Wu Xiao-Dan, Zhao Wei, Wang Quan-Wei, Yang Xin-Yu, Wang Jing-Yue, Yan Shuo, Tong Qian
Department of Cardiovascular Center, The First Hospital of Jilin University, Changchun, 130021, China.
Sci Rep. 2025 Jan 2;15(1):439. doi: 10.1038/s41598-024-84759-5.
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.
新发房颤（NOAF）与发病率和死亡率增加相关。尽管已确定许多导致NOAF的因素，但其潜在机制仍不确定。本研究引入甘油三酯-葡萄糖指数（TyG指数）作为预测指标并建立临床预测模型。我们纳入了551例无房颤病史的急性心肌梗死（AMI）患者。根据住院期间术后NOAF的发生情况将这些患者分为两组：NOAF组（n = 94）和窦性心律（SR）组（n = 457）。我们使用回归模型分析NOAF的危险因素并建立预测模型。使用受试者工作特征（ROC）、校准曲线、决策曲线分析和临床影响曲线评估预测性能、校准和临床有效性。94例患者在住院期间发生了NOAF。TyG被确定为NOAF的独立预测因子，且在NOAF组中显著更高。左心房（LA）直径、年龄、全身炎症反应指数（SIRI）和肌酐也被确定为NOAF的危险因素。将这些因素与TyG相结合构建临床预测模型，曲线下面积（AUC）为0.780（95%CI 0.358 - 0.888）。ROC、校准曲线、决策曲线分析和临床影响曲线表明新列线图的性能令人满意。通过将TyG指数纳入预测模型，可以有效预测住院期间AMI后发生的NOAF。早期检测NOAF可显著改善AMI患者的预后。
