Predictors of 1-Year Mortality Among Patients With Heart Failure With Preserved Ejection Fraction

Background: Accepted heart failure (HF) with preserved ejection fraction (EF) prognostic scores rely on limited variables and linear assumptions that are likely to miss complex risk patterns. Objectives: The objectives of the study were to develop, compare, and internally validate prediction models for 1-year all-cause mortality after first hospitalization for decompensated HF with preserved EF. Methods: We performed a retrospective cohort study using electronic medical records from a large academic health system, including adults with EF ≥50% admitted for a first-time HF exacerbation. Variables spanned demographics, comorbidities, laboratory tests, echocardiographic variables, medications, and outcomes. Data were split into training (80%) and test (20%) sets with stratification by outcome. Missing values were handled with multiple imputation by chained equations. Two tree-based classifiers (Extreme Gradient Boosting and Light Gradient Boosting) were tuned with cross-validation and evaluated by area under the receiver operating characteristic curve (AUROC) and calibration. Time-to-event models included Cox proportional hazards, random survival forest (RSF), and gradient boosting survival (GBS) with concordance index and calibration assessment. Global and local (patient-level) explainability was extracted from each model, with cross-model predictor ranking and comparison. Results: We analyzed 7,840 admissions; the mean age was 78 years with 55.6% women. One-year mortality was 31.5%. Test-set AUROC was 0.751 (95% CI: 0.727-0.775) for Extreme Gradient Boosting and 0.749 for (95% CI: 0.721-0.776) Light Gradient Boosting with acceptable calibration. GBS achieved the highest concordance index (0.718; 95% CI: 0.696-0.740), followed by RSF (0.711; 95% CI: 0.690-0.734) and Cox (0.704; 95% CI: 0.680-0.728). The 12-month time-dependent AUROCs for survival models were GBS 0.759 (95% CI: 0.716-0.799), RSF: 0.750 (95% CI 0.708-0.789), and Cox: 0.735 (95% CI 0.692-0.777). Lower albumin, older age, higher N-terminal pro-B-type natriuretic peptide, renal dysfunction, and lower hemoglobin were the most consistent risk signals. Conclusions: Our transparent risk tool using routinely available admission data appears feasible, allowing for patient-level, precision health risk assessment.