´aramky, kter´e jsou pro sv´e rozˇs´ıˇren´e funkce st´ale obl´ıbenˇejˇs´ı mezi uˇzivateli. C´ıle: C´ılem tohoto pˇrehledov´eho ˇcl´anku bylo zjistit, jak´y maj´ı fitness n´aramky vliv na faktory metabolick´eho syndromu (soubor rizikov´ych ˇcinitel˚ u, kter´e se ˇcasto vyskytuj´ı spoleˇcnˇe a vznikaj´ı velmi pravdˇepodobnˇe na podkladˇe inzul´ınov´e rezistence). Metody: Na z´akladˇe reˇserˇs´ı v datab´azi Web of Science bylo vyhled´ano 172 anglicky psan´ych studi´ı publikovan´ych ˇ do ˇcervna 2016. Sest studi´ı splˇ novalo stanoven´a krit´eria. U pˇeti studi´ı byl prok´az´an vliv uˇzit´ı fitness n´aramku na faktory metabolick´eho syndromu.
V´ ysledky: V´ysledky studi´ı byly velmi heterogenn´ı. Tˇri studie prok´azaly signifikantn´ı sn´ıˇzen´ı v´ahy u ´ˇcastn´ık˚ u. Uu ´ˇcastn´ık˚ u dalˇs´ı studie byl sn´ıˇzen prokazatelnˇe obvod pasu a v´ysledky jin´e studie pouk´azaly na sn´ıˇzen´ı rizika vzniku diabetu. Z´ avˇ er: V´ysledky studi´ı naznaˇcuj´ı, ˇze fitness n´aramky pozitivnˇe ovlivˇ nuj´ı rizikov´e faktory metabolick´eho syndromu, avˇsak nejsou pˇresvˇedˇciv´e. Efekt vyuˇzit´ı fitness n´aramku je nejednoznaˇcn´y a je potˇreba v´ıce dalˇs´ıch kvalitn´ıch randomizovan´ych studi´ı k posouzen´ı vlivu fitness n´aramku na faktory metabolick´eho syndromu.
Kl´ıˇ cov´ a slova Fitness n´aramek, Monitorovac´ı syst´em elektronick´e aktivity, fyzick´a aktivita, metabolick´y syndrom, sebekontrola
Kontakt: Martina Vlas´ akov´ a 1. l´ ekaˇrsk´ a fakulta, Univerzita Karlova Address: Kateˇrinsk´ a 32, 121 08 Praha 2 E–mail: [email protected]
´ Uvod Souˇ casn´ a situace
IJBH 2016; 4(2):36–39 zasl´ ano: 15. ˇ cervence 2016 pˇrijato: 15. srpna 2016 publikov´ ano: 20. z´ aˇr´ı 2016
aktivity, nezdrav´a strava a ˇskodliv´a konzumace alkoholu), kter´e vedou ke ˇctyˇrem kl´ıˇcov´ ym metabolick´ ym / fyziologick´ ym zmˇen´am (zv´ yˇsen´ y krevn´ı tlak, nadv´aha / obezita, zv´ yˇsen´a hladina gluk´ozy v krvi a vyˇsˇs´ı hladina cholesterolu) [2]. Inzulinov´a rezistence, abdomin´aln´ı obezita, hypertenze a hyperglyk´emie spolu s aterogenn´ı dyslipid´emi´ı tvoˇr´ı z´akladn´ı sloˇzky metabolick´eho syndromu (MS) [3]. Pˇredej´ıt vzniku rizikov´ ych faktor˚ u lze zdrav´ ymi dietn´ımi n´avyky, zapojen´ım pravideln´e fyzick´e aktivity a zachov´an´ım norm´aln´ı tˇelesn´e hmotnosti [2].
Svˇetov´a zdravotnick´ a organizace uv´ ad´ı, ˇze z 56 milion˚ u u ´mrt´ı v roce 2012 pˇripadalo 38 milion˚ u na nepˇrenosn´a onemocnˇen´ı, jako jsou kardiovaskul´ arn´ı nemoci, rakovina, diabetes, chronick´a respiraˇcn´ı onemocnˇen´ı a jin´e [2]. Z nepˇrenosn´ ych nemoc´ı pˇripadalo 31 % u ´mrt´ı na kardiovaskul´arn´ı onemocnˇen´ı (11 % z kardiovaskul´ arn´ıch u ´mrt´ı je pˇripisov´ano vysok´e hladinˇe gluk´ ozy v krvi) a dalˇs´ı t´emˇeˇr 3 % u ´mrt´ı bylo pˇripisov´ ano diabetu. V roce 2014 mˇel aramky v´ıce jak 1 ze 3 dospˇel´ ych lid´ı nadv´ ahu a kaˇzd´ y des´ at´ y byl Fitnes n´ ob´ezn´ı. K tomu 8,5 % dospˇel´ ych lid´ı trpˇelo onemocnˇen´ım diabetes mellitus [1]. V posledn´ıch letech byly vyuˇz´ıv´any k mˇeˇren´ı fyVˇetˇsina nepˇrenosn´ ych nemoc´ı je v´ ysledkem ˇctyˇr zick´e (ne)aktivity krokomˇery. V´ ysledky studi´ı ukazuj´ı, konkr´etn´ıch chov´an´ı (uˇz´ıv´ an´ı tab´ aku, nedostatek fyzick´e ˇze pouˇzit´ı krokomˇeru m˚ uˇze jeho uˇzivatele motivovat ke IJBH – Volume 4 (2016), Issue 2
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zv´ yˇsen´ı fyzick´e aktivity, coˇz je v u ´zk´em vztahu se sn´ıˇzen´ım tˇelesn´e hmotnosti [4]. Pro spr´ avnou motivaci je tak´e d˚ uleˇzit´a forma zpˇetn´e vazby, protoˇze ne vˇzdy intervence krokomˇerem vedla ke zv´ yˇsen´ı fyzick´e aktivity. Zpˇetn´a vazba mus´ı b´ yt upravena dle charakteristiky jednotliv´ ych pacient˚ u. Je d˚ uleˇzit´e, aby j´ı uˇzivatel rozumˇel, protoˇze pouze tehdy m˚ uˇze zmˇenit sv´e chov´ an´ı a udrˇzet si nov´e n´avyky [5]. Krokomˇery jsou jednoduch´ ym a dostupn´ ym n´astrojem pro hodnocen´ı fyzick´e aktivity. Informace, kter´e pod´avaj´ı uˇzivatel˚ um, jsou snadn´e na pochopen´ı a jejich pouˇzit´ı je jednoduch´e – coˇz umoˇzn ˇuje jejich ˇsirok´e pouˇzit´ı [6]. Pro udrˇzen´ı motivace je potˇreba, aby mˇel uˇzivatel stanoven dosaˇziteln´ y c´ıl, kter´ y se bude upravovat na z´akladˇe aktu´alnˇe namˇeˇren´ ych dat. Pedometry poskytuj´ı informace o poˇctu uˇsl´ ych krok˚ u, ale jiˇz neinformuj´ı o intenzitˇe pohybu, frekvenci ˇci d´elce aktivity [7]. V tomto ohledu maj´ı velk´ y potenci´ al fitness n´aramky (Activity trackery, Sporttestery), kter´e se st´avaj´ı st´ale obl´ıbenˇejˇs´ı mezi uˇzivateli [9]. Fitness n´ aramky nab´ızej´ı snadn´ y sbˇer dat, rychlou zpˇetnou vazbu k uˇzivateli a pomoc´ı synchronizace s poˇc´ıtaˇcem nebo chytr´ ym telefonem i sd´ılen´ı dat [8]. Oproti krokomˇer˚ um nab´ızej´ı uˇzivateli dalˇs´ı funkce – spotˇrebu kalori´ı, mˇeˇren´ı tepu nebo kvality sp´anku. Dalˇs´ı moˇznost´ı je poskytov´ an´ı vizu´aln´ı zpˇetn´e vazby pr˚ ubˇehu fyzick´e aktivity, verb´ aln´ı povzbuzov´an´ı a soci´aln´ı srovn´ an´ı. Lewis [9] definoval tato zaˇr´ızen´ı jako Electronic Activity Monitor System (EAMS) – Bezdr´ atov´e zaˇr´ızen´ı, kter´e objektivnˇe mˇeˇr´ı fyzickou aktivitu a prov´ ad´ı zpˇetnou vazbu, z´ aroveˇ n zobrazuj´ıc´ı z´ akladn´ı informace o ˇcinnosti, a kter´e pomoc´ı displeje nebo prostˇrednictv´ım aplikace vzbuzuje neust´ alou sebekontrolu aktivn´ıho chov´ an´ı.
C´ıle C´ılem tohoto pˇrehledu bylo zjistit souvislosti mezi pouˇz´ıv´an´ım fitness n´ aramku a rizikov´ ymi faktory metabolick´eho syndromu.
Metody ˇ anky byly identifikov´ Cl´ any pomoc´ı elektronick´e datab´aze Web of Science. Kl´ıˇcov´ a slova pro vyhled´an´ı byla n´asleduj´ıc´ı:( Fitness and tracker“ or Digital and trac” ” ker“ or Activity and tracker“ or Wearable and tracker“ ” ” or Wearable and device“ or Wearable and technology“ ” ” or Pedometer“ or Self* and tracker“) and ( Metabolic ” ” ” disease syndrome“ or Metabolic risk faktors“ or Dia” ” betes“ or High pressure“ or Syndrome X“ or Metabo” ” ” lic syndrome“ or Insuline resistence“ or Cardiovascular ” ” disease“). Vyhled´ av´ an´ı bylo zamˇeˇreno pouze na anglicky psan´e, pln´e texty se zamˇeˇren´ım na experiment´aln´ı studie publikovan´e do ˇcervna 2016. Do pˇrehledu nebyly zahrnuty n´avrhy studi´ı nebo systematick´e pˇrehledy a studie, kde bylo u ´ˇcastn´ık˚ um m´enˇe neˇz 18 let. V´ ybˇer studi´ı prob´ıhal ve 4 kroc´ıch – selekce duplicitn´ıch studi´ı, n´ aslednˇe tˇr´ıdˇen´ı podle n´azvu, abstraktu a pln´eho textu. Krit´eriem pro c
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v´ ybˇer studie byla intervence fitness n´aramku, popˇr´ıpadˇe krokomˇeru propojen´eho s aplikac´ı nebo poˇc´ıtaˇcem zahrnuj´ıc´ı vzd´alen´e kouˇcov´an´ı, na rizikov´e faktory metabolick´eho syndromu. Vybran´e studie byly porovn´any.
V´ ysledky Na z´akladˇe kl´ıˇcov´ ych slov bylo vyhled´ano 172 studi´ı. V prvn´ıch dvou kroc´ıch bylo vyˇrazeno 77 studi´ı. Pln´ y text byl hodnocen u 95 studi´ı. Velk´e mnoˇzstv´ı studi´ı ve tˇret´ım kroku bylo zapˇr´ıˇcinˇeno hlubˇs´ım zkoum´an´ım pˇresn´eho vyuˇzit´ı EAMS. Vyˇrazeny byly studie (celkem 86), kde EAMS slouˇzilo pouze k monitoraci fyzick´e aktivity bez zpˇetn´e vazby pro pacienta, nebo byl pˇredmˇet studie jin´eho zamˇeˇren´ı. Dvˇe studie byly vyˇrazeny z d˚ uvodu toho, ˇze byl pln´ y text psan´ y ve ˇspanˇelˇstinˇe, u jedn´e studie se nepodaˇrilo naj´ıt pln´ y text [10]. Vyfiltrov´ano bylo 6 studi´ı, kter´e byly zaˇrazeny do pˇrehledu.
Charakteristika studi´ı Vybran´e studie (Tabulka 1) byly velmi heterogenn´ı – co se t´ yˇce d´elky, poˇctu u ´ˇcastn´ık˚ u, sledovan´ ych parametr˚ u i zp˚ usobu intervence. Z tohoto d˚ uvodu je bylo tˇeˇzk´e vz´ajemnˇe porovnat. Nejdelˇs´ı studie trvala 16 mˇes´ıc˚ u [14], nejkratˇs´ı 4 t´ ydny [16]. Do tˇr´ı studi´ı bylo zaˇrazeno v´ıce jak 200 u ´ˇcastn´ık˚ u [12, 13, 15], pr˚ umˇern´ y poˇcet u ´ˇcastn´ık˚ u ve studii byl 182, ale se smˇerodatnou odchylkou (SD) 178. V pr˚ umˇeru dokonˇcilo studii 70 % u ´ˇcastn´ık˚ u (SD=25). U tˇr´ı studi´ı byla vstupn´ım parametrem pro v´ ybˇer u ´ˇcastn´ıka nadv´aha [13, 16, 14]. Dvˇe studie zahrnuly pracovn´ıky zapojen´e do preventivn´ıho programu spoleˇcnosti [12, 15] a jedna studie se orientovala na ˇzeny, kter´e prodˇelaly gestaˇcn´ı diabetes [11]. V´ ysledkem pˇeti studi´ı byla objektivnˇe mˇeˇriteln´a data – obvod pasu, v´aha, body mass index (BMI), krevn´ı tlak, or´aln´ı gluk´ozov´ y test nebo mˇeˇren´ı glykovan´eho hemoglobinu HbA1c. Jedna studie mˇeˇrila v´ ysledky pomoc´ı dotazn´ıku hodnot´ıc´ıho riziko vzniku onemocnˇen´ı diabetes mellitus II. typu (the Australian Type 2 Diabetes Risk Assessment Tool – ´ castn´ıci studie byli rozdˇeleni do tˇr´ı (AUSDRISK)) [12]. Uˇ skupin na z´akladˇe v´ ysledku AUSDRISK – skupina s vysok´ ym rizikem vzniku diabetu do 5 let, se stˇredn´ım rizikem a n´ızk´ ym rizikem.
V´ ysledky studi´ı Souhrnn´ ym znakem vˇsech studi´ı bylo vyuˇzit´ı EAMS jako intervenˇcn´ıho n´astroje s c´ılem zjistit jeho vliv na vybran´e rizikov´e faktory MS. Studie Kim [11] nezaznamenala ˇz´adn´e signifikantn´ı zmˇeny (proveden or´aln´ı gluk´ozov´ y test). Ostatn´ı studie zaznamenaly pozitivn´ı vliv na sn´ıˇzen´ı faktor˚ u metabolick´eho syndromu. Studie Rowe – Roberts [12] zaznamenala u 23 % u ´ˇcastn´ık˚ u sn´ıˇzen´ı AUSDRISK sk´ore, pˇriˇcemˇz u u ´ˇcastn´ık˚ u s vysok´ ym rizikem vzniku diabetu byla zaznamen´ana vyˇsˇs´ı fyzick´a aktivita v porovn´an´ı s ostatn´ımi skupinami – pr˚ umˇern´ y poˇcet IJBH – Volume 4 (2016), Issue 2
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krok˚ u 8588 v porovn´an´ı se skupinou stˇredn´ıho rizika (7836 krok˚ u) a n´ızk´eho rizika (7878 krok˚ u). Studie Fukuoky [13], Sepah [14] a Richardsona [16] zaznamenaly signifikantn´ı pokles v´ahy u u ´ˇcastn´ık˚ u. A v´ ysledky experimentu FreakPoli [15] zaznamenaly sn´ıˇzen´ı obvodu pasu v pr˚ umˇeru o 1.6 cm (SD=5.9). U studi´ı, kde u ´ˇcastn´ıci poch´ azeli z preventivn´ıho programu organizace [12, 15] byla shodn´ ym znakem vz´ajemn´a znalost u ´ˇcastn´ık˚ u. Tyto studie nezahrnovaly individu´alnˇe c´ılen´ y program ani strukturovan´ y behavior´aln´ı program, oproti ostatn´ım studi´ım. Pˇresto obˇe studie zaznamenaly prokazateln´ y pozitivn´ı vliv na mˇeˇren´e parametry (sn´ıˇzen´ı AUSDRISK sk´ ore a obvodu pasu). Ani u jedn´e z vybran´ ych studi´ı neprobˇehla n´ asledn´ a kontrola mˇeˇren´ ych parametr˚ u delˇs´ı dobu po intervenci s c´ılem zjiˇstˇen´ı vlivu na rizikov´e faktory MS i v delˇs´ım ˇcasov´em horizontu.
Diskuze Tento systematick´ y pˇrehled shrnuje v´ ysledky EAMS intervenc´ı na rizikov´e faktory MS zahrnuj´ıc´ı studie publikovan´e do ˇcervna 2016 dostupn´e v datab´ azi Web of Science. V´ ysledky naznaˇcuj´ı, ˇze EAMS mohou m´ıt kladn´ y vliv na rizikov´e faktory MS – sn´ıˇzen´ı rizika vzniku diabetu II typu, sn´ıˇzen´ı tˇelesn´e hmotnosti a obvodu pasu. Nicm´enˇe v´ ysledky nejsou jednoznaˇcn´e a je potˇreba dalˇs´ıch hlubˇs´ıch a specifiˇctˇejˇs´ıch v´ yzkum˚ u, u kter´ ych hodnot´ıc´ım parametrem budou objektivnˇe mˇeˇriteln´ a data doplnˇena z´ aroveˇ n ´ castn´ıci v´ subjektivn´ım posouzen´ım. Uˇ yzkumu by mˇeli m´ıt moˇznost aktivnˇe vyuˇz´ıvat vˇsechny funkce, kter´e EAMS nab´ız´ı, vˇcetnˇe individu´ alnˇe stanoven´ ych c´ıl˚ u, srozumiteln´e zpˇetn´e vazby a sd´ılen´ı dat mezi stejnˇe zamˇeˇren´ ymi uˇzivateli. Studie by se mˇely zamˇeˇrit i na n´ asledn´e hodnocen´ı vlivu EAMS na chov´ an´ı u ´ˇcastn´ık˚ u studie delˇs´ı dobu po intervenci.
Limity pˇrehledov´ e studie Limitem t´eto studie je velk´ a heterogenita vybran´ ych studi´ı, kter´a byla pˇr´ıˇcinou obt´ıˇzn´eho porovn´ an´ı a hodnocen´ı v´ ysledk˚ u. Pro budouc´ı pr´ aci je potˇreba zvolit vhodnˇejˇs´ı kl´ıˇcov´a slova – napˇr´ıklad zahrnut´ı accelerome” ter“ a nav´ yˇsen´ı poˇctu prohled´ avan´ ych datab´ az´ı. Z´ aroveˇ n je potˇreba zvolit kl´ıˇc k porovn´ an´ı kvality jednotliv´ ych vybran´ ych studi´ı a jejich n´ asledn´emu hodnocen´ı (stanoven´ı min. d´elky studie, typ studie, mnoˇzstv´ı u ´ˇcastn´ık˚ u, kteˇr´ı dokonˇc´ı studii). Podˇ ekov´ an´ı Tato pr´ace vznikla za podpory projektu specifick´eho v´ yzkumu SVV 260 267 a projektu OP VK: Mezin´ arodn´ı spolupr´ace na Fakultˇe biomedic´ınsk´eho inˇzen´ yrstv´ı ˇ CVUT, reg. ˇc. projektu: CZ.1.07/2.3.00/20.0093.
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Reference [1] Global report on diabetes. World Health Organization; 2016. [2] World health statistics 2016: monitoring health for the SDGs, sustainable development goals. World Health Organization; 2016. [3] MeSH Browser Record [Internet]. U.S National Library of Medicine. U.S. National Library of Medicine. [4] Bravata DM, Smith-Spangler C, Sundaram V, Gienger AL, Lin N, Lewis R, et al. Using Pedometers to Increase Physical Activity and Improve Health. Jama. 2007;298(19):2296. [5] Polonsky WH, Fisher L. When Does Personalized Feedback Make A Difference? A Narrative Review of Recent Findings and Their Implications for Promoting Better Diabetes SelfCare. Curr Diab Rep Current Diabetes Reports. 2015;15(8). [6] Coffman MJ, Ferguson BL, Steinman L, Talbot LA, DunbarJacob J. A Health Education Pilot for Latina Women with Diabetes. Clinical Nursing Research. 2012;22(1):70–81. [7] Allet L, Knols RH, Shirato K, Bruin EDD. Wearable Systems for Monitoring Mobility-Related Activities in Chronic Disease: A Systematic Review. Sensors. 2010Aug;10(10):9026–52. [8] Bloomgarden Z, Li X-Y. Helping people with diabetes to exercise . Journal of Diabetes. 2014;7(2):150–2. [9] Lewis ZH, Lyons EJ, Jarvis JM, Baillargeon J. Using an electronic activity monitor system as an intervention modality: A systematic review. BMC Public Health. 2015;15(1). [10] Chang SA, Lee JM, Sohn TS, Son HS, Park SW, Baik SH, et al. Pedometer-Determined Physical Activity in Type 2 Diabetes in Korea. The Journal of Korean Diabetes Association J Korean Diabetes Assoc. 2007;31(1):83. [11] Kim C, Draska M, Hess MM, Wilson EE. A web-based pedometer programme in women with a recent history of gestational diabetes. Diabetic medicine?: a journal of the British Diabetic Association . 2012;29(2):278–83. [12] Rowe-Roberts D, Mueller FF, Cercos R. Preliminary results from a study of the impact of digital activity trackers on health risk statusPreliminary results from a study of the impact of digital activity trackers on health risk status. Studies in health technology and informatics . 2014Aug8;204:143–8. [13] Fukuoka Y, Gay, CL, Joiner KL. A Novel Diabetes Prevention Intervention Using a Mobile App A Randomized Controlled Trial With Overweight Adults at Risk. American journal of preventive medicine. 2015Aug;49(2): 223–37. [14] Sepah SC, Jiang L, Peters AL. Translating the Diabetes Prevention Program into an Online Social Network: Validation against CDC Standards. The Diabetes Educator. 2014Oct;40(4):435–43. [15] Freak-Poli RL, Wolfe R, Walls H, Backholer K, Peeters A. Participant characteristics associated with greater reductions in waist circumference during a four-month, pedometerbased, workplace health program. BMC Public Health. 2011;11(1):824. [16] Richardson CR, Brown BB, Foley S, Dial KS, Lowery JC. Feasibility of Adding Enhanced Pedometer Feedback to Nutritional Counseling for Weight Loss. J Med Internet Res Journal of Medical Internet Research. 2005;7(5).
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2016 EuroMISE s.r.o.
5 měsíců
16 měsíců
13 týdnů
4 měsíce
Fukuoka, Y (2015)
Sepah, SC (2014)
Kim, C (2012)
Freak‐Poli, RLA (2011)
4 týdny
7 měsíců
Rowe ‐ Roberts, D (2014)
Richardson, CR (2005)
Délka studie
Autor (rok publikace)
kvaziexperim ent
12
539
49
randomizova ná dvojitě slepá kontrolovaná studie
kvaziexperim ent
220
61
randomizova ná dvojitě slepá kontrolovaná studie
kvaziexperim ent
212
kvaziexperim ent
Typ studie
100%
79%
42%
65%
100%
36%
Výsledky studií
orální glukózový toleranční test
obvod pasu
hmotnost
věk ≥18 let 57 % žen, účastníci byli vybráni z deseti pracovišť
BMI ≥ 30, alespoň jeden z následujících rizikových faktorů kardiovaskulárních onemocnění: diabetes, hypertenze, hypercholesterolemie, obezita, ischemická choroba srdeční
hmotnost, HBA1C
krokoměr, V průměru byl zaznamenán ubýtek hmotnosti účastníků 1,9 SportBrain First internetové kg během tří týdnů. Step stránky, sezení
ne
ano
není specifikováno
krokoměr, internetové stránky, motivační emaily
V průměru byl obovd pasu účsatníků redukován o 1,6 cm (SD=5,9).
ne
krokoměr, vzdělávání Nebyly zaznamenány žádné signifikantní změny v chování není pomocí interetu, účastníků, či jejich fyzické aktivitě oproti výchozímu stavu. specifikováno internetové fórum
ne
ano
krokoměr, soukromá online Byla zaznamenána regrese HbA1C z prediabetického Omron HJ‐320 sociální síť, rozmezí (5,7 %‐6,4 %) do normálního rozmezí (< 5,7 %), Tri‐Axis sezení, zdravotní účastníci shodili v průměru 5,0 % své původní tělesné Pedometer trénink a hmotnosti v průběhu 16 týdnuů (ve 12 týdnu to bylo 4,8 %). bezdrátová váha
Fitbit Ultra
Typ zařízení
ne
Účastníci s vyšším AUSDRISK skóre, změřeném na počátku studie, byli více motivováni zvýšit svou fyzickou aktivitu a kontinuálně využívat zařízení po celou dobu studie. Tito fitness náramek účastníci měli nejvyšší průměrný počet kroků za den v pilotní studii (8588 kroků ve srovnání se 7836 kroky skupiny se středním rizikem a 7878 kroky skupiny s nízkým rizikem), po 7 měsících 23 % účastníků snížilo své AUSDRISK skóre.
Zbůsob intervence
ne
ne
ne
ano
ne
ano
ano
ne
ano
ano
ano
ne
ano
ne
ano
ano
ano
ne
ano
ano
ano
ano
ano
ane
Znali se Možnost účastníci sdílení Byl zde Individuálně Prováděli studie výsledků strukturovaný zaměřený účastníci self‐ navzájem před na behaviorální monitoring? program? začátkem sociálních program? studie? sítích?
Účastníci po intervenci shodili v průměru 6,2 kg (‐6,8 %) oproti neintervenované skupině 5,9 kg (–5,7 %), průměrný krokoměr, počet kroků za den stoupl u intervenované skupiny o 2551 hmotnost, BMI, Omron Active mobilní aplikace, kroků (4712 kroků v průměru za den), účastníci v obvod pasu, krevní sezení, neintervenované skupině ušli v průměru o 734 kroků méně Style Pro HJA‐ tlak, lipidový profil a 350IT individualizované (celkový počet kroků denně byl v průměru 3308). Účastníci hodnota glykémie cíle v intervenované skupině dosáhli větší redukce obvodu pasu, krevního tlaku, avšak nebyl prokázán signifikatní efekt zlepšení lipidového profilu nebo hladiny glykémie.
AUSDRISK
Měřené veličiny
věk ≥18 let 100 % ženy, kterým byl v posledních třech letech diagnostikován gestační diabetes
věk ≥18 let, BMI ≥ 24 kg/m2 , 62 % žen s diagnozou prediabetes
věk =55,2 (SD=9,0) , BMI= 33,3 kg/m2 (SD=6,0), 77 % žen
AUSDRISK skóre na začátku studie Vysoké riziko 21,2 %, Střední riziko 40,6 %, Nízké riziko 38,2 %
Množství účastníků Informace o účastnících na konci studie
...
Počet účastníků studie
Tabulka 1: Charakteristika vyhledaných studií.
Vlas´akov´a M., Muˇz´ık J.– Vliv fitness n´aramku na rizikov´e faktory metabolick´eho syndromu
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Kr´ atk´ y p˚ uvodn´ı ˇ cl´ anek
Klinick´ y vzdˇ el´ avac´ı simulativn´ı sc´ en´ aˇr – jak tvoˇrit spr´ avn´ y design? Lenka Vondruˇskov´ a1 , Jan Hendl1 1
ˇ a republika 1. l´ekaˇrsk´ a fakulta Univerzity Karlovy, Praha, Cesk´
Abstrakt Virtu´aln´ı klinick´e sc´en´aˇre vznikly za u ´ˇcelem podpory vzdˇel´av´an´ı a podpory rozhodovac´ıch dovednost´ı. V´yhodou tˇechto simulativn´ıch sc´en´aˇr˚ u je zpˇetn´a vazba a aktivn´ı u ´ˇcast studenta. Design tˇechto sc´en´aˇr˚ u se m˚ uˇze znaˇcnˇe liˇsit. Kontakt: Lenka Vondruˇskov´ a ˇ a republika 1. l´ ekaˇrsk´ a fakulta Univerzity Karlovy, Praha, Cesk´ Adresa: Kateˇrinsk´ a 32, 121 08 Praha2 E–mail: [email protected]
Souˇ casn´ y stav pozn´ an´ı Asociace americk´ ych l´ekaˇrsk´ ych fakult definuje virtu´aln´ıho pacienta (VP) jako specifick´ y typ poˇc´ıtaˇcov´eho programu, kter´ y simuluje re´ aln´e klinick´e sc´en´aˇre, vede studenty v roli poskytovatele zdravotn´ı p´eˇce pˇri z´ısk´av´an´ı anamn´ezy, n´ asledn´em klinick´em vyˇsetˇren´ı a pˇri stanoven´ı diagn´ozy a l´eˇcebn´eho pl´ anu. [1] V souˇcasn´e dobˇe se vyuˇz´ıvaj´ı simulativn´ı poˇc´ıtaˇcov´e klinick´e sc´en´aˇre ve zdravotnictv´ı za u ´ˇcelem v´ yuky. [2] Simulace ve vzdˇel´av´an´ı nel´ekaˇrsk´ ych zdravotnick´ ych pracovn´ık˚ u poskytuj´ı student˚ um n´ acvik v rozhodovac´ıch dovednostech na z´akladˇe re´ aln´ ych klinick´ ych situac´ı. [3] O VP technologi´ıch hovoˇr´ı Cendan, Lok: The main ” components of VPs include interactivity on the learners part (as opposed to passively watching videos), the simulation of medical condition, and the visual and/or physical presentation of the conditions. The manifestation of the VPs can differ greatly and include 1) case studies presented on webpages or CD-ROMs, 2) immersive virtual reality simulations, and 3) robotic human-scale mennequins.“ D´ale autoˇri dod´avaj´ı: Simply stated, VPs are computer” based simulation of a patient and are typically composed of three components: inputs, simulation, and outputs.“ [4]
´ cel Uˇ Vyuˇzit´ı technologi´ı ve vzdˇel´ av´ an´ı zahrnuje moˇznost vzd´alen´eho pˇr´ıstupu, okamˇzitou zpˇetnou vazbu, moˇznost aktualizace obsahu. [5] Prostˇrednictv´ım t´eto formy vzdˇel´av´an´ı jsou monitorov´ any vzdˇel´ avac´ı aktivity a tak´e dosaˇzen´e v´ ysledky. Virtu´ aln´ı vzdˇel´ avac´ı programy poIJBH – Volume 4 (2016), Issue 2
Na z´akladˇe studi´ı, ˇreˇs´ıc´ıch funkˇcn´ı design virtu´aln´ıch sc´en´aˇr˚ u, vzniklo doporuˇcen´ı pro tvorbu.
Kl´ıˇ cov´ a slova Vzdˇel´av´an´ı, simulace, klinick´y, interaktivita, zpˇetn´a vazba IJBH 2016; 4(2):40–42 zasl´ ano: 15. ˇ cervence 2016 pˇrijato: 15. srpna 2016 publikov´ ano: 20. z´ aˇr´ı 2016
skytuj´ı zkuˇsenost v rozhodov´an´ı v relativnˇe bezpeˇcn´em prostˇred´ı. [5] Autor se zab´ yval vyuˇzit´ım VP ve vzdˇel´av´an´ı farmaceutick´ ych pracovn´ık˚ u. Poukazuje na to, ˇze vyuˇzit´ı poˇc´ıtaˇcov´ ych technologi´ı ve vzdˇel´av´an´ı m˚ uˇze b´ yt stejnˇe efektivn´ı jako tradiˇcn´ı metody v´ yuky, z´aroveˇ n zmiˇ nuje, ˇze klinick´ y sc´en´aˇr je vhodn´e vyuˇz´ıt pˇredevˇs´ım pro volbu strategick´ ych rozhodnut´ı, o ˇcemˇz hovoˇr´ı tak´e Cook v kritick´e liter´arn´ı reˇserˇsi z roku 2009. [6] Issenberg, zab´ yvaj´ıc´ı se ve sv´e studii pˇrehledem medic´ınsk´ ych simulac´ı ve zveˇrejnˇen´ ych v´ ysledc´ıch, poukazuje na to, ˇze simulace usnadˇ nuj´ı vzdˇel´av´an´ı za urˇcit´ ych podm´ınek, a to: zprostˇredkov´an´ı zpˇetn´e vazby, opakov´an´ı, integrace do curicula, klinick´e variace, aktivn´ı u ´ˇcast studenta, definovan´e v´ ysledky, simulaˇcn´ı validita. [7] Cendan a Lok ve sv´e studii zmiˇ nuj´ı v´ yhody VP oproti standardn´ımu pacientovi: VP umoˇzn ˇuje stejnou zkuˇsenost opakovanˇe, sc´en´aˇre jsou dostupn´e online, poskytuj´ı zpˇetnou vazbu, umoˇzn ˇuj´ı revizi pˇredchoz´ıho rozhodnut´ı a srovn´an´ı s best-practice protokoly. D´ale vyzdvihuj´ı moˇznost zprostˇredkov´an´ı simulovan´eho vyˇsetˇren´ı pacienta ohlednˇe specifick´ ych pˇr´ıznak˚ u, jako jsou napˇr´ıklad abnorm´aln´ı dechov´e zvuky (kter´e se slovnˇe obt´ıˇznˇe vysvˇetluj´ı) ˇci specifick´e neurologick´e n´alezy. [4] Cendan a Lok popisuj´ı experiment´aln´ı teorii Kolbeho a Fryseho, tzv. cyklick´ y model uˇcen´ı se. Cyklick´ y model uˇcen´ı se je sloˇzen z nˇekolika na sebe navazuj´ıc´ıch krok˚ u: konkr´etn´ı zkuˇsenost, reflektivn´ı sledov´an´ı, abstraktn´ı konceptualizace a aktivn´ı pokus nebo pl´an. Autoˇri poukazuj´ı na to, ˇze tato teorie motivuje studenta b´ yt aktivn´ım u ´ˇcastn´ıkem v procesu. Studie zmiˇ nuje, ˇze VP je uˇziteˇcnou pom˚ uckou ve srovn´an´ı s nulovou nebo ˇz´adnou intervenc´ı, a tedy ˇze VP usnadˇ nuje proces uˇcen´ı se. [4] c
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Vondruˇskov´a L., Hendl J.– Klinick´y vzdˇel´avac´ı simulativn´ı sc´en´aˇr
Metody V roce 2013 byla provedena metaanal´ yza simulaˇcn´ıch program˚ u ve v´ yuce urgentn´ı medic´ıny. V ˇc´asti limitace studie je zm´ınˇen ˇsirok´ y stupeˇ n variace napˇr´ıˇc celou anal´ yzou, rozd´ıly se t´ ykaly instrukt´ aˇzn´ıho designu jednotliv´ ych program˚ u, jejich zamˇeˇren´ı a takt´eˇz student˚ u. V z´avˇeru je uvedeno: Technology–enhanced simulation ” for EM learners is associated with moderate or large favorable effects in comparison with no intervention and generally small and nonsignificant benefits in comparison with other instruction.“ [8] Cook zmiˇ nuje potˇrebu v´ yzkumu, kter´ y bude ˇreˇsit ot´azku, jak efektivnˇe VP implementovat [2], zmiˇ nuje potˇrebu v´ yzkumu ˇreˇs´ıc´ı tr´enink procedur´ aln´ıch u ´kon˚ u a v´ yzkum ˇreˇs´ıc´ı efektivitu simulaˇcn´ıho instrukt´aˇzn´ıho designu. [8] Ve studii Comparative effectiveness of instructional ” design features in simulation base education. Systematic review and metaanalysis“ je zm´ınˇeno nˇekolik praktick´ ych doporuˇcen´ı na z´ akladˇe identifikace charakteristick´ ych rys˚ u pro design VP. [9] Autorem identifikace rys˚ u je Issenberg. Mezi kl´ıˇcov´e rysy pro efektivn´ı instrukt´aˇzn´ı design patˇr´ı: range of difficulty, repetitive practice, dis” tributed practice, cognitive interactivity, multiple learning strategies, individualized learning, mastery learning, feedback, longer time and clinical variation.“ [9] V´ ysledky studie zamˇeˇren´e na efektivitu designu autoˇri rozdˇelili podle tzv. Kirkpatrickovy klasifikace a abstraktn´ı informace popsali n´ asledovnˇe: Spokojenost, uˇcen´ı se – ” znalosti a dovednosti, ˇcas, proces a produkt, chov´an´ı k pacientovi (ˇcas a proces) a v´ ysledky (efekt v˚ uˇci pacientovi)“. Autoˇri studie zmiˇ nuj´ı potˇrebu v´ yzkumu, kter´ y objasn´ı mechanismus efektivn´ıho simulovan´eho vzdˇel´ av´ an´ı – co fun” guje, pro koho a v jak´em kontextu“. [9]
V´ ysledky Efekt klinick´ ych virtu´ aln´ıch sc´en´ aˇr˚ u je ˇcasto porovn´av´an s ˇz´adnou ˇci nulovou intervenc´ı. [2] Cook pˇredpokl´ad´a, ˇze opakov´ an´ı do demonstrace poˇzadovan´e u ´rovnˇe, pokroˇcilost organiz´ ator˚ u a zajiˇstˇen´ı zpˇetn´e vazby mohou zlepˇsit vzdˇel´ avac´ı v´ ysledky. D´ ale v z´ avˇeru studie dod´av´a, ˇze dalˇs´ı v´ yzkum zab´ yvaj´ıc´ı se ot´ azkou, jak efektivnˇe VP implementovat, je potˇrebn´ y. [2] V z´ avˇeru dalˇs´ı studie Cook ud´av´ a: In comparison with no intervention, ” technology-enhanced simulation training in health professions education is consistently associated with large effects for outcomes of knowledge, skills and behaviours and moderate effects for patient-related outcomes.“ [10] Identifikace kl´ıˇcov´ ych rys˚ u pro tvorbu designu klinick´e simulace m˚ uˇze b´ yt doporuˇcen´ım, jak konstruovat efektivn´ı simulaˇcn´ı program. V´ ysledky studi´ı zamˇeˇren´ ych na zkoum´ an´ı efektivity virtu´aln´ıch sc´en´aˇr˚ u/pom˚ ucek v medic´ınsk´em vzdˇel´av´an´ı mohou b´ yt nejednoznaˇcn´e pro znaˇcnou variabilitu design˚ u c
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jednotliv´ ych program˚ u, diferenciaci obor˚ u a d´ale student˚ u. [3, 8] D´a se pˇredpokl´adat, ˇze pokud budeme testovat efektivitu program˚ u VP, bude vhodn´e se zamˇeˇrit na urˇcitou c´ılovou skupinu, konkr´etn´ı obor a konkr´etn´ı pˇredmˇet v´ yzkumu efektivity programu VP. Virtu´aln´ı klinick´e sc´en´aˇre a pom˚ ucky jsou pouˇz´ıv´any v l´ekaˇrsk´em i nel´ekaˇrsk´em prostˇred´ı, bude se tedy liˇsit i u ´roveˇ n znalost´ı zdravotnick´ ych pracovn´ık˚ u, l´ekaˇr˚ u, zdravotn´ıch sester, z´achran´aˇr˚ u a farmaceutick´ ych pracovn´ık˚ u.
Z´ avˇ er Pr´ace bude ˇreˇsit zpracov´an´ı kazuistik pro nel´ekaˇrsk´e zdravotnick´e obory, a to pro obor vˇseobecn´a sestra. Bude pouˇzito 5–6 klinick´ ych virtu´aln´ıch kazuistik. Kaˇzd´a bude sloˇzena ze stromu ˇreˇsen´ı dan´eho klinick´eho pˇr´ıpadu/sc´en´aˇre. Kazuistiky budou zamˇeˇreny na pracoviˇstˇe jednotky intenzivn´ı p´eˇce. N´aslednˇe bude provedeno zhodnocen´ı efektivity tohoto programu. Jelikoˇz v´ ystavba virtu´aln´ıch klinick´ ych kazuistik m˚ uˇze b´ yt finanˇcnˇe n´akladn´a, bude pouˇzito jiˇz st´avaj´ıc´ıho technick´eho ˇreˇsen´ı v prostˇred´ı OpenLabyrinth.
Podˇ ekov´ an´ı Tato pr´ace byla podpoˇrena projektem SVV 260 267 Univerzity Karlovy.
Reference [1] Association of American Medical Colleges, 2007. Effective Use of Educational Technology in Medical Education: Summary Report of the 2006 AMC Colloquium on Educational Technology. AMC, Washington DC [2] Cook, D.A., Erwin, P.J., Triola, M.M. 2010. Computerized virtual patient in health professions education: a systematic review and meta-analysis. AcadMed. 2010 Oct, 85 (10): 1589-602. Doi: 10.1097/acm.ob013e3181edfe13 [3] Kim J, Park JH, Shin S. Effectiveness of simulation-based nursing education depending on fidelity: a meta-analysis. BMC Med Educ. 2016 May 23;16(1):152. doi: 10.1186/s12909-0160672-7 [4] Cendan, J., Lok, B. The use of virtual patients in medical school curricula. Adv Physiol Educ. 2012 Mar;36(1):48-53. doi: 10.1152/advan.00054.2011 [5] Zlotos, L., Power, A., Chapman, P. A scenario-based virtual patient program to support substance misuse education. Pharm Educ. 2016 Apr. 25 80 (3) :48 [6] Cook, D.A., Triola, M.M., 2009. VPs: a critical literature review and proposed next steps. Medical Education 43, 303e311 [7] Issenberg, SB., McGaghie WC., Petrusa, ER., LeeGordon, D., Scalese, RJ. Features and uses of high-fidelity medical simulations that lead to effective learning: a BEME systematic review. MedTeach 2005 Jan, 27(1): 10-28 [8] Ilgen,J.S., Sherbino, J., Cook, DA. Technology-enhanced simulation in emergency medicine: a systematic review and meta-analysis. Acad Emerg Med. 2013 Feb;20(2):11727.doi:10.1111/acem.12076 [9] Cook, D., Hamstra, S.J., Brydges, R., Zendejas, B., Szostek, J.H., Wang, A.T., Erwin, P.J. and Hatala, R. Comparative effectiveness of instructional design features in simulation-based
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education: systematic review and metaanalysis. MedTeaCH 2013; 35: e867-898. [10] Cook, D., Hatala, R., Brydges, R., Zendejas, B., Szostek, JH.
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Wang at all. Technology enhanced simulation for health proffesions education a systematic review and metaanalysis. Jama 2011, 306: 978-88
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Abstrakt
Strukturov´ an´ı informace z klinick´ ych zpr´ av Karel Zv´ ara1,2 , Marie Tomeˇ ckov´ a2 , Jan Peleˇska2 , Vojtˇ ech Sv´ atek3 , Jana Zv´ arov´ a1,2 1
´ ˇ a republika Univerzita Karlova, 1. l´ekaˇrsk´ a fakulta, Ustav hygieny a epidemiologie, Praha, Cesk´ 2 3
ˇ a republika EuroMISE Mentor Association, Praha, Cesk´
Vysok´ a ˇskola ekonomick´ a, Fakulta informatiky a statistiky,
ˇ a republika Oddˇelen´ı informaˇcn´ıho a znalostn´ıho inˇzen´yrstv´ı, Praha, Cesk´
Kontakt: IJBH 2016; 4(2):43–44 Karel Zv´ ara ´ Ustav hygieny a epidemiologie 1. LF a VFN, ˇ a republika Univerzita Karlova, Cesk´
zasl´ ano: 15. ˇ cervence 2016 pˇrijato: 15. srpna 2016 publikov´ ano: 20. z´ aˇr´ı 2016
Adresa: Studniˇ ckova 7, 128 00 Praha 2 E–mail: [email protected]
´ Uvod
kter´e je strukturovan´a informace zapsan´a pomoc´ı k´od˚ u z vybran´ ych k´odovac´ıch syst´em˚ u. Tyto k´ody mohou b´ yt uloˇzeny k normalizovan´e klinick´e zpr´avˇe do elektronick´eho Sd´ılen´ı informac´ı o zdravotn´ım stavu pacienta a souz´aznamu a znovu pouˇzity pro podporu l´ekaˇrsk´eho rozhovisej´ıc´ıch procesech pˇri poskytov´ an´ı zdravotn´ı p´eˇce je dov´an´ı a zlepˇsen´ı kvality p´eˇce. umoˇznˇeno jen v distribuovan´em, odborn´em a kooperuj´ıc´ım prostˇred´ı. V tomto kontextu, opˇetovn´e pouˇzit´ı informac´ı, tradiˇcnˇe dokumentovan´ ych zdravotnick´ ymi pra- V´ ysledky covn´ıky v nestrukturovan´ ych heterogenn´ıch klinick´ ych zpr´av´ach, je d˚ uleˇzit´e pro kvalitu a efektivnost l´ekaˇrsk´eho 3PP metoda pˇredzpracov´an´ı klinick´ ych zpr´av byla rozhodov´an´ı. Proto vytv´ aˇren´ı n´ astroj˚ u pro z´ısk´ av´an´ı infor- ovˇeˇrena dvˇema kardiology na 49 anonymizovan´ ych klimac´ı ve tvaru dat ˇci znalost´ı, kter´e jsou v klinick´e zpr´avˇe nick´ ych zpr´av´ach z oblasti kardiologie. Prvn´ı kardiolog obsaˇzeny [1], m´a z´ asadn´ı v´ yznam pro zkvalitnˇen´ı zdravotn´ı nalezl 1500 klinick´ ych term´ın˚ u v 49 klinick´ ych zpr´av´ach p´eˇce. doplnˇen´ ych k´ody z klasifikaˇcn´ıch syst´em˚ u ICD 10, SNOMED CT, LOINC a LEKY. Druh´ y kardiolog ovˇeˇril anotaci prvn´ıho kardiologa. Ovˇeˇren´e klinick´e term´ıny a k´ody C´ıle byly uloˇzeny v datab´azi, kter´a m˚ uˇze b´ yt znovu pouˇzita pro extrakci strukturovan´ ych informac´ı z jin´ ych klinick´ ych Vyuˇzit´ı informac´ı o zdrav´ı pacienta uloˇzen´ ych zpr´av. v elektronick´ ych zdravotn´ıch z´ aznamech pro podporu l´ekaˇrsk´eho rozhodov´ an´ı a zajiˇstˇen´ı kvalitn´ı p´eˇce vyˇzaduje avˇ er pokroˇcil´e metody pro extrakci strukturovan´ ych informac´ı Z´ z klinick´ ych zpr´av. Metoda 3PP umoˇzn ˇuje extrahovat strukturovanou informaci z ˇcesk´ ych klinick´ ych zpr´av a pˇridat ji do elektroMetoda nick´eho zdravotn´ıho z´aznamu. Strukturovan´a informace m˚ uˇze zlepˇsit l´ekaˇrsk´e rozhodov´an´ı a zajistit kvalitnˇejˇs´ı Tˇr´ıf´azov´a metoda pˇredzpracov´ an´ı – 3PP metoda p´eˇci o pacienta. Stejn´ ym zp˚ usobem m˚ uˇzeme pouˇz´ıt me(Three-phase preprocessing method) klinick´ ych zpr´av je todu 3PP pro klinick´e zpr´avy zaloˇzen´e na nˇekter´em jin´em pops´ana v pr´aci [2]. V prvn´ı f´ azi je klinick´ a zpr´ava toke- pˇrirozen´em jazyku. Klinick´e zpr´avy jsou velmi d˚ uleˇzitou nizov´ana. Ve druh´e f´ azi je tokenizovan´ a klinick´a zpr´ava souˇc´ast´ı zdravotnick´e dokumentace, ale opˇetovn´e pouˇzit´ı normalizov´ana. Normalizovan´ a klinick´ a zpr´ ava je dobˇre nestrukturovan´ ych informac´ı v klinick´ ych zpr´av´ach posrozumiteln´a pro zdravotnick´e pracovn´ıky se znalost´ı moc´ı informaˇcn´ıch a komunikaˇcn´ıch technologi´ı je velmi pˇrirozen´eho jazyka pouˇz´ıvan´eho v klinick´e zpr´ avˇe. Ve tˇret´ı obt´ıˇzn´e. Proto je d˚ uleˇzit´e, ˇze lze extrahovat alespoˇ n ˇc´ast f´azi je normalizovan´ a klinick´ a zpr´ ava obohacena o extra- nestrukturovan´e informace a pˇrev´est ji do strukturovan´e hovan´e strukturovan´e informace z normalizovan´e klinick´e formy, kter´a je ˇciteln´a pro poˇc´ıtaˇce a m˚ uˇze podporovat zpr´avy. Koneˇcn´ ym v´ ysledkem tˇret´ı f´ aze metody 3PP je opakovan´e vyuˇzit´ı t´eto informace ve strukturovan´e formˇe ˇc´asteˇcnˇe strukturovan´ a normalizovan´ a klinick´ a zpr´ava, ve pro n´asledn´e zpracov´an´ı. c
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Zv´ara K. a kol.– Strukturov´an´ı informace z klinick´ych zpr´av
Kl´ıˇ cov´ a slova Klinick´a zpr´ava, tokeny, strukturovan´ a informace, klasifikaˇcn´ı syst´emy, nomenklatury
Podˇ ekov´ an´ı Pr´ace byla ˇc´asteˇcnˇe podpoˇrena grantem SVV-2016260267 Univerzity Karlovy.
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Reference [1] Zv´ arov´ a J, Vesel´ y A, Vajda I. Data, Information and Knowledge. In: Berka P, Rauch J, Zighe D.A. (Eds.) Data Mining and Medical Knowledge Management: Cases and Applications, 36 IGI Global, Hershey, 2009:1-36 [2] Zv´ ara K, Tomeˇ ckov´ a M, Peleˇska J, Sv´ atek V, Zv´ arov´ a J. Advancing electronic health record by structured information from narrative clinical reports [zasl´ ano do Methods of Information in Medicine]
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