Prediction of cardiovascular and renal risk among patients with apparent treatment‐resistant hypertension in the United States using machine learning methods

Autor: George Bakris, Pei (Paul) Lin, Chang Xu, Cindy Chen, Veronica Ashton, Mukul Singhal
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: The Journal of Clinical Hypertension, Vol 26, Iss 5, Pp 500-513 (2024)
Druh dokumentu: article
ISSN: 1751-7176
1524-6175
DOI: 10.1111/jch.14791
Popis: Abstract Apparent treatment‐resistant hypertension (aTRH), defined as blood pressure (BP) that remains uncontrolled despite unconfirmed concurrent treatment with three antihypertensives, is associated with an increased risk of developing cardiovascular and renal complications compared with controlled hypertension. We aimed to identify the characteristics of aTRH patients with an elevated risk of major adverse cardiovascular events plus (MACE+; defined as stroke, myocardial infarction, or heart failure hospitalization) and end stage renal disease (ESRD). This retrospective cohort study included aTRH patients (BP ≥140/90 mmHg and taking ≥3 antihypertensives) from the United States–based Optum® de‐identified Electronic Health Record dataset and used machine learning models to identify risk factors of MACE+ or ESRD. Patients had claims for ≥3 antihypertensive classes within 30 days between January 1, 2015 and June 30, 2021, and two office BP measures recorded 1–90 days apart within 30 days to 11 months after the index regimen date. Of a total 18 797 070 patients identified with any hypertension, 71 100 patients had aTRH. During the study period (mean 25.5 months), 4944 (7.0%) patients had a MACE+ and 2403 (3.4%) developed ESRD. In total, 22 risk factors were included in the MACE+ model and 16 in the ESRD model, and most were significantly associated with study outcomes. The risk factors with the largest impact on MACE+ risk were congestive heart failure, stages 4 and 5 chronic kidney disease (CKD), age ≥80 years, and living in the Southern region of the United States. The risk factors with the largest impact on ESRD risk, other than pre‐existing CKD, were anemia, congestive heart failure, and type 2 diabetes. The overall study cohort had a 5‐year predicted MACE+ risk of 13.4%; this risk was increased in those in the top 50% and 25% high‐risk groups (21.2% and 29.5%, respectively). The overall study cohort had a predicted 5‐year risk of ESRD of 6.8%, which was increased in the top 50% and 25% high‐risk groups (10.9% and 17.1%, respectively). We conclude that risk models developed in our study can reliably identify patients with aTRH at risk of MACE+ and ESRD based on information available in electronic health records; such models may be used to identify aTRH patients at high risk of adverse outcomes who may benefit from novel treatment interventions.
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