A Logistic Regression Prediction Model for Post-PCI In-Hospital Major Adverse Cardiovascular Events in Patients With Acute ST-Segment Elevation Myocardial Infarction: Construction and Internal Validation.

backgroundThe incidence of in-hospital major adverse cardiovascular events (MACE) remains high in patients with acute ST-segment elevation myocardial infarction (STEMI) after emergency percutaneous coronary intervention (PCI). Existing risk prediction tools mostly focus on preoperative baseline indicators and ignore intraoperative interventional parameters, lacking comprehensive postprocedural risk assessment tools for STEMI patients after PCI. This study is aimed at developing and internally validating a logistic regression prediction model for post-PCI in-hospital MACE in STEMI patients based on multidimensional perioperative clinical indicators, so as to realize accurate postoperative risk stratification and guide individualized perioperative management.

methodsWe retrospectively included 1195 STEMI patients who underwent emergency PCI at the Chest Pain Center of Lu'an People's Hospital from January 2019 to October 2024. Patients were randomly divided into training (n = 837) and validation (n = 358) sets at a 7:3 ratio. The primary endpoint was in-hospital MACE (composite of cardiogenic shock, malignant arrhythmias, acute heart failure, reinfarction, or all-cause death). A prediction model was constructed using least absolute shrinkage and selection operator (LASSO) regression combined with multivariate logistic regression. Model discrimination was evaluated via the area under the receiver operating characteristic curve (AUC). Head-to-head discriminative comparison with the widely used GRACE and TIMI risk scores was performed using AUC values and the DeLong test to quantify the incremental predictive value of the novel model. Model calibration and clinical utility were assessed via calibration plots, the Hosmer-Lemeshow test, and decision curve analysis (DCA), with internal validation performed in the validation set.

resultsA total of 260 patients (21.8%) experienced in-hospital MACE. After LASSO regression screening and multivariate logistic regression analysis, eight independent predictors were identified: admission systolic blood pressure, Killip classification, intervention duration, contrast volume, postprocedural TIMI flow grade, culprit vessel occlusion, serum creatinine, and serum potassium. The training set AUC was 0.84 (95% CI: 0.80-0.87) with a sensitivity of 77.7% and specificity of 78.2%; the validation set AUC was 0.826 (95% CI: 0.782-0.870). Comparative discriminative analysis showed that the traditional GRACE and TIMI risk scores exhibited moderate predictive performance. The training set AUC of the GRACE score was 0.763 (95% CI: 0.724-0.800), and that of the TIMI score was 0.748 (95% CI: 0.708-0.786). In the validation set, the AUC of the GRACE score and TIMI score were 0.751 (95% CI: 0.699-0.800) and 0.736 (95% CI: 0.683-0.786), respectively. The DeLong test confirmed that our novel model achieved significantly higher discriminative ability than both GRACE and TIMI scores in both training and validation cohorts (all p < 0.05). The training set Brier score was 0.128, with a calibration intercept of 0.042 and calibration slope of 0.991; the validation set Brier score was 0.135, with a calibration intercept of 0.051 and calibration slope of 0.985. After 1000 bootstrap optimism correction, the corrected calibration slope was 0.987 and the corrected intercept was 0.045. The Hosmer-Lemeshow test indicated good model calibration (χ

conclusionThe logistic regression prediction model based on eight clinical indicators demonstrated good discrimination, quantitative calibration, and clinical utility for predicting post-PCI in-hospital MACE in STEMI patients. The model integrates preoperative baseline, intraoperative procedural, and laboratory indicators, achieves stable performance after internal validation, and can assist clinicians in postoperative risk stratification of patients, timely identifying high-risk populations after PCI, and optimizing postoperative perioperative management strategies.