Early Detection of Cardiac Rupture Risk in Acute Myocardial Infarction: A Comprehensive Predictive Model

Background Cardiac rupture is a critical and often fatal complication following acute myocardial infarction (AMI). Early identification of patients at high risk of this event is crucial for timely intervention and improved outcomes. Objectives This study aimed to identify clinical predictors of cardiac rupture in AMI patients and develop a predictive nomogram for clinical use. Methods We conducted a retrospective case-control study at Beijing Friendship Hospital, involving AMI patients treated from January 2018 to the December 2023. Patients were divided into two groups: those who experienced cardiac rupture and those who did not, matched at a 1:4 ratio, then this study included 30 with cardiac rupture and 120 controls. Using least absolute shrinkage and selection operator (LASSO) regression, univariate and multivariate logistic regression analyses, we identified key predictors of cardiac rupture. A nomogram was constructed based on these predictors and validated using receiver operating characteristic (ROC) curves and calibration plots. Results Significant predictors identified by LASSO-logistics regression were N-terminal pro-B type natriuretic peptide (NT-proBNP) on admission, decreased Osmolality, increased right ventricle size, elevated Gensini score, presence of anemia, and elevated glucose levels. The nomogram demonstrated good predictive accuracy with an area under the ROC curve of 0.942 (0.892–0.991) and the Hosmer-Lemeshow statistic, which measures the goodness of fit for the model, was calculated to be 3.315 with a p-value of 0.950. Conclusions The developed nomogram effectively identifies AMI patients at high risk of cardiac rupture, integrating multiple clinical parameters. This tool can aid clinicians in early risk stratification and decision-making, potentially reducing the incidence of this lethal complication.