TY  - JOUR
AU  - Nguyen, Hieu Minh
AU  - Anderson, William
AU  - Chou, Shih-Hsiung
AU  - McWilliams, Andrew
AU  - Zhao, Jing
AU  - Pajewski, Nicholas
AU  - Taylor, Yhenneko
PY  - 2024
DA  - 2024/10/28
TI  - Predictive Models for Sustained, Uncontrolled Hypertension and Hypertensive Crisis Based on Electronic Health Record Data: Algorithm Development and Validation
JO  - JMIR Med Inform
SP  - e58732
VL  - 12
KW  - machine learning
KW  - risk prediction
KW  - predictive model
KW  - decision support
KW  - blood pressure
KW  - cardiovascular
KW  - electronic health record
AB  - Background: Assessing disease progression among patients with uncontrolled hypertension is important for identifying opportunities for intervention. Objective: We aim to develop and validate 2 models, one to predict sustained, uncontrolled hypertension (≥2 blood pressure [BP] readings ≥140/90 mm Hg or ≥1 BP reading ≥180/120 mm Hg) and one to predict hypertensive crisis (≥1 BP reading ≥180/120 mm Hg) within 1 year of an index visit (outpatient or ambulatory encounter in which an uncontrolled BP reading was recorded). Methods: Data from 142,897 patients with uncontrolled hypertension within Atrium Health Greater Charlotte in 2018 were used. Electronic health record–based predictors were based on the 1-year period before a patient’s 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. Results: In internal validation, the C-statistic and integrated calibration index were 0.72 (95% CI 0.71‐0.72) and 0.015 (95% CI 0.012‐0.020) for the sustained, uncontrolled hypertension model, and 0.81 (95% CI 0.79‐0.82) and 0.009 (95% CI 0.007‐0.011) 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 0.69‐0.71) and 0.79 (95% CI 0.78‐0.81) for the sustained, uncontrolled hypertension model and hypertensive crisis model, respectively. Conclusions: An electronic health record–based 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. 
SN  - 2291-9694
UR  - https://medinform.jmir.org/2024/1/e58732
UR  - https://doi.org/10.2196/58732
DO  - 10.2196/58732
ID  - info:doi/10.2196/58732
ER  -