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Identification of risk factors and clinical outcomes for symmetric peripheral gangrene: A new scoring system using a common data model database

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JOURNAL OF ORTHOPAEDIC SURGERY 2024年第2期32卷

作者： Deslivia, Maria Florencia Kim, Do-Hoon Lee, Suk-Joong Kim, Hee-June Kholinne, Erica Lee, Hyun-Joo Kyungpook Natl Univ Sch Med & Hosp Dept Orthopaed Surg 130 Dongduk Ro Daegu 700721 South Korea St Carolus Hosp Dept Orthoped Surg Jakarta Indonesia Kyungpook Natl Univ Hosp Med Big Data Res Ctr Daegu South Korea Daejeon Eulji Med Ctr Dept Nucl Med Daejeon South Korea Gyeongsang Natl Univ Dept Orthopaed Surg Sch Med Chang Won South Korea Gyeongsang Natl Univ Changwon Hosp Chang Won South Korea Univ Trisakti St Carolus Hosp Fac Med Dept Orthoped Surg Jakarta Indonesia

Background: Symmetrical peripheral gangrene (SPG) is a destructive clinical condition where amputation is often the final treatment option. We aimed to identify the predictors of SPG using the common data model (CDM) and propose a new scoring system for predicting hospitalized patients at risk of developing SPG. Methods: A cohort of patients treated with intravenous noradrenaline, epinephrine, and vasopressin between 2011 and 2020 was retrospectively analyzed using the CDM database. The main outcome was amputation performed as a resuscitation measure. We investigated risk factors including demographic characteristics, comorbidities, and preoperative laboratory values. Based on demographic variables such as age and sex, a 1:10 propensity score matching (PSM) was performed. The odds ratio (OR) was calculated using logistic regression analysis. Results: Amputation was performed in 308 (0.4%) patients out of a cohort of 73,902 patients. Age, sex, hypertension, diabetes mellitus (DM), renal disease (RD), heart failure, anemia, hypercholesterolemia, peripheral vascular disease (PVD), and laboratory markers such as albumin, eosinophils, hematocrit, lymphocytes, monocytes, neutrophils, ESR, aPTT, creatinine, and BUN were statistically significant. Logistic regression analysis revealed statistically significant differences in DM (OR 5.51), RD (OR 2.90), PVD (OR 9.67), and cerebrovascular disease (CVD) (OR 0.49). Compared to the group without amputation, logistic regression analysis after matching the age and sex group with 1:10 PSM showed statistically significant results in DM (OR 3.59), RD (OR 2.59), PVD (OR 7.76), and CVD (OR 0.40). Conclusion: Early recognition of high-risk patients may help medical providers prevent severe outcomes, including amputation surgery.

关键词： common data model gangrene risk factors

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Transforming Anesthesia data Into the Observational Medical Outcomes Partnership common data model: Development and Usability Study

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JOURNAL OF MEDICAL INTERNET RESEARCH 2021年第10期23卷 e29259页

作者： Lamer, Antoine Abou-Arab, Osama Bourgeois, Alexandre Parrot, Adrien Popoff, Benjamin Beuscart, Jean-Baptiste Tavernier, Benoit Moussa, Mouhamed Djahoum Univ Lille CHU Lille ULR METRICS Evaluat Technol Sante & Prat Med 2694 2 Pl Verdun F-59000 Lille France InterHop Paris France Univ Lille Fac Ingn & Management Sante Lille France Amiens Picardie Univ Hosp Dept Anaesthesiol & Crit Care Med Amiens France Reg Univ Hosp Nancy Dept Anesthesiol & Crit Care Med Nancy France Rouen Univ Hosp Dept Anaesthesiol & Crit Care Rouen France CHU Lille Dept Anesthesiol & Crit Care Lille France

Background: Electronic health records (EHRs, such as those created by an anesthesia management system) generate a large amount of data that can notably be reused for clinical audits and scientific research. The sharing of these data and tools is generally affected by the lack of system interoperability. To overcome these issues, Observational Health data Sciences and Informatics (OHDSI) developed the Observational Medical Outcomes Partnership (OMOP) common data model (CDM) to standardize EHR data and promote large-scale observational and longitudinal research. Anesthesia data have not previously been mapped into the OMOP CDM. Objective: The primary objective was to transform anesthesia data into the OMOP CDM. The secondary objective was to provide vocabularies, queries, and dashboards that might promote the exploitation and sharing of anesthesia data through the CDM. Methods: Using our local anesthesia data warehouse, a group of 5 experts from 5 different medical centers identified local concepts related to anesthesia. The concepts were then matched with standard concepts in the OHDSI vocabularies. We performed structural mapping between the design of our local anesthesia data warehouse and the OMOP CDM tables and fields. To validate the implementation of anesthesia data into the OMOP CDM, we developed a set of queries and dashboards. Results: We identified 522 concepts related to anesthesia care. They were classified as demographics, units, measurements, operating room steps, drugs, periods of interest, and features. After semantic mapping, 353 (67.7%) of these anesthesia concepts were mapped to OHDSI concepts. Further, 169 (32.3%) concepts related to periods and features were added to the OHDSI vocabularies. Then, 8 OMOP CDM tables were implemented with anesthesia data and 2 new tables (EPISODE and FEATURE) were added to store secondarily computed data. We integrated data from 5,72,609 operations and provided the code for a set of 8 queries and 4 dashboards relate

关键词： data reuse common data model Observational Medical Outcomes Partnership anesthesia data warehouse reproducible research

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Long-term use of proton-pump inhibitor on Alzheimer's disease: a real-world distributed network analysis of six observational Korean databases using a common data model

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THERAPEUTIC ADVANCES IN NEUROLOGICAL DISORDERS 2022年 15卷 17562864221135700页

作者： Kim, Yerim Seo, Seung In Lee, Kyung Joo Kim, Jinseob Yoo, Jong Jin Seo, Won-Woo Lee, Hyung Seok Shin, Woon Geon Hallym Univ Kangdong Sacred Heart Hosp Coll Med Dept Internal MedDiv Gastroenterol 150 Seongan Ro Seoul 05355 South Korea Hallym Univ Inst Liver & Digest Dis Chunchon South Korea Hallym Univ Kangdong Sacred Heart Hosp Coll Med Dept Neurol Seoul South Korea Hallym Univ Kangdong Sacred Heart Hosp Coll Med Dept Internal MedDiv Gastroenterol Seoul South Korea Hallym Univ Univ Ind Fdn Chunchon South Korea Seoul Natl Univ Sch Publ Hlth Dept Epidemiol Seoul South Korea Hallym Univ Kangdong Sacred Heart Hosp Coll Med Dept Internal MedDiv Rheumatol Seoul South Korea Hallym Univ Kangdong Sacred Heart Hosp Coll Med Dept Internal MedDiv Cardiol Seoul South Korea Hallym Univ Sacred Heart Hosp Coll Med Dept Internal MedDiv Nephrol Anyang South Korea

Background:Dementia has a crucial impact on the quality of life of elderly patients and their caregivers. Proton-pump inhibitors (PPIs) are the most frequently prescribed treatment, but they have been shown to be associated with dementia. The data are inconsistent, however. Objective:To investigate the association between PPIs use and Alzheimer's disease (AD) or all-cause dementia in six observational Korean databases using a common data model (CDM) and to perform a distributed network analysis. Methods:Subjects aged over 18 years between 1 January 2004 and 31 December 2020. Among 7,293,565 subjects from 6 cohorts, 41,670 patients met the eligibility criteria. A total of 2206 patients who were included in both cohorts or with a history of dementia were excluded. After propensity matching, 5699 propensity-matched pairs between the PPIs and histamine-2 receptor antagonist (H(2)RA) users were included in this study. The primary outcome was the incidence of AD at least 365 days after drug exposure. The secondary outcome was the incidence of all-cause dementia at least 365 days after drug exposure. Results:In the 1:1 propensity score matching, the risk of AD or all-cause dementia was not significantly different between the PPIs and H(2)RA groups in all six databases. In the distributed network analysis, the long-term PPI users (> 365 days) were unassociated with AD [hazard ratio (HR) = 0.92, 95% confidence interval (CI) = 0.68-1.23;I-2 = 0%] and all-cause dementia (HR =1.04, 95% CI = 0.82-1.31;I-2 = 0%) compared with H(2)RA users. Conclusion:In the distributed network analysis of six Korean hospital databases using Observational Medical Outcomes Partnership (OMOP)-CDM data, the long-term use of PPI was not associated with a statistically significantly increased risk of AD or all-cause dementia. Therefore, we suggest that physicians should not avoid these medications because of concern about dementia risk.

关键词： Alzheimer disease common data model dementia H-2 receptor antagonist proton-pump inhibitors

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Calculating daily dose in the Observational Medical Outcomes Partnership common data model

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PHARMACOEPIDEMIOLOGY AND DRUG SAFETY 2024年第6期33卷 e5809-e5809页

作者： Burkard, Theresa Lopez-Guell, Kim Gorbachev, Artem Bellas, Lucia Jodicke, Annika M. Burn, Edward de Ridder, Maria Mosseveld, Mees Gratton, Jasmine Seager, Sarah Vojinovic, Dina Mayer, Miguel Angel Ramirez-Anguita, Juan Manuel Machin, Angela Leis Oja, Marek Kolde, Raivo Bonadt, Klaus Prieto-Alhambra, Daniel Reich, Chistian Catala, Marti Univ Oxford Nuffield Dept Orthopaed Rheumatol & Musculoskeleta Windmill Rd Oxford OX3 7LD England Odysseus Data Serv Cambridge MA USA Hosp Univ Vall dHebron Clin Pharmacol Serv Barcelona Spain Erasmus MC Dept Med Informat Rotterdam Netherlands IQVIA Ltd London England IQVIA Solut BV Amsterdam Netherlands Hosp del Mar Barcelona Management & Control Dept Barcelona Spain Hosp del Mar Res Inst IMIM Res Program Biomed Informat GRIB Barcelona Spain Univ Pompeu Fabra Barcelona Spain Univ Tartu Inst Comp Sci Tartu Estonia IQVIA Commercial GmbH & Co OHG Frankfurt Germany

Purpose: We aimed to develop a standardized method to calculate daily dose (i.e., the amount of drug a patient was exposed to per day) of any drug on a global scale using only drug information of typical observational data in the Observational Medical Outcomes Partnership common data model (OMOP CDM) and a single reference table from Observational Health data Sciences And Informatics (OHDSI). Materials and Methods: The OMOP DRUG_STRENGTH reference table contains information on the strength or concentration of drugs, whereas the OMOP DRUG_EXPOSURE table contains information on patients' drug prescriptions or dispensations/claims. Based on DRUG_EXPOSURE data from the primary care databases Clinical Practice Research datalink GOLD (United Kingdom) and Integrated Primary Care Information (IPCI, The Netherlands) and healthcare claims from PharMetrics (R) Plus for Academics (USA), we developed four formulas to calculate daily dose given different DRUG_STRENGTH reference table information. We tested the dose formulas by comparing the calculated median daily dose to the World Health Organization (WHO) Defined Daily Dose (DDD) for six different ingredients in those three databases and additional four international databases representing a variety of healthcare settings: MAITT (Estonia, healthcare claims and discharge summaries), IQVIA Disease Analyzer Germany (outpatient data), IQVIA Longitudinal Patient database Belgium (outpatient data), and IMASIS Parc Salut (Spain, hospital data). Finally, in each database, we assessed the proportion of drug records for which daily dose calculations were possible using the suggested formulas. Results: Applying the dose formulas, we obtained median daily doses that generally matched the WHO DDD definitions. Our dose formulas were applicable to >85% of drug records in all but one of the assessed databases. Conclusion: We have established and implemented a standardized daily dose calculation in OMOP CDM providing reliable and reproducible r

关键词： common data model dose OMOP WHO DDD

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Transformation and Evaluation of the MIMIC database in the OMOP common data model: Development and Usability Study

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JMIR MEDICAL INFORMATICS 2021年第12期9卷 e30970页

作者： Paris, Nicolas Lamer, Antoine Parrot, Adrien InterHop 30 Ave Maine F-75015 Paris France Univ Lille ULR 2694 METRICS Evaluat Technol Sante & Prat Med CHU Lille Lille France

Background: In the era of big data, the intensive care unit (ICU) is likely to benefit from real-time computer analysis and modeling based on close patient monitoring and electronic health record data. The Medical Information Mart for Intensive Care (MIMIC) is the first open access database in the ICU domain. Many studies have shown that common data models (CDMs) improve database searching by allowing code, tools, and experience to be shared. The Observational Medical Outcomes Partnership (OMOP) CDM is spreading all over the world. Objective: The objective was to transform MIMIC into an OMOP database and to evaluate the benefits of this transformation for analysts. Methods: We transformed MIMIC (version 1.4.21) into OMOP format (version 5.3.3.1) through semantic and structural mapping. The structural mapping aimed at moving the MIMIC data into the right place in OMOP, with some data transformations. The mapping was divided into 3 phases: conception, implementation, and evaluation. The conceptual mapping aimed at aligning the MIMIC local terminologies to OMOP's standard ones. It consisted of 3 phases: integration, alignment, and evaluation. A documented, tested, versioned, exemplified, and open repository was set up to support the transformation and improvement of the MIMIC community's source code. The resulting data set was evaluated over a 48-hour datathon. Results: With an investment of 2 people for 500 hours, 64% of the data items of the 26 MIMIC tables were standardized into the OMOP CDM and 78% of the source concepts mapped to reference terminologies. The model proved its ability to support community contributions and was well received during the datathon, with 160 participants and 15,000 requests executed with a Conclusions: The resulting MIMIC-OMOP data set is the first MIMIC-OMOP data set available free of charge with real disidentified data ready for replicable intensive care research. This approach can be generalized to any medical field.

关键词： data reuse open data OMOP common data model critical care machine learning big data health informatics health data health database electronic health records open access database digital health intensive care health care

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Transforming Primary Care data Into the Observational Medical Outcomes Partnership common data model: Development and Usability Study

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JMIR MEDICAL INFORMATICS 2024年 12卷 e49542页

作者： Fruchart, Mathilde Quindroit, Paul Jacquemont, Chloe Beuscart, Jean-Baptiste Calafiore, Matthieu Lamer, Antoine Univ Lille CHU Lille ULR 2694 METRICS Evaluat Technol Sante & Prat Med 2 Pl Verdun F-59000 Lille France Univ Lille Dept Medecine Gen Lille France F2RSM Psy Federat Reg Rech Psychiat & St Mentale H Lille France

Background: Patient-monitoring software generates a large amount of data that can be reused for clinical audits and scientific research. The Observational Health data Sciences and Informatics (OHDSI) consortium developed the Observational Medical Outcomes Partnership (OMOP) common data model (CDM) to standardize electronic health record data and promote large-scale observational and longitudinal research. Objective: This study aimed to transform primary care data into the OMOP CDM format. Methods: We extracted primary care data from electronic health records at a multidisciplinary health center in Wattrelos, France. We performed structural mapping between the design of our local primary care database and the OMOP CDM tables and fields. Local French vocabularies concepts were mapped to OHDSI standard vocabularies. To validate the implementation of primary care data into the OMOP CDM format, we applied a set of queries. A practical application was achieved through the development of a dashboard. Results: data from 18,395 patients were implemented into the OMOP CDM, corresponding to 592,226 consultations over a period of 20 years. A total of 18 OMOP CDM tables were implemented. A total of 17 local vocabularies were identified as being related to primary care and corresponded to patient characteristics (sex, location, year of birth, and race), units of measurement, biometric measures, laboratory test results, medical histories, and drug prescriptions. During semantic mapping, 10,221 primary care concepts were mapped to standard OHDSI concepts. Five queries were used to validate the OMOP CDM by comparing the results obtained after the completion of the transformations with the results obtained in the source software. Lastly, a prototype dashboard was developed to visualize the activity of the health center, the laboratory test results, and the drug prescription data. Conclusions: Primary care data from a French health care facility have been implemented into the OMOP CDM

关键词： data reuse Observational Medical Outcomes Partnership common data model data warehouse reproducible research primary care dashboard electronic health record patient tracking system patient monitoring EHR primary care data

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Assessing the Use of German Claims data Vocabularies for Research in the Observational Medical Outcomes Partnership common data model: Development and Evaluation Study

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JMIR MEDICAL INFORMATICS 2023年 11卷 e47959页

作者： Henke, Elisa Zoch, Michele Kallfelz, Michael Ruhnke, Thomas Leutner, Liz Annika Spoden, Melissa Guenster, Christian Math, Dipl Sedlmayr, Martin Bathelt, Franziska Tech Univ Dresden Inst Med Informat & Biometry Carl Gustav Carus Fac Med Dresden Germany Odysseus Data Serv GmbH Berlin Germany AOK Res Inst Wissensch Inst AOK Berlin Germany Thiem Res GmbH Cottbus Germany Tech Univ Dresden Carl Gustav Carus Fac Med Inst Med Informat & Biometry Fetscherstr 74 D-01307 Dresden Germany

Background: National classifications and terminologies already routinely used for documentation within patient care settings enable the unambiguous representation of clinical information. However, the diversity of different vocabularies across health care institutions and countries is a barrier to achieving semantic interoperability and exchanging data across sites. The Observational Medical Outcomes Partnership (OMOP) common data model (CDM) enables the standardization of structure and medical terminology. It allows the mapping of national vocabularies into so-called standard concepts, representing normative expressions for international analyses and research. Within our project "Hybrid Quality Indicators Using Machine Learning Methods" (Hybrid-QI), we aim to harmonize source codes used in German claims data vocabularies that are currently unavailable in the OMOP ***: This study aims to increase the coverage of German vocabularies in the OMOP CDM. We aim to completely transform the source codes used in German claims data into the OMOP CDM without data loss and make German claims data usable for OMOP CDM-based ***: To prepare the missing German vocabularies for the OMOP CDM, we defined a vocabulary preparation approach consisting of the identification of all codes of the corresponding vocabularies, their assembly into machine-readable tables, and the translation of German designations into English. Furthermore, we used 2 proposed approaches for OMOP-compliant vocabulary preparation: the mapping to standard concepts using the Observational Health data Sciences and Informatics (OHDSI) tool Usagi and the preparation of new 2-billion concepts (ie, concept_id >2 billion). Finally, we evaluated the prepared vocabularies regarding completeness and correctness using synthetic German claims data and calculated the coverage of German claims data vocabularies in the OMOP ***: Our vocabulary preparation approach was able to map 3 missing German vo

关键词： OMOP CDM interoperability vocabularies claims data OHDSI Observational Medical Outcomes Partnership common data model Observational Health data Sciences and Informatics

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Effect of Age on the Initiation of Biologic Agent Therapy in Patients With Inflammatory Bowel Disease: Korean common data model Cohort Study

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JMIR MEDICAL INFORMATICS 2020年第4期8卷 e15124页

作者： Choi, Youn, I Kim, Yoon Jae Chung, Jun-Won Kim, Kyoung Oh Kim, Hakki Park, Rae Woong Park, Dong Kyun Gachon Univ Coll Internal Med Gil Med Ctr Dept Gastroenterol 21Namdong Daero 774 Beon Gil Incheon 405760 South Korea Gachon Univ Hlth IT Res Ctr Gil Med Ctr Incheon South Korea Ajou Med Ctr Suwon South Korea

Background: The Observational Health data Sciences and Informatics (OHDSI) network is an international collaboration established to apply open-source data analytics to a large network of health databases, including the Korean common data model (K-CDM) network. Objective: The aim of this study is to analyze the effect that age at diagnosis has on the prognosis of inflammatory bowel disease (IBD) in Korea using a CDM network database. Methods: We retrospectively analyzed the K-CDM network database from 2005 to 2015. We transformed the electronic medical record into the CDM version 5.0 used in OHD SI. A worsened IBD prognosis was defined as the initiation of therapy with biologic agents, including infliximab and adalimumab To evaluate the effect that age at diagnosis had on the prognosis of IBD, we divided the patients into an early-onset (EO) IBD group (age at diagnosis <40 years) and a late-onset (LO) IBD group (age at diagnosis >= 40 years) with the cutoff value of age at diagnosis as 40 years, which was calculated using the Youden index method. We then used the logrank test and Cox proportional hazards model to analyze the effect that age at diagnosis (EO group vs LO group) had on the prognosis in patients with IBD. Results: A total of 3480 patients were enrolled. There was 2017 patients with ulcerative colitis (UC) and 1463 with Crohn's disease (CD). The median follow up period was 109.5 weeks. The EO UC group was statistically significant and showed less event-five survival (ie, experiences of biologic agents) than the LO UC group (P<.001). In CD, the EO CD group showed less event-five survival (ie, experiences of biologic agents) than the LO CD group. In the Cox proportional hazard analysis, the odds ratio (OR) of the EO UC group on experiences of biologic agents compared with the LO UC group was 2.3 (95% CI 1.3-3.8, P=.002). The OR of the EO CD group on experiences of biologic agents compared with the LO CD group was 5.4 (95% CI 1.9-14.9, P=.001). Conclusions:

关键词： ulcerative colitis Crohn's disease early-onset late-onset common data model

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Chronic Disease Prediction Using the common data model: Development Study

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JMIR AI 2022年第1期1卷 e41030页

作者： Lee, Chanjung Jo, Brian Woo, Hyunki Im, Yoori Park, Rae Woong Park, Chulhyoung Evidnet Seongnam South Korea Ajou Univ Hosp Dept Biomed Informat 164 World cup Ro Suwon 16499 Gyeonggi do South Korea

Background: Chronic disease management is a major health issue worldwide. With the paradigm shift to preventive medicine, disease prediction modeling using machine learning is gaining importance for precise and accurate medical judgement. Objective: This study aimed to develop high-performance prediction models for 4 chronic diseases using the common data model (CDM) and machine learning and to confirm the possibility for the extension of the proposed models. Methods: In this study, 4 major chronic diseases-namely, diabetes, hypertension, hyperlipidemia, and cardiovascular disease-were selected, and a model for predicting their occurrence within 10 years was developed. For model development, the Atlas analysis tool was used to define the chronic disease to be predicted, and data were extracted from the CDM according to the defined conditions. A model for predicting each disease was built with 4 algorithms verified in previous studies, and the performance was compared after applying a grid search. Results: For the prediction of each disease, we applied 4 algorithms (logistic regression, gradient boosting, random forest, and extreme gradient boosting), and all models show greater than 80% accuracy. As compared to the optimized model's performance, extreme gradient boosting presented the highest predictive performancefor the4 diseases (diabetes, hypertension, hyperlipidemia, and cardiovascular disease) with 80% or greater and from 0.84 to 0.93 in area under the curve standards. Conclusions: This study demonstrates the possibility for the preemptive management of chronic diseases by predicting the occurrence of chronic diseases using the CDM and machine learning. With these models, the risk of developing major chronic diseases within 10 years can be demonstrated by identifying health risk factors using our chronic disease prediction machine learning model developed with the real-world data-based CDM and National Health Insurance Corporation examination data that individ

关键词： common data model chronic disease prediction model machine learning disease management data model disease prediction prediction risk prediction risk factors health risk

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Applying computable phenotypes within a common data model to identify heart failure patients for an implantable cardiac device registry

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IJC HEART & VASCULATURE 2022年 39卷 100974-100974页

作者： Graham, Jove Iverson, Andy Monteiro, Joao Weiner, Katherine Southall, Kara Schiller, Katherine Gupta, Mudit Simard, Edgar P. Geisinger Med Clin Ctr Pharm Innovat & Outcomes Danville PA USA Medtronic Inc Global Clin Res & Analyt Minneapolis MN USA Geisinger Med Clin Phen Analyt & Clin Data Core Danville PA USA

Background: Use of existing data in electronic health records (EHRs) could be used more extensively to better leverage real world data for clinical studies, but only if standard, reliable processes are developed. Numerous computable phenotypes have been validated against manual chart review, and common data models (CDMs) exist to aid implementation of such phenotypes across platforms and sites. Our objective was to measure consistency between data that had previously been manually collected for an implantable cardiac device registry and CDM-based phenotypes for the condition of heart failure (HF).Methods: Patients enrolled in an implantable cardiac device registry at two hospitals from 2013 to 2018 contributed to this analysis wherein registry data were compared to PCORnet CDM-formatted EHR data. Seven different phenotype algorithms were used to search for the presence of HF and compare the results with the registry. Sensitivity, specificity, predictive value and congruence were calculated for each ***: In the registry, 176 of 319 (55%) patients had history of HF, compared with different phenotypes estimating between 96 (30%) and 188 (59%). The least-restrictive phenotypes (any diagnosis) had high sensitivity and specificity (90%/80%), but more restrictive phenotypes had higher specificity (e.g., code present in problem list, 94%). Differences were observed using time-based criteria (e.g., days between visit diagnoses) and between participating ***: Consistency between manually-collected registry data and CDM-based phenotypes for history of HF was high overall, but use of different phenotypes impacted sensitivity and specificity, and results may differ depending on the medical condition of interest.

关键词： Comorbidities heart failure Clinical trial Registry Electronic health record Computable phenotype common data model Comorbidities heart failure Clinical trial Registry Electronic health record Computable phenotype common data model

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