ArticlePharmacoepidemiology and drug safety2020
Optimizing identification of resistant hypertension: Computable phenotype development and validation.
Article in Pharmacoepidemiology and drug safety, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Common data models and data standards for tabular health data: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- Leveraging the Electronic Health Record to Identify and Manage Resistant Hypertension.Current hypertension reports · 2026Review
- Characterizing Alzheimer's Disease and Related Dementia in a Hypertension Population Within the State of Florida Using Electronic Health Record-Based Data.Clinical pharmacology and therapeutics · 2026Article
- SHREC: A framework for advancing next-generation computational phenotyping with large language models.PLOS digital health · 2026Article
- Subtypes and onset of hypertensive disorders of pregnancy and cardiovascular disease within 5 years after delivery.Frontiers in cardiovascular medicine · 2026Article
- The spatial and contextual exposome and subtypes of hypertensive disorders of pregnancy: a double machine learning-based analysis.Exposome · 2026Article
- Overcoming data challenges through enriched validation and targeted sampling to measure whole-person health in electronic health records.Journal of biomedical informatics · 2025Article
- SHREC: A Framework for Advancing Next-Generation Computational Phenotyping with Large Language Models.ArXiv · 2025Article
- Development and validation of identification algorithms for five autoimmune diseases using electronic health records: a retrospective cohort study in China.Frontiers in immunology · 2025Article
- Characterizing apparent treatment resistant hypertension in the United States: insights from the All of Us Research Program.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Using electronic health records for clinical pharmacology research: Challenges and considerations.Clinical and translational science · 2024Review
- Developing a computable phenotype for glioblastoma.Neuro-oncology · 2024Article
- Characteristics and Predictors of Apparent Treatment-Resistant Hypertension in Real-World Populations Using Electronic Health Record-Based Data.American journal of hypertension · 2024Article
- Leveraging Electronic Health Records to Construct a Phenotype for Hypertension Surveillance in the United States.American journal of hypertension · 2023Article
- A flexible symbolic regression method for constructing interpretable clinical prediction models.NPJ digital medicine · 2023Article
- Development and validation of algorithms to identify patients with chronic kidney disease and related chronic diseases across the Northern Territory, Australia.BMC nephrology · 2022Article
- Accurate long-term prediction of death for patients with cirrhosis.Hepatology (Baltimore, Md.) · 2022Article
- Multisite learning of high-dimensional heterogeneous data with applications to opioid use disorder study of 15,000 patients across 5 clinical sites.Scientific reports · 2022Article
- The OneFlorida Data Trust: a centralized, translational research data infrastructure of statewide scope.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Developing real-world evidence from real-world data: Transforming raw data into analytical datasets.Learning health systems · 2022Article
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Authors and funding
8 authors.
Funding
Abstract
purposeComputable 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.
methodsWe 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).
resultsAmong 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.
conclusionsWe 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.
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