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Facilitating phenotype transfer using a common data model

JOURNAL OF BIOMEDICAL INFORMATICS 2019年 96卷 103253-000页

作者： Hripcsak, George Shang, Ning Peissig, Peggy L. Rasmussen, Luke, V Liu, Cong Benoit, Barbara Carroll, Robert J. Carrell, David S. Denny, Joshua C. Dikilitas, Ozan Gainer, Vivian S. Howell, Kayla Marie Klann, Jeffrey G. Kullo, Iftikhar J. Lingren, Todd Mentch, Frank D. Murphy, Shawn N. Natarajan, Karthik Pacheco, Jennifer A. Wei, Wei-Qi Wiley, Ken Weng, Chunhua Columbia Univ Dept Biomed Informat New York NY 10032 USA NewYork Presbyterian Hosp Med Informat Serv New York NY USA Marshfield Clin Res Inst Ctr Precis Med Res Marshfield WI USA Northwestern Univ Feinberg Sch Med Chicago IL 60611 USA Partners Healthcare Res Informat Sci & Comp Boston MA USA Vanderbilt Univ Med Ctr Dept Biomed Informat Nashville TN USA Kaiser Permanente Washington Hlth Res Inst Seattle WA USA Vanderbilt Univ Med Ctr Dept Med Nashville TN USA Mayo Clin Dept Cardiovasc Med Rochester MN USA Vanderbilt Univ Med Ctr Vanderbilt Inst Clin & Translat Res Nashville TN USA Cincinnati Childrens Hosp Med Ctr Cincinnati OH 45229 USA Childrens Hosp Philadelphia Ctr Appl Genom Philadelphia PA 19104 USA NHGRI NIH Bethesda MD 20892 USA

Background: Implementing clinical phenotypes across a network is labor intensive and potentially error prone. Use of a common data model may facilitate the process. Methods: Electronic Medical Records and Genomics (eMERGE) sites implemented the Observational Health data Sciences and Informatics (OHDSI) Observational Medical Outcomes Partnership (OMOP) common data model across their electronic health record (EHR)-linked DNA biobanks. Two previously implemented eMERGE phenotypes were converted to OMOP and implemented across the network. Results: It was feasible to implement the common data model across sites, with laboratory data producing the greatest challenge due to local encoding. Sites were then able to execute the OMOP phenotype in less than one day, as opposed to weeks of effort to manually implement an eMERGE phenotype in their bespoke research EHR databases. Of the sites that could compare the current OMOP phenotype implementation with the original eMERGE phenotype implementation, specific agreement ranged from 100% to 43%, with disagreements due to the original phenotype, the OMOP phenotype, changes in data, and issues in the databases. Using the OMOP query as a standard comparison revealed differences in the original implementations despite starting from the same definitions, code lists, flowcharts, and pseudocode. Conclusion: Using a common data model can dramatically speed phenotype implementation at the cost of having to populate that data model, though this will produce a net benefit as the number of phenotype implementations increases. Inconsistencies among the implementations of the original queries point to a potential benefit of using a common data model so that actual phenotype code and logic can be shared, mitigating human error in reinterpretation of a narrative phenotype definition.

关键词： common data model Phenotyping Electronic health records

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Coronary Artery Computed Tomography Angiography for Preventing Cardio-Cerebrovascular Disease: Observational Cohort Study Using the Observational Health data Sciences and Informatics' common data model

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JMIR MEDICAL INFORMATICS 2022年第10期10卷 e41503页

作者： Bae, Woo Kyung Cho, Jihoon Kim, Seok Kim, Borham Baek, Hyunyoung Song, Wongeun Yoo, Sooyoung Seoul Natl Univ Dept Family Med Hlth Promot Ctr Bundang Hosp Seongnam Si Gyeonggi Do South Korea Seoul Natl Univ Healthcare Informat & Commun Technol Res Ctr Off eHlth Res & Business Bundang Hosp Seongnam Si South Korea

Background: Cardio-cerebrovascular diseases (CVDs) result in 17.5 million deaths annually worldwide, accounting for 46.2% of noncommunicable causes of death, and are the leading cause of death, followed by cancer, respiratory disease, and diabetes mellitus. Coronary artery computed tomography angiography (CCTA), which detects calcification in the coronary arteries, can be used to detect asymptomatic but serious vascular disease. It allows for noninvasive and quick testing despite involving radiation exposure. Objective: The objective of our study was to investigate the effectiveness of CCTA screening on CVD outcomes by using the Observational Health data Sciences and Informatics' Observational Medical Outcomes Partnership common data model (OMOP-CDM) data and the population-level estimation method. Methods: Using electronic health record-based OMOP-CDM data, including health questionnaire responses, adults (aged 30-74 years) without a history of CVD were selected, and 5-year CVD outcomes were compared between patients undergoing CCTA (target group) and a comparison group via 1:1 propensity score matching. Participants were stratified into low-risk and high-risk groups based on the American College of Cardiology/American Heart Association atherosclerotic cardiovascular disease (ASCVD) risk score and Framingham risk score (FRS) for subgroup analyses. Results: The 2-year and 5-year risk scores were compared as secondary outcomes between the two groups. In total, 8787 participants were included in both the target group and comparison group. No significant differences (calibration P=.37) were found between the hazard ratios of the groups at 5 years. The subgroup analysis also revealed no significant differences between the ASCVD risk scores and FRSs of the groups at 5 years (ASCVD risk score: P=.97;FRS: P=.85). However, the CCTA group showed a significantly lower increase in risk scores at 2 years (ASCVD risk score: P=.03;FRS: P=.02). Conclusions: Although we could not c

关键词： cardiovascular diseases coronary artery computed tomography angiography observational study common data model population level estimation cardiology vascular disease medical informatics computed tomography angiography electronic health record risk score health data science data modeling

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Patient-Level Fall Risk Prediction Using the Observational Medical Outcomes Partnership's common data model: Pilot Feasibility Study

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JMIR MEDICAL INFORMATICS 2022年第3期10卷 e35104页

作者： Jung, Hyesil Yoo, Sooyoung Kim, Seok Heo, Eunjeong Kim, Borham Lee, Ho-Young Hwang, Hee Seoul Natl Univ Bundang Hosp Off eHlth Res & Business 172 Dolma Ro Seongnam Si 13620 South Korea Kakao Healthcare Co Co Seongnam Si South Korea

Background: Falls in acute care settings threaten patients' safety. Researchers have been developing fall risk prediction models and exploring risk factors to provide evidence-based fall prevention practices;however, such efforts are hindered by insufficient samples, limited covariates, and a lack of standardized methodologies that aid study replication. Objective: The objectives of this study were to (1) convert fall-related electronic health record data into the standardized Observational Medical Outcome Partnership's (OMOP) common data model format and (2) develop models that predict fall risk during 2 time periods. Methods: As a pilot feasibility test, we converted fall-related electronic health record data (nursing notes, fall risk assessment sheet, patient acuity assessment sheet, and clinical observation sheet) into standardized OMOP common data model format using an extraction, transformation, and load process. We developed fall risk prediction models for 2 time periods (within 7 days of admission and during the entire hospital stay) using 2 algorithms (least absolute shrinkage and selection operator logistic regression and random forest). Results: In total, 6277 nursing statements, 747,049,486 clinical observation sheet records, 1,554,775 fall risk scores, and 5,685,011 patient acuity scores were converted into OMOP common data model format. All our models (area under the receiver operating characteristic curve 0.692-0.726) performed better than the Hendrich II Fall Risk model. Patient acuity score, fall history, age >= 60 years, movement disorder, and central nervous system agents were the most important predictors in the logistic regression models. Conclusions: To enhance model performance further, we are currently converting all nursing records into the OMOP common data model data format, which will then be included in the models. Thus, in the near future, the performance of fall risk prediction models could be improved through the application of abundan

关键词： common data model accidental falls Observational Medical Outcomes Partnership nursing records medical informatics health data electronic health record data model prediction model risk prediction fall risk

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Proposal and Assessment of a De-Identification Strategy to Enhance Anonymity of the Observational Medical Outcomes Partnership common data model (OMOP-CDM) in a Public Cloud-Computing Environment: Anonymization of Medical data Using Privacy models

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JOURNAL OF MEDICAL INTERNET RESEARCH 2020年第11期22卷 e19597页

作者： Jeon, Seungho Seo, Jeongeun Kim, Sukyoung Lee, Jeongmoon Kim, Jong-Ho Sohn, Jang Wook Moon, Jongsub Joo, Hyung Joon Korea Univ Grad Sch Informat Secur Div Informat Secur Seoul South Korea Korea Univ Res Inst Med Bigdata Sci Seoul South Korea Korea Univ Cardiovasc Ctr Dept Cardiol Seoul South Korea Korea Univ Coll Med Dept Internal Med Div Infect Dis Seoul South Korea Korea Univ Coll Med Dept Internal Med 145 Anam Ro Seoul 02841 South Korea

Background: De-identifying personal information is critical when using personal health data for secondary research. The Observational Medical Outcomes Partnership common data model (CDM), defined by the nonprofit organization Observational Health data Sciences and Informatics, has been gaining attention for its use in the analysis of patient-level clinical data obtained from various medical institutions. When analyzing such data in a public environment such as a cloud-computing system, an appropriate de-identification strategy is required to protect patient privacy. Objective: This study proposes and evaluates a de-identification strategy that is comprised of several rules along with privacy models such as k-anonymity, 1-diversity, and t-closeness. The proposed strategy was evaluated using the actual CDM database. Methods: The CDM database used in this study was constructed by the Anam Hospital of Korea University. Analysis and evaluation were performed using the ARX anonymizing framework in combination with the k-anonymity, 1-diversity, and t-closeness privacy models. Results: The CDM database, which was constructed according to the rules established by Observational Health data Sciences and Informatics, exhibited a low risk of re-identification: The highest re-identifiable record rate (11.3%) in the dataset was exhibited by the DRUG_EXPOSURE table, with a re-identification success rate of 0.03%. However, because all tables include at least one "highest risk" value of 100%, suitable anonymizing techniques are required;moreover, the CDM database preserves the "source values" (raw data), a combination of which could increase the risk of re-identification. Therefore, this study proposes an enhanced strategy to de-identify the source values to significantly reduce not only the highest risk in the k-anonymity, 1-diversity, and t-closeness privacy models but also the overall possibility of re-identification. Conclusions: Our proposed de-identification strategy effectivel

关键词： de-identification privacy anonymization common data model Observational Health data Sciences and Informatics

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IncidencePrevalence: An R package to calculate population-level incidence rates and prevalence using the OMOP common data model

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PHARMACOEPIDEMIOLOGY AND DRUG SAFETY 2024年第1期33卷 e5717-e5717页

作者： Raventos, Berta Catala, Marti Du, Mike Guo, Yuchen Black, Adam Inberg, Ger Li, Xintong Lopez-Guell, Kim Newby, Danielle de Ridder, Maria Barboza, Cesar Duarte-Salles, Talita Verhamme, Katia Rijnbeek, Peter Alhambra, Daniel Prieto Burn, Edward Fundacio Inst Univ Recerca Atencio Primaria Salut Barcelona Spain Univ Autonoma Barcelona Barcelona Spain Univ Oxford Nuffield Dept Orthopaed Rheumatol & Musculoskeleta Ctr Stat Med CSM Oxford England Odysseus Data Serv Cambridge MA USA Erasmus MC Dept Med Informat Rotterdam Netherlands Botnar Res Ctr Windmill Rd Oxford OX37LD England

Purpose: Real-world data (RWD) offers a valuable resource for generating population-level disease epidemiology metrics. We aimed to develop a well-tested and user-friendly R package to compute incidence rates and prevalence in data mapped to the observational medical outcomes partnership (OMOP) common data model (CDM).Materials and Methods: We created IncidencePrevalence, an R package to support the analysis of population-level incidence rates and point- and period-prevalence in OMOP-formatted data. On top of unit testing, we assessed the face validity of the package. To do so, we calculated incidence rates of COVID-19 using RWD from Spain (SIDIAP) and the United Kingdom (CPRD Aurum), and replicated two previously published studies using data from the Netherlands (IPCI) and the United Kingdom (CPRD Gold). We compared the obtained results to those previously published, and measured execution times by running a benchmark analysis across ***: IncidencePrevalence achieved high agreement to previously published data in CPRD Gold and IPCI, and showed good performance across databases. For COVID-19, incidence calculated by the package was similar to public data after the first-wave of the ***: For data mapped to the OMOP CDM, the IncidencePrevalence R package can support descriptive epidemiological research. It enables reliable estimation of incidence and prevalence from large real-world data sets. It represents a simple, but extendable, analytical framework to generate estimates in a reproducible and timely manner.

关键词： common data model incidence OMOP prevalence R package

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Development of Prediction models for Unplanned Hospital Readmission within 30 Days Based on common data model: A Feasibility Study

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METHODS OF INFORMATION IN MEDICINE 2021年第S 02期60卷 E65-E75页

作者： Ryu, Borim Yoo, Sooyoung Kim, Seok Choi, Jinwook Seoul Natl Univ Bundang Hosp Off eHlth Res & Business Seongnam South Korea Seoul Natl Univ Coll Med Dept Biomed Engn 28 Yongon Dong Seoul 110799 South Korea Seoul Natl Univ Med Res Ctr Inst Med & Biol Engn Seoul South Korea

Background Unplanned hospital readmission after discharge reflects low satisfaction and reliability in care and the possibility of potential medical accidents, and is thus indicative of the quality of patient care and the appropriateness of discharge plans. Objectives The purpose of this study was to develop and validate prediction models for all-cause unplanned hospital readmissions within 30 days of discharge, based on a common data model (CDM), which can be applied to multiple institutions for efficient readmission management. Methods Retrospective patient-level prediction models were developed based on clinical data of two tertiary general university hospitals converted into a CDM developed by Observational Medical Outcomes Partnership. Machine learning classification models based on the LASSO logistic regression model, decision tree, AdaBoost, random forest, and gradient boosting machine (GBM) were developed and tested by manipulating a set of CDM variables. An internal 10-fold cross-validation was performed on the target data of the model. To examine its transportability, the model was externally validated. Verification indicators helped evaluate the model performance based on the values of area under the curve (AUC). Results Based on the time interval for outcome prediction, it was confirmed that the prediction model targeting the variables obtained within 30 days of discharge was the most efficient (AUC of 82.75). The external validation showed that the model is transferable, with the combination of various clinical covariates. Above all, the prediction model based on the GBM showed the highest AUC performance of 84.140.015 for the Seoul National University Hospital cohort, yielding in 78.33 in external validation. Conclusions This study showed that readmission prediction models developed using machine-learning techniques and CDM can be a useful tool to compare two hospitals in terms of patient-data features.

关键词： readmission machine learning database patient-level prediction common data model

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Standardizing registry data to the OMOP common data model: experience from three pulmonary hypertension databases

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BMC MEDICAL RESEARCH METHODOLOGY 2021年第1期21卷 238-238页

作者： Biedermann, Patricia Ong, Rose Davydov, Alexander Orlova, Alexandra Solovyev, Philip Sun, Hong Wetherill, Graham Brand, Monika Didden, Eva-Maria Actel Pharmaceut Ltd Gewerbestr 16 CH-4123 Allschwil Switzerland Odysseus Data Serv Inc Cambridge MA USA Janssen Cilag Ltd High Wycombe Bucks England

Background The Observational Medical Outcomes Partnership (OMOP) common data model (CDM) can be used to transform observational health data to a common format. CDM transformation allows for analysis across disparate databases for the generation of new, real-word evidence, which is especially important in rare disease where data are limited. Pulmonary hypertension (PH) is a progressive, life-threatening disease, with rare subgroups such as pulmonary arterial hypertension (PAH), for which generating real-world evidence is challenging. Our objective is to document the process and outcomes of transforming registry data in PH to the OMOP CDM, and highlight challenges and our potential solutions. Methods Three observational studies were transformed from the Clinical data Interchange Standards Consortium study data tabulation model (SDTM) to OMOP CDM format. OPUS was a prospective, multi-centre registry (2014-2020) and OrPHeUS was a retrospective, multi-centre chart review (2013-2017);both enrolled patients newly treated with macitentan in the US. EXPOSURE is a prospective, multi-centre cohort study (2017-ongoing) of patients newly treated with selexipag or any PAH-specific therapy in Europe and Canada. OMOP CDM version 5.3.1 with recent OMOP CDM vocabulary was used. Imputation rules were defined and applied for missing dates to avoid exclusion of data. Custom target concepts were introduced when existing concepts did not provide sufficient granularity. Results Of the 6622 patients in the three registry studies, records were mapped for 6457. Custom target concepts were introduced for PAH subgroups (by combining SNOMED concepts or creating custom concepts) and World Health Organization functional class. Per the OMOP CDM convention, records about the absence of an event, or the lack of information, were not mapped. Excluding these non-event records, 4% (OPUS), 2% (OrPHeUS) and 1% (EXPOSURE) of records were not mapped. Conclusions SDTM data from three registries were transfor

关键词： Pulmonary hypertension Registry Observational data common data model data mapping

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Creating a common data model for Comparative Effectiveness with the Observational Medical Outcomes Partnership

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APPLIED CLINICAL INFORMATICS 2015年第3期6卷 536-547页

作者： FitzHenry, F. Resnic, F. S. Robbins, S. L. Denton, J. Nookala, L. Meeker, D. Ohno-Machado, L. Matheny, M. E. Vet Affairs Med Ctr Tennessee Valley Healthcare Syst Nashville TN 37212 USA Vanderbilt Univ Dept Biomed Informat Nashville TN 37235 USA Brigham & Womens Hosp Div Cardiol Boston MA 02115 USA Vanderbilt Univ Div Gen Internal Med & Publ Hlth Nashville TN 37235 USA RAND Corp Dept Hlth Santa Monica CA USA Univ Calif San Diego Div Biomed Informat San Diego CA 92103 USA Vanderbilt Univ Dept Biostat Nashville TN 37235 USA

Background: Adoption of a common data model across health systems is a key infrastructure requirement to allow large scale distributed comparative effectiveness analyses. There are a growing number of common data models (CDM), such as Mini-Sentinel, and the Observational Medical Outcomes Partnership (OMOP) CDMs. Objective: In this case study, we describe the challenges and opportunities of a study specific use of the OMOP CDM by two health systems and describe three comparative effectiveness use cases developed from the CDM. Methods: The project transformed two health system databases (using crosswalks provided) into the OMOP CDM. Cohorts were developed from the transformed CDMs for three comparative effectiveness use case examples. Administrative/billing, demographic, order history, medication, and laboratory were included in the CDM transformation and cohort development rules. Results: Record counts per person month are presented for the eligible cohorts, highlighting differences between the civilian and federal datasets, e. g. the federal data set had more outpatient visits per person month (6.44 vs. 2.05 per person month). The count of medications per person month reflected the fact that one system's medications were extracted from orders while the other system had pharmacy fills and medication administration records. The federal system also had a higher prevalence of the conditions in all three use cases. Both systems required manual coding of some types of data to convert to the CDM. Conclusion: The data transformation to the CDM was time consuming and resources required were substantial, beyond requirements for collecting native source data. The need to manually code subsets of data limited the conversion. However, once the native data was converted to the CDM, both systems were then able to use the same queries to identify cohorts. Thus, the CDM minimized the effort to develop cohorts and analyze the results across the sites.

关键词： common data model big data comparative effectiveness

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Integrating Clinical data and Medical Imaging in Lung Cancer: Feasibility Study Using the Observational Medical Outcomes Partnership common data model Extension

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JMIR MEDICAL INFORMATICS 2024年 12卷 e59187页

作者： Ji, Hyerim Kim, Seok Sunwoo, Leonard Jang, Sowon Lee, Ho-Young Yoo, Sooyoung Seoul Natl Univ Off eHlth Res & Business Bundang Hosp 172 Dolma Ro Seongnam Si 13605 South Korea Seoul Natl Univ Grad Sch Convergence Sci & Technol Dept Hlth Sci & Technol Seoul South Korea Seoul Natl Univ Dept Radiol Bundang Hosp Seongnam South Korea Seoul Natl Univ Dept Nucl Med Bundang Hosp Seongnam South Korea

Background: Digital transformation, particularly the integration of medical imaging with clinical data, is vital in personalized medicine. The Observational Medical Outcomes Partnership (OMOP) common data model (CDM) standardizes health data. However, integrating medical imaging remains a challenge. Objective: This study proposes a method for combining medical imaging data with the OMOP CDM to improve multimodal research. Methods: Our approach included the analysis and selection of digital imaging and communications in medicine header tags, validation of data formats, and alignment according to the OMOP CDM framework. The Fast Healthcare Interoperability Resources ImagingStudy profile guided our consistency in column naming and definitions. Imaging common data model (I-CDM), constructed using the entity-attribute-value model, facilitates scalable and efficient medical imaging data management. For patients with lung cancer diagnosed between 2010 and 2017, we introduced 4 new tables-IMAGING_STUDY, IMAGING_SERIES, IMAGING_ANNOTATION, and FILEPATH-to standardize various imaging-related data and link to clinical data. Results: This framework underscores the effectiveness of I-CDM in enhancing our understanding of lung cancer diagnostics and treatment strategies. The implementation of the I-CDM tables enabled the structured organization of a comprehensive data set, including 282,098 IMAGING_STUDY, 5,674,425 IMAGING_SERIES, and 48,536 IMAGING_ANNOTATION records, illustrating the extensive scope and depth of the approach. A scenario-based analysis using actual data from patients with lung cancer underscored the feasibility of our approach. A data quality check applying 44 specific rules confirmed the high integrity of the constructed data set, with all checks successfully passed, underscoring the reliability of our findings. Conclusions: These findings indicate that I-CDM can improve the integration and analysis of medical imaging and clinical data. By addressing the challe

关键词： DICOM OMOP CDM lung cancer medical imaging data integration data quality common data model Digital Imaging

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Feasibility of Mapping Austrian Health Claims data to the OMOP common data model

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JOURNAL OF MEDICAL SYSTEMS 2019年第10期43卷 314-314页

作者： Haberson, Andrea Rinner, Christoph Schoeberl, Alexander Gall, Walter Med Univ Vienna Ctr Med Stat Informat & Intelligent Syst Spitalgasse 23 A-1090 Vienna Austria

The Main Association of Austrian Social Security Institutions collects pseudonymized claims data from Austrian social security institutions and information about hospital stays in a database for research purposes. For new studies the same data are repeatedly reprocessed and it is difficult to compare different study results even though the data is already preprocessed and prepared in a proprietary data model. Based on a study on adverse drug events in relation to inappropriate medication in geriatric patients the suitability of the Observational Medical Outcomes Partnership (OMOP) common data model (CDM) is analyzed and data is transformed into the OMOP CDM. 1,023 (99.7%) of drug codes and 3,812 (99.2%) of diagnoses codes coincide with the OMOP vocabularies. The biggest obstacles are missing mappings for the Local Vocabularies like the Austrian pharmaceutical registration numbers and the Socio-Economic Index to the OMOP vocabularies. OMOP CDM is a promising approach for the standardization of Austrian claims data. In the long run, the benefits of standardization and reproducibility of research should outweigh this initial drawback.

关键词： Standardized health data Secondary use Claims data Drug safety common data model OMOP

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