Risk prediction for long-term cardiovascular events in patients with concurrent hypertension, HFpEF, and unstable angina: a multicenter machine learning-assisted cohort study
TL;DR
A machine learning-assisted, Cox-based nomogram incorporating five routinely available clinical and laboratory variables provided acceptable discrimination in the development cohort and modest-to-acceptable performance in geographically distinct validation and test cohorts, and may support individualized long-term risk stratification in patients with concurrent hypertension, HFpEF, and UAP.
Abstract
Background Patients with concurrent hypertension, heart failure with preserved ejection fraction (HFpEF), and unstable angina pectoris (UAP) represent a high-risk population with heterogeneous long-term cardiovascular outcomes. However, practical tools for individualized risk stratification in this clinically complex population remain limited. This study aimed to develop and validate a machine learning-assisted nomogram for predicting long-term major adverse cardiovascular events (MACEs) in patients with concomitant hypertension, HFpEF, and UAP. Methods This multicenter retrospective cohort study included 1,669 patients with hypertension, HFpEF, and UAP from Six medical centers in China. Patients were assigned according to treating hospital to a development cohort, a validation cohort, and an independent test cohort. The primary endpoint was MACEs, defined as cardiac death, unplanned coronary revascularization, or rehospitalization for acute heart failure. Candidate predictors were selected using least absolute shrinkage and selection operator regression, the Boruta algorithm, and random forest feature importance analysis. Variables consistently identified across these methods were entered into a multivariable Cox proportional hazards model, and a Cox-based nomogram was constructed to estimate 2-, 3-, and 4-year MACE risk. Model performance was assessed using receiver operating characteristic curves, time-dependent area under the curve, calibration curves, decision curve analysis, and Kaplan-Meier survival analysis. Results During a median follow-up of 48 months, 254 patients experienced MACEs. Five predictors were retained in the final model: diabetes mellitus, previous myocardial infarction, systemic immune-inflammation index, triglyceride, and N-terminal pro-B-type natriuretic peptide. In the development cohort, the nomogram yielded area under the curve values of 0.785, 0.787, and 0.760 for predicting 2-, 3-, and 4-year MACEs, respectively. The corresponding values were 0.674, 0.697, and 0.682 in the validation cohort, and 0.685, 0.711, and 0.718 in the test cohort. Calibration curves showed acceptable agreement between predicted and observed risks, and decision curve analysis suggested potential net benefit across clinically relevant threshold probabilities. Patients classified as high risk according to the nomogram had a significantly higher incidence of MACEs than those classified as low risk across all cohorts. Conclusion A machine learning-assisted, Cox-based nomogram incorporating five routinely available clinical and laboratory variables provided acceptable discrimination in the development cohort and modest-to-acceptable performance in geographically distinct validation and test cohorts. This model may support individualized long-term risk stratification in patients with concurrent hypertension, HFpEF, and UAP, although prospective validation and clinical impact studies are warranted before routine implementation.