Multicenter development and validation of machine-learning risk models to predict procedural complete revascularization and in-hospital heart failure in STEMI patients treated with primary PCI.

BACKGROUND

In-hospital heart failure (HF) remains common after primary percutaneous coronary intervention (PPCI) for ST-segment elevation myocardial infarction (STEMI) and is associated with adverse in-hospital outcomes. In addition, whether procedural complete revascularization (CR) can be achieved during the index PCI is clinically relevant but often constrained in real-world practice. We aimed to develop and externally validate machine-learning (ML) models for these two complementary prediction tasks.

METHODS

We conducted a multicenter cohort study of STEMI patients treated with PPCI from three hospitals. Patients from Hezhou People's Hospital (January 2020 to June 2024) comprised the training cohort (n = 734). Patients from two other centers (July 2024 to December 2025) were combined as an independent testing cohort (n = 352). Multiple ML algorithms were benchmarked to predict (1) in-hospital HF and (2) the real-world feasibility of achieving procedural CR during the index PCI. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), the area under the precision-recall curve (AUPRC), classification metrics, calibration curves, decision curve analysis (DCA), and clinical impact curves. Shapley Additive Explanations (SHAP) were used to enhance interpretability.

RESULTS

For in-hospital HF prediction, CatBoost showed the best overall performance in the independent testing cohort (AUC: 0.973; 95% CI: 0.957-0.989; accuracy: 88.6%), with good calibration and favorable net benefit on DCA. For procedural CR prediction, CatBoost was also selected as the primary model based on its overall performance profile in the independent testing cohort (AUC: 0.970; 95% CI: 0.954-0.987; accuracy: 92.0%), with acceptable calibration and positive net benefit across a broad range of threshold probabilities. Key predictors included LAD involvement, age, symptom-to-guidewire crossing time, and markers related to inflammation, coagulation, renal function, and lipid metabolism.

CONCLUSIONS

In a three-center cohort, we developed and externally validated two ML models for predicting subsequent in-hospital HF after index PPCI and the feasibility of achieving procedural CR during the index PCI. Both models demonstrated good discrimination, calibration, clinical utility, and interpretability, supporting peri-procedural risk stratification and catheterization-laboratory decision support in STEMI patients treated with PPCI.