Accuracy of Electronic Health Record Phenotypes to Detect Recognition of Hypertension in Pediatric Primary Care

Abstract

Objective: To evaluate the accuracy of extractable electronic health record (EHR) data to define clinician recognition of hypertension in pediatric primary care.

Methods: We used EHR data to perform a cross-sectional study of children aged 3 to 18 years at well-visits in Connecticut from 2018 to 2023 (n = 50,290) that had either 1) incident hypertension (hypertensive blood pressure [BP] at the well-visit and ≥2 prior hypertensive BPs without prior diagnosis of hypertension) or 2) isolated hypertensive BP at the well-visit without necessarily having prior hypertensive BPs. We tested the accuracy of EHR phenotypes to detect recognition of incident hypertension or hypertensive BP using structured elements, including diagnosis codes, problem list entries, number of BP measurements, orders, and follow-up information. The primary outcome of hypertension recognition was determined by chart review.

Results: Among 239 children with incident hypertension and a random sample of 220 children with hypertensive BP, 13% in each sample had clinician recognition of hypertension and hypertensive BP, respectively. An algorithm using International Classification of Diseases, Tenth Revision (ICD-10) encounter diagnosis code, ICD-10 problem list, or multiple BPs during the visit had the highest area under the curve (AUC) for attention to incident hypertension (AUC, 0.84; sensitivity, 71.9%; specificity, 95.7%). Adding follow-up BP information to this algorithm had the highest AUC for attention to hypertensive BP (AUC, 0.85; sensitivity, 75.9%; specificity, 93.2%). For patients with hypertension recognition by chart review, ∼20% had only free text documentation of hypertension without any structured elements.

Conclusions: EHR phenotypes for hypertension recognition have high specificity and moderate sensitivity and may be used in clinician decision support to improve guideline-recommended care.

Keywords: blood pressure; computable phenotype; electronic health record; hypertension.