Predictive Factors of Apparent Treatment Resistant Hypertension Among Patients With Hypertension Identified Using Electronic Health Records.

Autor: Lin S; Division of General Internal Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA., Hsu YJ; Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA., Kim JS; Division of Rheumatology, Johns Hopkins University School of Medicine, Baltimore, MD, USA., Jackson JW; Center for Drug Safety and Effectiveness, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA.; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.; Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.; Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA., Segal JB; Division of General Internal Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA. jsegal@jhmi.edu.; Center for Drug Safety and Effectiveness, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA. jsegal@jhmi.edu.; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA. jsegal@jhmi.edu.; Division of General Internal Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA. jsegal@jhmi.edu.
Jazyk: angličtina
Zdroj: Journal of general internal medicine [J Gen Intern Med] 2024 Oct 02. Date of Electronic Publication: 2024 Oct 02.
DOI: 10.1007/s11606-024-09068-z
Abstrakt: Background: Early identification of a patient with resistant hypertension (RH) enables quickly intensified treatment, short-interval follow-up, or perhaps case management to bring his or her blood pressure under control and reduce the risk of complications.
Objective: To identify predictors of RH among individuals with newly diagnosed hypertension (HTN), while comparing different prediction models and techniques for managing missing covariates using electronic health records data.
Design: Risk prediction study in a retrospective cohort.
Participants: Adult patients with incident HTN treated in any of the primary care clinics of one health system between April 2013 and December 2016.
Main Measures: Predicted risk of RH at the time of HTN identification and candidate predictors for variable selection in future model development.
Key Results: Among 26,953 individuals with incident HTN, 613 (2.3%) met criteria for RH after 4.7 months (interquartile range, 1.2-11.3). Variables selected by the least absolute shrinkage and selection operator (LASSO), included baseline systolic blood pressure (SBP) and its missing indicator (a dummy variable created if baseline SBP is absent), use of antihypertensive medication at the time of cohort entry, body mass index, and atherosclerosis risk. The random forest technique achieved the highest area under the curve (AUC) of 0.893 (95% CI, 0.881-0.904) and the best calibration with a calibration slope of 1.01. Complete case analysis is not a valuable option (AUC = 0.625).
Conclusions: Machine learning techniques and traditional logistic regression exhibited comparable levels of predictive performance after handling the missingness. We suggest that the variables identified by this study may be good candidates for clinical prediction models to alert clinicians to the need for short-interval follow up and more intensive early therapy for HTN.
(© 2024. The Author(s), under exclusive licence to Society of General Internal Medicine.)
Databáze: MEDLINE
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