Abstract
OBJECTIVE: To identify patients with hypertension with resistant and controlled blood pressure (BP) using electronic health records (EHRs) in order to elucidate practices in the real-world clinical treatment of hypertension and to enable future genetic studies. DESIGN: Using EHRs, we developed and validated algorithms to identify patients with resistant and controlled hypertension. SETTING: An academic medical centre in Nashville, Tennessee. POPULATION: European-American (EA) and African-American (AA) patients with hypertension. MAIN OUTCOME MEASURES: Demographic characteristics: race, age, gender, body mass index, outpatient BPs and the history of diabetes mellitus, chronic kidney disease stage 3, ischaemic heart disease, transient ischaemic attack, atrial fibrillation and sleep apnoea. MEDICATION TREATMENT: All antihypertensive medication classes prescribed to a patient at the time of classification and ever prescribed following classification. RESULTS: The algorithms had performance metrics exceeding 92%. The prevalence of resistant hypertension in the total hypertensive population was 7.3% in EA and 10.5% in AA. At diagnosis, AA were younger, heavier, more often female and had a higher incidence of type 2 diabetes and higher BPs than EA. AA with resistant hypertension were more likely to be treated with vasodilators, dihydropyridine calcium channel blockers and alpha-2 agonists while EA were more likely to be treated with angiotensin receptor blockers, renin inhibitors and beta blockers. Mineralocorticoid receptor antagonists use was increased in patients treated with more than four antihypertensive medications compared with patients treated with three (12.4% vs 2.6% in EA, p<0.001; 12.3% vs 2.8% in AA, p<0.001). The number of patients treated with a mineralocorticoid receptor antagonist increased to 37.4% in EA and 41.2% in AA over a mean follow-up period of 7.4 and 8.7 years, respectively. CONCLUSIONS: Clinical treatment of resistant hypertension differs in EA and AA patients. These results demonstrate the feasibility of identifying resistant hypertension using an EHR.