Characteristics and Predictors of Apparent Treatment-Resistant Hypertension in Real-World Populations Using Electronic Health Record-Based Data. Am J Hypertens 2024 Jan 01;37(1):60-68
Date
09/15/2023Pubmed ID
37712350Pubmed Central ID
PMC10724527DOI
10.1093/ajh/hpad084Scopus ID
2-s2.0-85180004382 (requires institutional sign-in at Scopus site)Abstract
BACKGROUND: Apparent treatment-resistant hypertension (aTRH) is defined as uncontrolled blood pressure (BP) despite using ≥3 antihypertensive classes or controlled BP while using ≥4 antihypertensive classes. Patients with aTRH have a higher risk for adverse cardiovascular outcomes compared with patients with controlled hypertension (HTN). Although there have been prior reports on the prevalence, characteristics, and predictors of aTRH, these have been broadly derived from smaller datasets, randomized controlled trials, or closed healthcare systems.
METHODS: We extracted patients with HTN defined by ICD-9 and ICD-10 codes during 1/1/2015-12/31/2018, from 2 large electronic health record databases: the OneFlorida Data Trust (n = 223,384) and Research Action for Health Network (REACHnet) (n = 175,229). We applied our previously validated aTRH and stable controlled HTN computable phenotype algorithms and performed univariate and multivariate analyses to identify the prevalence, characteristics, and predictors of aTRH in these populations.
RESULTS: The prevalence of aTRH among patients with HTN in OneFlorida (16.7%) and REACHnet (11.3%) was similar to prior reports. Both populations had a significantly higher proportion of Black patients with aTRH compared with those with stable controlled HTN. aTRH in both populations shared similar significant predictors, including Black race, diabetes, heart failure, chronic kidney disease, cardiomegaly, and higher body mass index. In both populations, aTRH was significantly associated with similar comorbidities, when compared with stable controlled HTN.
CONCLUSIONS: In 2 large, diverse real-world populations, we observed similar comorbidities and predictors of aTRH as prior studies. In the future, these results may be used to improve healthcare professionals' understanding of aTRH predictors and associated comorbidities.
Author List
Jafari E, Cooper-DeHoff RM, Effron MB, Hogan WR, McDonough CWAuthor
William R. Hogan MD Director, Professor in the Data Science Institute department at Medical College of WisconsinMESH terms used to index this publication - Major topics in bold
Antihypertensive AgentsBlood Pressure
Electronic Health Records
Humans
Hypertension
Prevalence
Risk Factors
