Machine Learning Prediction of Blood Pressure Control in Patients With Hypertension and Heart Failure Using Longitudinal Clinical Data

ObjectiveTo develop and validate machine learning models for predicting Blood Pressure (BP) control status using demographic characteristics and longitudinal BP history. MethodsThis retrospective cohort study analyzed deidentified data from a multi-site primary care quality improvement program for hypertension management. Participants included adults with diagnosed hypertension (N=23,002) or heart failure (N=1,137) who had at least 2 clinical visits. The primary outcome was BP control status, defined as systolic BP less than 140 mm Hg and diastolic BP less than 90 mm Hg. Five machine learning algorithms (logistic regression, decision tree, Random Forest (RF), support vector machine, and extreme gradient boosting) were compared using accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUROC). Feature importance was assessed using Shapley Additive Explanations (SHAP) values. ResultsAmong 23,002 hypertensive patients (mean [SD] age, 65.25 [13.95] years; 13,015 [56.58%] female), the RF model achieved the highest performance with an AUROC of 0.88 (95% CI, 0.86-0.90) using BP history features alone and 0.87 (95% CI, 0.85-0.89) with combined features. BP history substantially outperformed demographic factors (AUROC, 0.60; 95% CI, 0.58-0.62). Mean systolic BP, maximum systolic BP, and maximum diastolic BP were the most influential predictors. In the heart failure cohort (N=1,137; mean [SD] age, 75.15 [15.05] years; 579 [50.92%] female), the RF model achieved an AUROC of 0.93 (95% CI, 0.90-0.96) with combined features. The model demonstrated accuracy of 0.77 (95% CI, 0.76-0.78), precision of 0.78 (95% CI, 0.76-0.79), recall of 0.73 (95% CI, 0.71-0.75), and F1-score of 0.75 (95% CI, 0.74-0.77) for the hypertension cohort. ConclusionsMachine learning models incorporating longitudinal BP history effectively predicted hypertension control status, with BP variability metrics showing substantially greater predictive value than demographic characteristics. These findings suggest that systematic incorporation of historical BP patterns into clinical decision-support systems may enhance personalized hypertension management.