Development and validation of prediction models for heart failure with improved ejection fraction

Approximately 10-60% of heart failure with reduced ejection fraction (HFrEF) patients may transition to heart failure with improved ejection fraction (HFimpEF), a clinically meaningful phenotype associated with better prognosis. However, existing prediction tools demonstrate suboptimal discrimination and poor clinical utility. We aimed to develop and validate interpretable models for HFimpEF prediction by comparing logistic regression with ensemble machine learning.

This retrospective cohort study included 1,257 HFrEF patients from the First Affiliated Hospital of USTC (March 2015-October 2023), split into training (n = 1,006) and internal validation (n = 251) sets. External validation used data from 310 patients across eight hospitals in Anhui Province (January 2024-January 2025). HFimpEF was defined as LVEF increase ≥10% to >40%. Five models were developed: data-driven logistic regression (Model 1); literature-based logistic regression (Model 2) with 5 common predictors validated in both BIOSTAT-CHF and ASIAN-HF cohorts; random forest (Model 3) and XGBoost (Model 4), both tuned via 5-fold cross-validation; and a simplified ensemble (Model 5) that averaged the predicted probabilities of Models 1, 3, and 4, then calibrated the average via logistic regression. Performance was assessed using AUC for discrimination, Brier score with calibration plots for calibration, decision curve analysis for clinical utility, and DeLong's test for model comparisons; SHAP analysis was applied for interpretability of Models 3 and 4.

Median age was 65 years (IQR 53-73); 69.9% were male. Multivariable analysis identified seven predictors: age (OR 0.97, 95% CI 0.96-0.99), BMI (OR 1.04, 95% CI 1.00-1.09), systolic blood pressure (per 10 mmHg increase, OR 1.13, 95% CI 1.01-1.25), prior PCI (OR 0.45, 95% CI 0.26-0.79), HDL-C (OR 2.48, 95% CI 1.36-4.50), FT3 (OR 1.21, 95% CI 1.02-1.45), and LVESD (OR 0.92, 95% CI 0.87-0.99). In external validation, Model 5 showed superior discrimination (AUC 0.67 vs. 0.60 for Model 2, P = 0.004) but poorer calibration (Brier score 0.34). Model 1 demonstrated balanced performance (AUC 0.66, Brier score 0.23).

Our findings indicate that ensemble learning methods provide a moderate enhancement in discrimination for HFimpEF prediction, while logistic regression models exhibit superior robustness and interpretability. We have developed an online tool (https://alslyy-wq.shinyapps.io/USTC-HF-HFimpEF-Prediction/) that integrates both methodologies to facilitate practical HFimpEF risk assessment.