ArticleJournal of general internal medicine2025
Predictive Factors of Apparent Treatment Resistant Hypertension Among Patients With Hypertension Identified Using Electronic Health Records.
Article in Journal of general internal medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Review
- Leveraging the Electronic Health Record to Identify and Manage Resistant Hypertension.Current hypertension reports · 2026Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
backgroundEarly 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.
objectiveTo 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.
designRisk prediction study in a retrospective cohort.
participantsAdult 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
resultsAmong 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).
conclusionsMachine 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.
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