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Evidence generation from healthcare databases: recommendations for managing change

PHARMACOEPIDEMIOLOGY AND DRUG SAFETY 2016年第7期25卷 749-754页

作者： Bourke, Alison Bate, Andrew Sauer, Brian C. Brown, Jeffrey S. Hall, Gillian C. IMS Hlth 210 Pentonville Rd London N1 9JY England Pfizer Tadworth England Univ Utah Sch Med Salt Lake City UT USA Harvard Med Sch Boston MA USA Grimsdyke House London England

There is an increasing reliance on databases of healthcare records for pharmacoepidemiology and other medical research, and such resources are often accessed over a long period of time so it is vital to consider the impact of changes in data, access methodology and the environment. The authors discuss change in communication and management, and provide a checklist of issues to consider for both database providers and users. The scope of the paper is database research, and changes are considered in relation to the three main components of database research: the data content itself, how it is accessed, and the support and tools needed to use the database. Copyright (c) 2016 John Wiley & Sons, Ltd.

关键词： change management database common data model programming reproducibility sustainability pharmacoepidemiology pharmacoepidemiology

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Semantic Harmonization of Alzheimer's Disease datasets Using AD-Mapper

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JOURNAL OF ALZHEIMERS DISEASE 2024年第4期99卷 1409-1423页

作者： Wegner, Philipp Balabin, Helena Ay, Mehmet Can Bauermeister, Sarah Killin, Lewis Gallacher, John Hofmann-Apitius, Martin Salimi, Yasamin Fraunhofer Inst Algorithms & Sci Comp SCAI Dept Bioinformat D-53757 St Augustin Germany Rhein Friedrich Wilhelms Univ Bonn Bonn Germany German Ctr Neurodegenerat Dis DZNE Venusberg Campus 1 D-53121 Bonn Germany Katholieke Univ Leuven Lab Cognit Neurol Dept Neurosci Leuven Belgium Katholieke Univ Leuven Language Intelligence & Informat Retrieval Lab Dept Comp Sci Leuven Belgium Rhein Friedrich Wilhelms Univ Bonn Bonn Aachen Int Ctr IT Bonn Germany Univ Oxford Warneford Hosp Dept Psychiat Oxford England SYNAPSE Res Management Partners Barcelona Spain

Background: Despite numerous past endeavors for the semantic harmonization of Alzheimer's disease (AD) cohort studies, an automatic tool has yet to be developed. Objective: As cohort studies form the basis of data-driven analysis, harmonizing them is crucial for cross-cohort analysis. We aimed to accelerate this task by constructing an automatic harmonization tool. Methods: We created a common data model (CDM) through cross-mapping data from 20 cohorts, three CDMs, and ontology terms, which was then used to fine-tune a BioBERT model. Finally, we evaluated the model using three previously unseen cohorts and compared its performance to a string-matching baseline model. Results: Here, we present our AD-Mapper interface for automatic harmonization of AD cohort studies, which outperformed a string-matching baseline on previously unseen cohort studies. We showcase our CDM comprising 1218 unique variables. Conclusion: AD-Mapper leverages semantic similarities in naming conventions across cohorts to improve mapping performance.

关键词： Alzheimer's disease automatic data harmonization cohort study common data model data interoperability semantic mapping

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Standardizing electronic health record ventilation data in the pediatric long-term mechanical ventilator-dependent population

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PEDIATRIC PULMONOLOGY 2023年第2期58卷 433-440页

作者： Kanbar, Lara J. Dexheimer, Judith W. Zahner, Janet Burrows, Evanette K. Chatburn, Robert Messinger, Amanda Baker, Christopher D. Schuler, Christine L. Benscoter, Dan Amin, Raouf Pajor, Nathan Cincinnati Childrens Hosp Med Ctr Div Pulm Med 3333 Burnet Ave Cincinnati OH 45229 USA Cincinnati Childrens Hosp Med Ctr Div Biomed Informat Cincinnati OH 45229 USA Cincinnati Childrens Hosp Med Ctr Div Emergency Med Cincinnati OH 45229 USA Cincinnati Childrens Hosp Med Ctr Dept Informat Serv Cincinnati OH 45229 USA Childrens Hosp Philadelphia Dept Biomed & Hlth Informat Philadelphia PA 19104 USA Cleveland Clin Program Manager Enterprise Res Resp Care Cleveland OH 44106 USA Univ Colorado Dept Pediat Sect Pulm & Sleep Med Denver CO 80202 USA Univ Cincinnati Coll Med Div Hosp Med Cincinnati OH USA Univ Cincinnati Coll Med Dept Pediat Cincinnati OH USA

Background Sharing data across institutions is critical to improving care for children who are using long-term mechanical ventilation (LTMV). Mechanical ventilation data are complex and poorly standardized. This lack of data standardization is a major barrier to data sharing. Objective We aimed to describe current ventilator data in the electronic health record (EHR) and propose a framework for standardizing these data using a common data model (CDM) across multiple populations and sites. Methods We focused on a cohort of patients with LTMV dependence who were weaned from mechanical ventilation (MV). We extracted and described relevant EHR ventilation data. We identified the minimum necessary components, termed "Clinical Ideas," to describe MV from time of initiation to liberation. We then utilized existing resources and partnered with informatics collaborators to develop a framework for incorporating Clinical Ideas into the PEDSnet CDM based on the Observational Medical Outcomes Partnership (OMOP). Results We identified 78 children with LTMV dependence who weaned from ventilator support. There were 25 unique device names and 28 unique ventilation mode names used in the cohort. We identified multiple Clinical Ideas necessary to describe ventilator support over time: device, interface, ventilation mode, settings, measurements, and duration of ventilation usage per day. We used Concepts from the SNOMED-CT vocabulary and integrated an existing ventilator mode taxonomy to create a framework for CDM and OMOP integration. Conclusion The proposed framework standardizes mechanical ventilation terminology and may facilitate efficient data exchange in a multisite network. Rapid data sharing is necessary to improve research and clinical care for children with LTMV dependence.

关键词： common data model data sharing electronic health record informatics mechanical ventilation pediatrics

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Invited Commentary: Standards, Inputs, and Outputs-Strategies for Improving data-Sharing and Consortia-Based Epidemiologic Research

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AMERICAN JOURNAL OF EPIDEMIOLOGY 2022年第1期191卷 159-162页

作者： Lacey, James V., Jr. Benbow, Jennifer L. City Hope Natl Med Ctr Dept Computat & Quantitat Med Beckman Res Inst Div Hlth Analyt Duarte CA 91010 USA

data-sharing improves epidemiologic research, but the sharing of data frustrates epidemiologic researchers. The inefficiencies of current methods and options for data-sharing are increasingly documented and easily understood by any study group that has shared its data and any researcher who has received shared data. In this issue of the Journal, Temprosa et al. (Am J Epidemiol. 2022;191(1):147-158) describe how the Consortium of Metabolomics Studies (COMETS) developed and deployed a flexible analytical platform to eliminate key pain points in large-scale metabolomics research. COMETS Analytics includes an online tool, but its cloud computing and technology are the supporting rather than the leading actors in this script. The COMETS team identified the need to standardize diverse and inconsistent metabolomics and covariate data and models across its many participating cohort studies, and then developed a flexible tool that gave its member studies choices about how they wanted to meet the consortium's analytical requirements. Different specialties will have different specific research needs and will probably continue to use and develop an array of diverse analytical and technical solutions for their projects. COMETS Analytics shows how important-and enabling-the upstream attention to data standards and data consistency is to producing high-quality metabolomics, consortia-based, and large-scale epidemiology research.

关键词： common data model data analysis data harmonization data pooling epidemiologic methods metabolomics prospective studies

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The US Food and Drug Administration's Sentinel Initiative: Expanding the horizons of medical product safety

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PHARMACOEPIDEMIOLOGY AND DRUG SAFETY 2012年第sup1期21卷 9-11页

作者： Robb, Melissa A. Racoosin, Judith A. Sherman, Rachel E. Gross, Thomas P. Ball, Robert Reichman, Marsha E. Midthun, Karen Woodcock, Janet US FDA Off Med Policy Ctr Drug Evaluat & Res Silver Spring MD 20993 USA US FDA Off Surveillance & Biometr Ctr Devices & Radiol Hlth Silver Spring MD 20993 USA US FDA Off Biostat & Epidemiol Ctr Biol Evaluat & Res Rockville MD 20852 USA US FDA Off Surveillance & Epidemiol Ctr Drug Evaluat & Res Silver Spring MD 20993 USA

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DQueST: dynamic questionnaire for search of clinical trials

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2019年第11期26卷 1333-1343页

作者： Liu, Cong Yuan, Chi Butler, Alex M. Carvajal, Richard D. Li, Ziran Ryan Ta, Casey N. Weng, Chunhua Columbia Univ Dept Biomed Informat 622 W 168 StPH-20Room 407 New York NY 10032 USA Columbia Univ Dept Med New York NY 10032 USA

Objective: Information overload remains a challenge for patients seeking clinical trials. We present a novel system (DQueST) that reduces information overload for trial seekers using dynamic questionnaires. Materials and Methods: DQueST first performs information extraction and criteria library curation. DQueST transforms criteria narratives in the *** repository into a structured format, normalizes clinical entities using standard concepts, clusters related criteria, and stores the resulting curated library. DQueST then implements a real-time dynamic question generation algorithm. During user interaction, the initial search is similar to a standard search engine, and then DQueST performs real-time dynamic question generation to select criteria from the library 1 at a time by maximizing its relevance score that reflects its ability to rule out ineligible trials. DQueST dynamically updates the remaining trial set by removing ineligible trials based on user responses to corresponding questions. The process iterates until users decide to stop and begin manually reviewing the remaining trials. Results: In simulation experiments initiated by 10 diseases, DQueST reduced information overload by filtering out 60%-80% of initial trials after 50 questions. Reviewing the generated questions against previous answers, on average, 79.7% of the questions were relevant to the queried conditions. By examining the eligibility of random samples of trials ruled out by DQueST, we estimate the accuracy of the filtering procedure is 63.7%. In a study using 5 mock patient profiles, DQueST on average retrieved trials with a 1.465 times higher density of eligible trials than an existing search engine. In a patient-centered usability evaluation, patients found DQueST useful, easy to use, and returning relevant results. Conclusion: DQueST contributes a novel framework for transforming free-text eligibility criteria to questions and filtering out clinical trials based on user ans

关键词： clinical trial search natural language processing common data model interactive search eligibility criteria

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Distributed data networks: a blueprint for Big data sharing and healthcare analytics

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ANNALS OF THE NEW YORK ACADEMY OF SCIENCES 2017年第1期1387卷 105-111页

作者： Popovic, Jennifer R. Harvard Med Sch Harvard Pilgrim Hlth Care Inst Boston MA 02115 USA

This paper defines the attributes of distributed data networks and outlines the data and analytic infrastructure needed to build and maintain a successful network. We use examples from one successful implementation of a large-scale, multisite, healthcare-related distributed data network, the U.S. Food and Drug Administration-sponsored Sentinel Initiative. Analytic infrastructure-development concepts are discussed from the perspective of promoting six pillars of analytic infrastructure: consistency, reusability, flexibility, scalability, transparency, and reproducibility. This paper also introduces one use case for machine learning algorithm development to fully utilize and advance the portfolio of population health analytics, particularly those using multisite administrative data sources.

关键词： distributed data network common data model machine learning algorithm healthcare analytics Big data

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OHDSI Standardized Vocabularies-a large-scale centralized reference ontology for international data harmonization

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JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION 2024年第3期31卷 583-590页

作者： Reich, Christian Ostropolets, Anna Ryan, Patrick Rijnbeek, Peter Schuemie, Martijn Davydov, Alexander Dymshyts, Dmitry Hripcsak, George Observat Hlth Data Sci & Informat Coordinating Ctr New York NY 10032 USA Northeastern Univ OHDSI Ctr Roux Inst 100 Fore St Portland ME 04101 USA Erasmus MC Dept Med Informat NL-3015 GD Rotterdam Netherlands Columbia Univ Med Ctr Dept Biomed Informat New York NY 10032 USA Odysseus Data Serv Cambridge MA 02142 USA Janssen Res & Dev Observat Hlth Data Analyt Titusville NJ 08560 USA

Importance The Observational Health data Sciences and Informatics (OHDSI) is the largest distributed data network in the world encompassing more than 331 data sources with 2.1 billion patient records across 34 countries. It enables large-scale observational research through standardizing the data into a common data model (CDM) (Observational Medical Outcomes Partnership [OMOP] CDM) and requires a comprehensive, efficient, and reliable ontology system to support data *** and methods We created the OHDSI Standardized Vocabularies-a common reference ontology mandatory to all data sites in the network. It comprises imported and de novo-generated ontologies containing concepts and relationships between them, and the praxis of converting the source data to the OMOP CDM based on these. It enables harmonization through assigned domains according to clinical categories, comprehensive coverage of entities within each domain, support for commonly used international coding schemes, and standardization of semantically equivalent *** The OHDSI Standardized Vocabularies comprise over 10 million concepts from 136 vocabularies. They are used by hundreds of groups and several large data networks. More than 8600 users have performed 50 000 downloads of the system. This open-source resource has proven to address an impediment of large-scale observational research-the dependence on the context of source data representation. With that, it has enabled efficient phenotyping, covariate construction, patient-level prediction, population-level estimation, and standard *** and conclusion OHDSI has made available a comprehensive, open vocabulary system that is unmatched in its ability to support global observational research. We encourage researchers to exploit it and contribute their use cases to this dynamic resource.

关键词： OHDSI controlled vocabulary common data model observational data

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Association Between Ursodeoxycholic Acid and Clinical Outcomesin Patients With COVID-19 Infection:Population-Based CohortStudy

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JMIR PUBLIC HEALTH AND SURVEILLANCE 2024年 10卷 e59274页

作者： Lee, Hyunjun Kim, Min Gul Yeom, Sang Woo Noh, Sang Jae Jeong, Cho Yun Kim, Minji Kang, Min Gu Ko, Ji Hoon Park, Su Cheol Kweon, Hyeok Tae Sim, Sang Il Lee, Hyun You, Yeon Seok Kim, Jong Seung Jeonbuk Natl Univ Med Sch Dept Otorhinolaryngol Head & Neck Surg Jeonju South Korea Jeonbuk Natl Univ Med Sch Dept Pharmacol Jeonju South Korea Jeonbuk Natl Univ Med Sch Dept Med Informat Jeonju South Korea Jeonbuk Natl Univ Jeonbuk Natl Univ Hosp Res Inst Clin Med Biomed Res Inst Jeonju South Korea Jeonbuk Natl Univ Hosp Big Data Ctr Jeonju South Korea Hanyang Univ Coll Med Dept Internal Med Seoul South Korea Jeonbuk Natl Univ Sch Med & Hosp Dept Med Informat Dept Otorhinolaryngol 20 Geonji Ro Jeonju 54907 Jeonbuk Do South Korea

Background: Several studies have investigated the relationship between ursodeoxycholic acid (UDCA) and COVID-19 ***, complex and conflicting results have generated confusion in the application of these results. Objective: We aimed to investigate whether the association between UDCA and COVID-19 infection can also be demonstratedthrough the analysis of a large-scale cohort. Methods: This retrospective study used local and nationwide cohorts, namely, the Jeonbuk National University Hospital intothe Observational Medical Outcomes Partnership common data model cohort (JBUH CDM) and the Korean National HealthInsurance Service claim-based database (NHIS). We investigated UDCA intake and its relationship with COVID-19 susceptibilityand severity using validated propensity score ***: Regarding COVID-19 susceptibility, the adjusted hazard ratio (aHR) value of the UDCA intake was significantlylowered to 0.71 in the case of the JBUH CDM (95% CI 0.52-0.98) and was significantly lowered to 0.93 (95% CI 0.90-0.96) inthe case of the NHIS. Regarding COVID-19 severity, the UDCA intake was found to be significantly lowered to 0.21 (95% CI0.09-0.46) in the case of JBUH CDM. Furthermore, the aHR value was significantly lowered to 0.77 in the case of NHIS (95%CI 0.62-0.95). Conclusions: Using a large-scale local and nationwide cohort, we confirmed that UDCA intake was significantly associatedwith reductions in COVID-19 susceptibility and severity. These trends remained consistent regardless of the UDCA dosage. Thissuggests the potential of UDCA as a preventive and therapeutic agent for COVID-19 infection.

关键词： Covid 19 COVID-19 ursodeoxycholic acid population-based cohort study SARS-CoV-2 Coronavirus pandemic population-based retrospective cohort study propensity score UDCA public health common data model clinical severity

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How pharmacoepidemiology networks can manage distributed analyses to improve replicability and transparency and minimize bias

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PHARMACOEPIDEMIOLOGY AND DRUG SAFETY 2020年第Sup1期29卷 3-7页

作者： Platt, Robert W. Platt, Richard Brown, Jeffrey S. Henry, David A. Klungel, Olaf H. Suissa, Samy McGill Univ Dept Epidemiol Biostat & Occupat Hlth 1020 Pine Ave W Montreal PQ H3A 1A2 Canada Jewish Gen Hosp Ctr Clin Epidemiol Lady Davis Res Inst Montreal PQ Canada McGill Univ Ctr Hlth Res Inst Ctr Hlth Outcomes Res Montreal PQ Canada Harvard Med Sch Dept Populat Med Boston MA 02115 USA Harvard Pilgrim Hlth Care Inst Boston MA USA Bond Univ Ctr Res Evidence Based Practice Gold Coast Australia Univ Melbourne Melbourne Sch Populat & Global Hlth Melbourne Vic Australia Inst Clin & Evaluat Sci Toronto ON Canada Univ Utrecht Utrecht Inst Pharmaceut Sci Div Pharmacoepidemiol & Clin Pharmacol Utrecht Netherlands Univ Med Ctr Utrecht Julius Ctr Hlth Sci & Primary Care Utrecht Netherlands

Several pharmacoepidemiology networks have been developed over the past decade that use a distributed approach, implementing the same analysis at multiple data sites, to preserve privacy and minimize data sharing. Distributed networks are efficient, by interrogating data on very large populations. The structure of these networks can also be leveraged to improve replicability, increase transparency, and reduce bias. We describe some features of distributed networks using, as examples, the Canadian Network for Observational Drug Effect Studies, the Sentinel System in the USA, and the European Research Network of Pharmacovigilance and Pharmacoepidemiology. common protocols, analysis plans, and data models, with policies on amendments and protocol violations, are key features. These tools ensure that studies can be audited and repeated as necessary. Blinding and strict conflict of interest policies reduce the potential for bias in analyses and interpretation. These developments should improve the timeliness and accuracy of information used to support both clinical and regulatory decisions.

关键词： bias common data model distributed networks pharmacoepidemiology protocol

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