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. 2023 Nov 15;36(12):677-685.
doi: 10.1093/ajh/hpad081.

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

Siran He  1 , Soyoun Park  1 , Elena Kuklina  1 , Nicole L Therrien  1 , Elizabeth A Lundeen  2 , Hilary K Wall  1 , Katrice Lampley  1   3 , Lyudmyla Kompaniyets  4 , Samantha L Pierce  4 , Laurence Sperling  1 , Sandra L Jackson  1
Affiliations

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

Siran He et al. Am J Hypertens. .

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.

Keywords: blood pressure; chronic disease; electronic health record; hypertension; phenotype; surveillance.

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Conflict of interest statement

Disclosure

The authors have no conflict of interest to declare.

Figures

Figure 1.
Figure 1.
Constructing hypertension e-phenotypes in AEMR-US. *The denominator for pregnancy: females aged 18–44 years (N = 2,876,436); All other items used the denominator noted as “100%”. Abbreviation: IQVIA AEMR OMOP, IQVIA Ambulatory Electronic Medical Record-US dataset, in Observational Medical Outcomes Partnership format.
Figure 2.
Figure 2.
Crude hypertension prevalence by age in AEMR-US 2019 based on selected criteria, compared with NHANES 2017–18. Main measurement period, 2019. Abbreviations: AEMR-US, IQVIA Ambulatory Electronic Medical Record-US dataset (2019); BP, blood pressure, ≥2 BP measurements; DX, ≥1 diagnosis code; Antihypertensives, ≥1 antihypertensive medication; NHANES, National Health and Nutrition Examination Survey (2017–18); Hypertension in NHANES: BP ≥140/90 or 130/80, or self-reported antihypertensive medication use.
Figure 3.
Figure 3.
Age-standardized hypertension prevalence by sex in AEMR-US based on selected criteria, compared with NHANES. BP cutoffs are consistent between NHANES and AEMR-US in each panel. Main measurement period, 2019. Abbreviations: AEMR-US, IQVIA Ambulatory Electronic Medical Record-US dataset (2019); BP, blood pressure, ≥2 BP measurements; DX, ≥1 diagnosis code; Antihypertensives, ≥1 antihypertensive medication; NHANES, National Health and Nutrition Examination Survey (2017–18); Hypertension in NHANES: BP ≥140/90 or 130/80, or self-reported antihypertensive medication use.
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