Establishment and Evaluation of a Predictive Model for Cardiac Rupture Risk in Acute Myocardial Infarction Patients

OBJECTIVE: To identify risk factors for cardiac rupture (CR) in patients with acute myocardial infarction (AMI) and develop a validated prognostic model. METHODS: This study included 89 consecutive AMI patients with CR and 451 randomly selected controls without CR from a pool of 4,559 patients (2013-2024). Risk factors were identified using least absolute shrinkage and selection operator regression alongside univariate and multivariate logistic regression. A nomogram prediction model was built and evaluated using the receiver operating characteristic curve, calibration plots, the Hosmer-Lemeshow test, and decision curve analysis. RESULTS: Among 540 participants (74.3% male, 25.7% female), CR occurred in 89 (16.5%) cases. Univariate analysis identified 24 associated factors. Least absolute shrinkage and selection operator regression refined these to six key predictors for the final model: older age, male sex, higher Killip classification, lower use of angiotensin-converting enzyme inhibitors/angiotensin receptor blockers medications, higher CK-MB levels, and lower ejection fraction. The nomogram demonstrated strong predictive performance, with an area under the curve of 0.874 in the training set and 0.820 in the validation set. Calibration curves showed excellent agreement between predictions and observations, and the Hosmer-Lemeshow test indicated good fit ( P > 0.05). CONCLUSION: Advanced age, higher Killip class, and elevated creatine kinase MB are risk factors for CR, while angiotensin-converting enzyme inhibitors/angiotensin receptor blockers therapy and higher ejection fraction are protective. The developed nomogram provides a reliable tool for individualized risk prediction.