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Leveraging Electronic Health Records to Construct a Phenotype for Hypertension Surveillance in the United States

Siran He, So-Youn Park, Elena V. Kuklina, Nicole L. Therrien, Elizabeth A. Lundeen, Hilary K. Wall, Katrice Lampley, Lyudmyla Kompaniyets, Samantha L. Pierce, Laurence Sperling, Sandra L. Jackson

2023American Journal of Hypertension16 citationsDOIOpen Access PDF

Abstract

BACKGROUND: Hypertension is an important risk factor for cardiovascular diseases. Electronic health records (EHRs) may augment chronic disease surveillance. We aimed to develop an electronic phenotype (e-phenotype) for hypertension surveillance. METHODS: We included 11,031,368 eligible adults from the 2019 IQVIA Ambulatory Electronic Medical Records-US (AEMR-US) dataset. We identified hypertension using three criteria, alone or in combination: diagnosis codes, blood pressure (BP) measurements, and antihypertensive medications. We compared AEMR-US estimates of hypertension prevalence and control against those from the National Health and Nutrition Examination Survey (NHANES) 2017-18, which defined hypertension as BP ≥130/80 mm Hg or ≥1 antihypertensive medication. RESULTS: The study population had a mean (SD) age of 52.3 (6.7) years, and 56.7% were women. The selected three-criteria e-phenotype (≥1 diagnosis code, ≥2 BP measurements of ≥130/80 mm Hg, or ≥1 antihypertensive medication) yielded similar trends in hypertension prevalence as NHANES: 42.2% (AEMR-US) vs. 44.9% (NHANES) overall, 39.0% vs. 38.7% among women, and 46.5% vs. 50.9% among men. The pattern of age-related increase in hypertension prevalence was similar between AEMR-US and NHANES. The prevalence of hypertension control in AEMR-US was 31.5% using the three-criteria e-phenotype, which was higher than NHANES (14.5%). CONCLUSIONS: Using an EHR dataset of 11 million adults, we constructed a hypertension e-phenotype using three criteria, which can be used for surveillance of hypertension prevalence and control.

Topics & Concepts

MedicineNational Health and Nutrition Examination SurveyMedical recordBlood pressureElectronic health recordPopulationInternal medicineHealth recordsAmbulatory blood pressureDiagnosis codeEnvironmental healthHealth careEconomicsEconomic growthElectronic Health Records SystemsMachine Learning in HealthcareMedical Coding and Health Information
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