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Optimizing identification of resistant hypertension: Computable phenotype development and validation
Author(s) -
McDonough Caitrin W.,
Babcock Kyle,
Chucri Kristen,
Crawford Dana C.,
Bian Jiang,
Modave François,
CooperDeHoff Rhonda M.,
Hogan William R.
Publication year - 2020
Publication title -
pharmacoepidemiology and drug safety
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.023
H-Index - 96
eISSN - 1099-1557
pISSN - 1053-8569
DOI - 10.1002/pds.5095
Subject(s) - medicine , phenotype , population , algorithm , computer science , biology , genetics , environmental health , gene
Purpose Computable phenotypes are constructed to utilize data within the electronic health record (EHR) to identify patients with specific characteristics; a necessary step for researching a complex disease state. We developed computable phenotypes for resistant hypertension (RHTN) and stable controlled hypertension (HTN) based on the National Patient‐Centered Clinical Research Network (PCORnet) common data model (CDM). The computable phenotypes were validated through manual chart review. Methods We adapted and refined existing computable phenotype algorithms for RHTN and stable controlled HTN to the PCORnet CDM in an adult HTN population from the OneFlorida Clinical Research Consortium (2015‐2017). Two independent reviewers validated the computable phenotypes through manual chart review of 425 patient records. We assessed precision of our computable phenotypes through positive predictive value (PPV) and test validity through interrater reliability (IRR). Results Among the 156 730 HTN patients in our final dataset, the final computable phenotype algorithms identified 24 926 patients with RHTN and 19 100 with stable controlled HTN. The PPV for RHTN in patients randomly selected for validation of the final algorithm was 99.1% (n = 113, CI: 95.2%‐99.9%). The PPV for stable controlled HTN in patients randomly selected for validation of the final algorithm was 96.5% (n = 113, CI: 91.2%‐99.0%). IRR analysis revealed a raw percent agreement of 91% (152/167) with Cohen's kappa statistic = 0.87. Conclusions We constructed and validated a RHTN computable phenotype algorithm and a stable controlled HTN computable phenotype algorithm. Both algorithms are based on the PCORnet CDM, allowing for future application to epidemiological and drug utilization based research.