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Building the observational medical outcomes partnership's T-MSIS Analytic File common data model

Informatics in Medicine Unlocked 2023年 39卷 101259页

作者： Williams, Nick National Library of Medicine Lister Hill National Center for Biomedical Communications Bethesda MD United States

Objectives: This effort used databricks to create an Observational Medical Outcomes Partnership (OMOP) common data model (CDM) for Transformed MSIS Analytic File (TAF) Medicaid records. Materials and methods: Our process included data volume and content assessment of TAF, translation mapping of TAF concepts to OMOP concepts and the creation of Extract Transform and Load (ETL) code. Results: The final CDM contained 119, 048, 562 individuals and 24, 806, 828,121 clinical observations from 2014 through 2018. Discussion: The transformation of TAF into OMOP can support the generation of evidence with special attention to low-income patients on public insurance. Such patients are perhaps underrepresented in academic medical center patient populations. Conclusion: Our effort successfully used databricks to transform TAF records into OMOP CDM. Our CDM can be used to generate evidence for OMOP network studies. © 2023

关键词： Centers for medicare and medicaid services common data model Medicaid Observational health data sciences and informatics Observational medical outcomes partnership

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Conversion of CPRD AURUM data into the OMOP common data model

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Informatics in Medicine Unlocked 2023年 43卷 101407页

作者： Mayer, Craig S. Lister Hill National Center for Biomedical Communication National Library of Medicine 8600 Rockville Pike Bethesda NIH Bethesda 20894 MD United States

Introduction: Efforts to standardize clinical data using common data models (CDMS) has grown in recent years. Use of CDMs allows for quicker understanding of data structure and reuse of existing tools. One CDM is the Observational Medical Outcomes Partnership (OMOP) CDM. Clinical Practice Research datalink (CPRD) is a data collection program collecting general practitioner data in the UK. Objective: Our objective was to convert a static copy of CPRD AURUM data into the OMOP CDM and run existing tools on the converted data. Methods: Two methods were used to convert each CPRD file into the OMOP CDM. The first was direct mapping used when converting CPRD files that had comparable tables in the OMOP CDM. The original names were changed to the OMOP equivalent and source values converted to standardized OMOP concepts. CPRD files: Patient (to OMOP Person), Staff (to Provider), Drug Issue (to Drug Exposure) and Practice (to Care Site) were directly mapped. The second method was indirect where for the CPRD Observation file the domain of each data row was used to assign data to proper OMOP tables or columns done by converting all source values to standard concepts. Results: The OMOP CDM conversion populated 12 tables and 20,240,453,339 rows, with the largest table being the Measurement table (5,202,579,174 data row). Mapping source values to OMOP standard concepts, we found 60.2% (46,413 of 77,149) of source concepts were also standard concepts. The Drug Exposure table had the fewest source values already in the standard form as only 4.7% (1433 of 30,194) of the source concepts were standard concepts. On a data retention level, only 2.00% of all data rows were excluded as they did not have a clear fit in the developed CDM and were not able to stand alone without additional information which was not present. Conclusion: CPRD AURUM was successfully converted into the OMOP CDM with minimal data loss. Existing OHDSI tools were used with the converted data to show efficacy of the

关键词： Clinical informatics common data model data science Real world data

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A common data model for the standardization of intensive care unit medication features

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JAMIA OPEN 2024年第2期7卷 ooae033页

作者： Sikora, Andrea Keats, Kelli Murphy, David J. Devlin, John W. Smith, Susan E. Murray, Brian Buckley, Mitchell S. Rowe, Sandra Coppiano, Lindsey Kamaleswaran, Rishikesan Univ Georgia Dept Clin & Adm Pharm Coll Pharm 1120 15th StHM 118 Augusta GA 30912 USA Augusta Univ Dept Pharm Med Ctr Augusta GA 30912 USA Emory Univ Div Pulm Allergy Crit Care & Sleep Med Atlanta GA 30322 USA Northeastern Univ Sch Pharm Boston MA 02115 USA Brigham & Womens Hosp Div Pulm & Crit Care Med 75 Francis St Boston MA 02115 USA Univ Georgia Coll Pharm Dept Clin & Adm Pharm Athens GA 30601 USA Univ North Carolina Med Ctr Dept Pharm Chapel Hill NC 27514 USA Banner Univ Med Ctr Phoenix Dept Pharm Phoenix AZ 85032 USA Oregon Hlth & Sci Univ Dept Pharm Portland OR 97239 USA Emory Univ Sch Med Dept Biomed Informat Atlanta GA 30322 USA Georgia Inst Technol Dept Biomed Engn Atlanta GA 30322 USA

Objective: common data models provide a standard means of describing data for artificial intelligence (AI) applications, but this process has never been undertaken for medications used in the intensive care unit (ICU). We sought to develop a common data model (CDM) for ICU medications to standardize the medication features needed to support future ICU AI efforts. Materials and Methods: A 9-member, multi-professional team of ICU clinicians and AI experts conducted a 5-round modified Delphi process employing conference calls, web-based communication, and electronic surveys to define the most important medication features for AI efforts. Candidate ICU medication features were generated through group discussion and then independently scored by each team member based on relevance to ICU clinical decision-making and feasibility for collection and coding. A key consideration was to ensure the final ontology both distinguished unique medications and met Findable, Accessible, Interoperable, and Reusable (FAIR) guiding principles. Results: Using a list of 889 ICU medications, the team initially generated 106 different medication features, and 71 were ranked as being core features for the CDM. Through this process, 106 medication features were assigned to 2 key feature domains: drug product-related (n=43) and clinical practice-related (n=63). Each feature included a standardized definition and suggested response values housed in the electronic data library. This CDM for ICU medications is available online. Conclusion: The CDM for ICU medications represents an important first step for the research community focused on exploring how AI can improve patient outcomes and will require ongoing engagement and refinement. Lay Summary Medication data pose a unique challenge for interpretation by artificial intelligence (AI) because of its alphanumerical combinations (eg, ibuprofen 200 mg every 4 hours) and the technical detail associated with drug prescriptions (eg, ibuprofen 200 mg and

关键词： artificial intelligence common data model critical care

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data Quality- and Utility-Compliant Anonymization of common data model-Harmonized Electronic Health Record data: Protocol for a Scoping Review

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JMIR RESEARCH PROTOCOLS 2023年 12卷 e46471页

作者： Wabo, Gaetan Kamdje Prasser, Fabian Gierend, Kerstin Siegel, Fabian Ganslandt, Thomas Heidelberg Univ Mannheim Med Fac Ctr Prevent Med & Digital Hlth Baden Wurttemberg Dept Biomed Informat Theodor Kutzer Ufer 1-3House 3Floor 4 D-68167 Mannheim Germany Charite Univ Med Berlin Berlin Inst Hlth Berlin Germany Heidelberg Univ Univ Med Ctr Mannheim Mannheim Med Fac Dept Urol & Urosurgery Mannheim Germany Friedrich Alexander Univ Erlangen Nurnberg Chair Med Informat Erlangen Germany

Background: The anonymization of common data model (CDM)-converted EHR data is essential to ensure the data privacy in the use of harmonized health care data. However, applying data anonymization techniques can significantly affect many properties of the resulting data sets and thus biases research results. Few studies have reviewed these applications with a reflection of approaches to manage data utility and quality concerns in the context of CDM-formatted health care ***: Our intended scoping review aims to identify and describe (1) how formal anonymization methods are carried out with CDM-converted health care data, (2) how data quality and utility concerns are considered, and (3) how the various CDMs differ in terms of their suitability for recording anonymized data. Methods: The planned scoping review is based on the framework of Arksey and O'Malley. By using this, only articles published in English will be included. The retrieval of literature items should be based on a literature search string combining keywords related to data anonymization, CDM standards, and data quality assessment. The proposed literature search query should be validated by a librarian, accompanied by manual searches to include further informal sources. Eligible articles will first undergo a deduplication step, followed by the screening of titles. Second, a full-text reading will allow the 2 reviewers involved to reach the final decision about article selection, while a domain expert will support the resolution of citation selection conflicts. Additionally, key information will be extracted, categorized, summarized, and analyzed by using a proposed template into an iterative process. Tabular and graphical analyses should be addressed in alignment with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) checklist. We also performed some tentative searches on Web of Science for estimating the feasibility of reaching el

关键词： EHR electronic health record data quality common data model data standard data privacy models data anonymization

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Glucagon-Like Peptide 1 Receptor Agonists and Chronic Lower Respiratory Disease Among Type 2 Diabetes Patients: Replication and Reliability Assessment Across a Research Network

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PHARMACOEPIDEMIOLOGY AND DRUG SAFETY 2025年第1期34卷 e70087页

作者： Conover, Mitchell M. Albogami, Yasser Hardin, Jill Reich, Christian G. Ostropolets, Anna Ryan, Patrick B. Observational Hlth data Sci and Informatics OHDSI Res Network Observat Hlth Data Sci & Informat New York NY 10032 USA Johnson & Johnson Observat Hlth Data Analyt Titusville NJ 08560 USA King Saud Univ Coll Pharm Dept Clin Pharm Riyadh Saudi Arabia IQVIA Real World Solut Cambridge MA USA Columbia Univ Dept Biomed Informat New York NY USA

IntroductionThe aim of this study is to use observational methods to evaluate reliability of evidence generated by a study of the effect of glucagon-like peptide 1 receptor agonists (GLP-1RA) on chronic lower respiratory disease (CLRD) outcomes among Type-2 diabetes mellitus (T2DM) *** Design and MethodsWe independently reproduced a study comparing effects of GLP-1RA versus dipeptidyl peptidase-4 inhibitors (DPP4-i) on CLRD outcomes among patients with T2DM and prior CLRD. We reproduced inputs and outputs using the original study data (national administrative claims) and evaluated the robustness of results in comparison to alternate design/analysis decisions. To evaluate generalizability, we applied an analysis protocol and conducted a meta-analysis across a research network that includes a diverse array of populations and data sources. We also produced additional analyses evaluating individual drugs within the GLP-1RA class and CLRD *** confirmed alignment of study inputs and outputs and closely reproduced effect estimates and sensitivity analyses. Adjusted effect estimates were robust to empirical calibration. Network meta-analysis confirmed original findings but indicated weaker effects than originally published. Meta-analysis of drugs within the GLP-1RA class against DPP4-i provided evidence that effects vary within the GLP-1RA class, indicating stronger effects for exenatide and weaker effects of *** study supports and establishes the reliability of the original study by (1) producing consistent findings in a range of alternate databases and populations, (2) demonstrating effects for multiple drugs within the GLP-1RA class, and (3) independently confirming the reproducibility of the original study and its findings. This reliability evaluation provides the beginnings of a broader framework for using standardized tools and distributed data networks to systematically interrogate the reliability of findings gener

关键词： chronic lower respiratory disease common data model glucagon-like peptide 1 receptor agonists pharmacoepidemiology real world data real world evidence reliability replicability reproducibility transparency

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Opportunities and Challenges in Using Electronic Health Record Systems to Study Postacute Sequelae of SARS-CoV-2 Infection: Insights From the NIH RECOVER Initiative

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JOURNAL OF MEDICAL INTERNET RESEARCH 2025年 27卷 e59217页

作者： Mandel, Hannah L. Shah, Shruti N. Bailey, L. Charles Carton, Thomas Chen, Yu Esquenazi-Karonika, Shari Haendel, Melissa Hornig, Mady Kaushal, Rainu Oliveira, Carlos R. Perlowski, Alice A. Pfaff, Emily Rao, Suchitra Razzaghi, Hanieh Seibert, Elle Thomas, Gelise L. Weiner, Mark G. Thorpe, Lorna E. Divers, Jasmin NYU Grossman Sch Med Dept Populat Hlth 180 Madison Ave New York NY 10016 USA Childrens Hosp Philadelphia Appl Clin Res Ctr Philadelphia PA USA Louisiana Publ Hlth Inst New Orleans LA USA Univ North Carolina Sch Med Dept Genet Chapel Hill NC USA Columbia Univ Mailman Sch Publ Hlth Dept Epidemiol New York NY USA Weill Cornell Med Dept Populat Hlth Sci New York NY USA Yale Univ Sch Med Dept Pediat Div Infect Dis New Haven CT USA Yale Univ Sch Publ Hlth Dept Biostat Div Hlth Informat New Haven CT USA Blooming Magnolia Los Angeles CA USA Univ North Carolina Sch Med Dept Med Chapel Hill NC USA Univ Colorado Sch Med Dept Pediat Aurora CO USA Childrens Hosp Colorado Aurora CO USA USC Dornsife Coll Letters Arts & Sci Dept Neurosci Los Angeles CA USA Case Western Reserve Univ Clin & Translat Sci Collaborat Northern Ohio Cleveland OH USA NYU Dept Fdn Med Long Isl Sch Med Mineola NY USA

The benefits and challenges of electronic health records (EHRs) as data sources for clinical and epidemiologic research have been well described. However, several factors are important to consider when using EHR data to study novel, emerging, and multifaceted conditions such as postacute sequelae of SARS-CoV-2 infection or long COVID. In this article, we present opportunities and challenges of using EHR data to improve our understanding of long COVID, based on lessons learned from the National Institutes of Health (NIH)-funded RECOVER (REsearching COVID to Enhance Recovery) Initiative, and suggest steps to maximize the usefulness of EHR data when performing long COVID research.

关键词： COVID-19 SARS-CoV-2 Long COVID, post-acute COVID-19 syndrome electronic health records machine learning public health surveillance post-infection syndrome medical informatics electronic medical record electronic health record network electronic health record data clinical research network clinical data research network common data model digital health infection respiratory infectious epidemiological pandemic

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The European Drug-Drug Interaction (EuroDDI) Study Protocol: A Cross-Country Comparison of Drug-Drug Interaction Prevalence in the Older Community-Dwelling Population

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PHARMACOEPIDEMIOLOGY AND DRUG SAFETY 2025年第1期34卷 e70092页

作者： Hughes, John E. Menditto, Enrica Mucherino, Sara Orlando, Valentina Moreno-Juste, Aida Gimeno-Miguel, Antonio Poblador-Plou, Beatriz Aza-Pascual-Salcedo, Mercedes Gonzalez-Rubio, Francisca Ioakeim-Skoufa, Ignatios Bennett, Kathleen Cahir, Caitriona RCSI Univ Med & Hlth Sci Sch Populat Hlth Dublin Ireland Univ Naples Federico II Ctr Pharmacoecon & Drug Utilizat Res Dept Pharm CIRFF Naples Italy Miguel Servet Univ Hosp Aragon Hlth Sci Inst IACS EpiChron Res Grp IIS Aragon Zaragoza 50009 Spain Inst Hlth Carlos III ISCIII Primary Care Hlth Promot RICAPPS Network Res Chron Madrid Spain Aragon Hlth Serv SALUD San Pablo Primary Care Hlth Ctr Zaragoza Spain Spanish Soc Family & Community Med semFYC Drug Utilizat Work Grp Barcelona Spain Norwegian Inst Publ Hlth Dept Drug Stat Div Hlth Data & Digitalisat Oslo Norway ISACA Emerging Technol Advisory Grp Schaumburg IL USA

BackgroundDrug-drug interactions (DDIs), highly prevalent amongst the elderly, can lead to avoidable medication-related harm. Cardiovascular and central nervous system (CNS) drugs are commonly implicated. To date, there is no consensus on how to measure DDIs, making comparisons across countries *** (i) establish a common data model (CDM) to measure DDI prevalence in the older (aged >= 70 years) community-dwelling population of three European countries and (ii) compare and describe cardiovascular and CNS DDI prevalence rates across these *** cross-country study will apply a harmonised method of DDI identification and analysis using the WHO ATC classification system and national pharmacy claims data from three European countries (Ireland, Italy, Spain). Patients aged >= 70 years dispensed >= 2 medications during 2016 will be identified from each country's national database. 'Severe' cardiovascular and CNS DDIs (i.e., may result in a life-threatening event/permanent detrimental effect) will be identified using the British National Formulary and Stockley's Drug Interactions. Two separate lists of 'severe' DDIs, per medications reimbursed, will be applied to each database: (i) DDIs relevant to each individual country and (ii) DDIs relevant to all three countries. DDIs will be defined as co-dispensed (same day) and concomitantly (+/- 7 days) *** statistics, including DDI prevalence and 95% confidence intervals, will be reported for each country. Prevalence will be pooled and compared across countries using random effects models and meta-regression, where *** EuroDDI study will develop a harmonised method to measure and compare DDI prevalence across health-related databases in Europe.

关键词： adverse drug reactions cardiovascular common data model drug interactions elderly medication safety nervous system pharmacoepidemiology polypharmacy protocol

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Comparing large scale and selected feature learning for community acquired pneumonia prognosis prediction using clinical data: a stacked ensemble approach

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SCIENTIFIC REPORTS 2025年第1期15卷 1-11页

作者： Lee, Ji Hyun Lee, Hyun Woo Lee, Hyo Jin Park, Tae Yun Jin, Kwang Nam Kim, Dong Hyun Ryu, Borim Seoul Natl Univ Dept Radiol Boramae Med Ctr Seoul Metropolitan Govt 20Boramae Ro5 Gil Seoul South Korea Seoul Natl Univ Coll Med Seoul Metropolitan Govt Boramae Med CtrDept Internal MedDiv Pulm & Crit 20 Boramae Ro5 Gil Seoul South Korea Seoul Natl Univ Biomed Res Inst Ctr Data Sci Boramae Med CtrSeoul Metropolitan Govt 20 Boramae Ro5 Gil Seoul South Korea

This study investigated and validated all-cause in-hospital death prediction models for hospitalized pneumonia patients based on large-scale clinical data, including diagnoses, medication prescriptions, and laboratory test codes. Feature selection was performed using both large-scale feature learning with a common data model (CDM) and specific pneumonia-related risk factors. A stacked ensemble mixed machine-learning model was compared with traditional machine-learning models. Accuracy, F1-score, the Area Under Precision Recall Curve (AUPRC) and the Area Under the Receiver Operating Characteristic (AUROC) were used for performance evaluation. For large-scale feature learning using a CDM, the ensemble model (LASSO LR + GBM + RF) achieved the highest performance. For the 365-day lookback, the ensemble model's AUROC was 0.867 (95% CI: 0.823-0.910), and for the 7-day lookback (AUROC 0.867, 95% CI: 0.822-0.912). In contrast, for feature learning based on selected pneumonia risk factors, among the traditional models, the RF model performed best with AUROCs of 0.774 (95% CI: 0.717-0.830) for the 365-day lookback and 0.773 (95% CI: 0.717-0.828) for the 7-days lookback. Leveraging large-scale feature learning within the CDM and using a stacked ensemble model predicts more accurately and robustly, highlighting the potential to capture complex relationships among clinical features and improve prognostic assessments.

关键词： Mortality prediction Stacked ensemble model Treatment planning Pneumonia common data model

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Antinuclear Positivity and Malignant Transformation Potential of Oral Potentially Malignant Disorder

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ORAL DISEASES 2025年第4期31卷 1107-1115页

作者： Hong, Seok Woo Kang, Jeong-Hyun Sungkyunkwan Univ Kangbuk Samsung Hosp Dept Orthoped Surg Sch Med Seoul South Korea Sungkyunkwan Univ Samsung Adv Inst Hlth Sci & Technol Seoul South Korea Ajou Univ Inst Oral Hlth Sci Clin Oral Med & Orofacial Pain Sch Med Suwon South Korea

ObjectiveThis multicenter cohort study aimed to investigate the impact of antinuclear antibody (ANA) positivity on the malignant transformation potential of oral potentially malignant disorders (OPMDs) using the Observational Health Sciences and Informatics network tools for the Observational Medical Outcomes Partnership (OMOP) common data model (CDM).Materials and MethodsRetrospective cohort data from five medical centers were analyzed. Cox regression and 1:4 propensity score matching followed by aggregated meta-analysis were used to evaluate association between ANA positivity and the risk of oral squamous cell carcinoma (OSCC). The index date was defined as the first day of diagnosis of OPMD. The target and comparator cohorts comprised patients who tested positive and negative for ANA within 180 days before or after the index date, *** 27,984 OPMD patients, 1302 patients (4.65%) confirmed malignant transformation. Among these, 3035 patients had ANA laboratory data. After 1:4 PS matching, 523 and 1598 patients with positive and negative for ANA were analyzed. Meta-analysis of the hazard ratios demonstrated that ANA positivity significantly increased the risk of malignant transformation compared to ANA *** positivity is a significant prognostic marker for predicting malignant transformation in patients with OPMD.

关键词： antinuclear antibody common data model malignant transformation oral potentially malignant disorder propensity score matching risk factor

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INSIGHT: A Tool for Fit-for-Purpose Evaluation and Quality Assessment of Standardized Observational data Sources for Real World Evidence on Medicine and Vaccine Safety

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PHARMACOEPIDEMIOLOGY AND DRUG SAFETY 2025年第1期34卷 e70089页

作者： Hoxhaj, Vjola Navarro, Constanza L. Andaur Riera-Arnau, Judit Elbers, Roel J. H. J. Alsina, Ema Dodd, Caitlin Sturkenboom, Miriam C. J. M. Univ Utrecht Univ Med Ctr Utrecht Julius Ctr Hlth Sci & Primary Care Utrecht Netherlands Univ Autonoma Barcelona Vall dHebron Hosp Univ Clin Pharmacol Serv Vall dHebron Barcelona Hosp Campus Barcelona Spain

PurposeTo describe the development of INSIGHT, a real-world data quality tool to assess completeness, consistency, and fitness-for-purpose of observational health data *** designed a three-level pipeline with data quality assessments (DQAs) to be performed in ConcePTION common data model (CDM) instances. The pipeline has been coded using *** is an open-source tool that identifies potential data quality issues in CDM-standardized instances through the systematic execution and summary of over 588 configurable DQAs. Level 1 focuses on conformance to the ConcePTION CDM specifications. Level 2 evaluates the temporal plausibility of events and uniqueness of records. Level 3 provides an overview of distributions, outliers, and trends over time to facilitate fit-for-purpose evaluation. Therefore, level 1 and 2 assure a proper data standardization, while level 3 provides information regarding the study population, and potential sub-populations. The DQAs are run locally and assessed centrally by a data quality revisor together with the data access provider's *** quality is the sum of several internal and external features of the data. While DQAs can provide reassurance about fitness-for-purpose for secondary-use data sources, improvements in data collection are essential to reduce errors and enhance overall data quality for Real World *** aims to support clinical and regulatory decision-making for medicines and vaccines by evaluating the quality of observational health data sources to support fit for purpose assessment. Assessing and improving data quality will enhance the reliability and quality of the generated *** RegistrationThis research was registered in EU PAS registration with number EU50142.

关键词： common data model data quality drug safety multi-database studies pregnancy quality checks real-world data

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