{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T17:18:38Z","timestamp":1787851118697,"version":"build-2784847793"},"reference-count":32,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Med Inform"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n Assessing disease progression among patients with uncontrolled hypertension is important for identifying opportunities for intervention.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n We aim to develop and validate 2 models, one to predict sustained, uncontrolled hypertension (\u22652 blood pressure [BP] readings \u2265140\/90 mm Hg or \u22651 BP reading \u2265180\/120 mm Hg) and one to predict hypertensive crisis (\u22651 BP reading \u2265180\/120 mm Hg) within 1 year of an index visit (outpatient or ambulatory encounter in which an uncontrolled BP reading was recorded).<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n Data from 142,897 patients with uncontrolled hypertension within Atrium Health Greater Charlotte in 2018 were used. Electronic health record\u2013based predictors were based on the 1-year period before a patient\u2019s index visit. The dataset was randomly split (80:20) into a training set and a validation set. In total, 4 machine learning frameworks were considered: L2-regularized logistic regression, multilayer perceptron, gradient boosting machines, and random forest. Model selection was performed with 10-fold cross-validation. The final models were assessed on discrimination (C-statistic), calibration (eg, integrated calibration index), and net benefit (with decision curve analysis). Additionally, internal-external cross-validation was performed at the county level to assess performance with new populations and summarized using random-effect meta-analyses.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n In internal validation, the C-statistic and integrated calibration index were 0.72 (95% CI 0.71\u20100.72) and 0.015 (95% CI 0.012\u20100.020) for the sustained, uncontrolled hypertension model, and 0.81 (95% CI 0.79\u20100.82) and 0.009 (95% CI 0.007\u20100.011) for the hypertensive crisis model. The models had higher net benefit than the default policies (ie, treat-all and treat-none) across different decision thresholds. In internal-external cross-validation, the pooled performance was consistent with internal validation results; in particular, the pooled C-statistics were 0.70 (95% CI 0.69\u20100.71) and 0.79 (95% CI 0.78\u20100.81) for the sustained, uncontrolled hypertension model and hypertensive crisis model, respectively.<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n An electronic health record\u2013based model predicted hypertensive crisis reasonably well in internal and internal-external validations. The model can potentially be used to support population health surveillance and hypertension management. Further studies are needed to improve the ability to predict sustained, uncontrolled hypertension.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/58732","type":"journal-article","created":{"date-parts":[[2024,10,28]],"date-time":"2024-10-28T10:02:38Z","timestamp":1730109758000},"page":"e58732-e58732","source":"Crossref","is-referenced-by-count":5,"title":["Predictive Models for Sustained, Uncontrolled Hypertension and Hypertensive Crisis Based on Electronic Health Record Data: Algorithm Development and Validation"],"prefix":"10.2196","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8030-6058","authenticated-orcid":false,"given":"Hieu 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