Clinical predictive model of new-onset atrial fibrillation in patients with acute myocardial infarction after percutaneous coronary intervention.

New-onset atrial fibrillation (NOAF) is associated with increased morbidity and mortality. Despite identifying numerous factors contributing to NOAF, the underlying mechanisms remain uncertain. This study introduces the triglyceride-glucose index (TyG index) as a predictive indicator and establishes a clinical predictive model. We included 551 patients with acute myocardial infarction (AMI) without a history of atrial fibrillation (AF). These patients were divided into two groups based on the occurrence of postoperative NOAF during hospitalization: the NOAF group (n = 94) and the sinus rhythm (SR) group (n = 457). We utilized a regression model to analyze the risk factors of NOAF and to establish a predictive model. The predictive performance, calibration, and clinical effectiveness were evaluated using the receiver operational characteristics (ROC), calibration curve, decision curve analysis, and clinical impact curve. 94 patients developed NOAF during hospitalization. TyG was identified as an independent predictor of NOAF and was significantly higher in the NOAF group. Left atrial (LA) diameter, age, the systemic inflammatory response index (SIRI), and creatinine were also identified as risk factors for NOAF. Combining these with the TyG to build a clinical prediction model resulted in an area under the curve (AUC) of 0.780 (95% CI 0.358-0.888). The ROC, calibration curve, decision curve analysis, and clinical impact curve demonstrated that the performance of the new nomogram was satisfactory. By incorporating the TyG index into the predictive model, NOAF after AMI during hospitalization can be effectively predicted. Early detection of NOAF can significantly improve the prognosis of AMI patients.