Development and validation of a risk prediction model for myocardial hypoperfusion after primary PCI in ST-segment elevation myocardial infarction

Objective: To analyze the determinants of myocardial hypoperfusion following primary percutaneous coronary intervention (PCI) in patients with acute ST-segment elevation myocardial infarction (STEMI) and to develop a risk prediction model. Methods: = 201). Candidate variables were screened using Boruta and least absolute shrinkage and selection operator (LASSO) regression, followed by multivariable logistic regression to identify independent predictors. A risk prediction model was constructed using R software and visualized as a nomogram. Model performance and clinical utility were evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Results: < 0.05). The nomogram demonstrated good discrimination, with area under the curve (AUC) values of 0.855 (95% CI: 0.811-0.900) in the training cohort and 0.838 (95% CI: 0.764-0.912) in the validation cohort. Calibration curves indicated good agreement between predicted and observed outcomes. Decision curve analysis showed that the model provided greater net benefit than both treat-all and treat-none strategies across threshold probabilities of 0.01-0.99 in the training cohort and 0.07-0.99 in the validation cohort. Conclusion: Time from onset to primary PCI, atorvastatin dose before PCI, balloon deflation method during PCI, RDW, and MAO levels are important determinants of myocardial hypoperfusion following primary PCI in patients with STEMI. The proposed prediction model demonstrated favorable predictive performance and clinical utility, suggesting its potential value for early risk stratification.