BackgroundInsulin resistance (IR) is a significant risk factor for cardiometabolic diseases and a defining feature of type 2 diabetes mellitus (T2DM). This study aimed to examine the potential value of triglyceride-glucose (TyG) index as a predictor of prognosis in coronary heart disease (CHD) patients with T2DM after coronary artery bypass grafting (CABG) surgery and to facilitate the identification of those at high risk of major adverse cardiovascular events (MACEs) for closer monitoring or possible early intervention.MethodsThis study enrolled 386 T2DM patients who underwent CABG surgery at Nanjing Drum Tower Hospital. Patients were separated into two groups according to the median preoperative TyG Index. The Kaplan-Meier plot was used to compare the rate of MACEs-free survival in T2DM patients after CABG. The independent risk factors for the occurrence of MACEs were investigated using multivariate analysis. Nomogram was used to depict the predictive model.ResultsSignificantly more MACEs occurred in individuals with higher medians of the TyG index (65 (33.7%) vs. 39 (20.2%), p=0.003). TyG index [hazard ratio (HR) 12.926], LVEF [hazard ratio (HR) 0.916], and NYHA functional class III/IV [hazard ratio (HR) 4.331] were identified as independent predictors of MACEs incidence in post-CABG T2DM patients by multivariate analysis. The area under the curve (AUC) for predicting MACEs using the TyG index was 0.89 at five years. Combining the TyG index, LVEF, and NYHA functional class III/IV to build a novel risk assessment model for postoperative MACEs, the AUC climbed to 0.93 at five years. With AUCs, the nomogram comprised of the TyG index, LVEF, and NYHA functional class III/IV demonstrated strong specificity in the training and test sets.ConclusionsThe incidence of MACEs is high among post-CABG T2DM patients with a high TyG index. TyG index improves the diagnostic accuracy of MACEs, especially at long-term follow-up. A high TyG index may serve as an early warning signal for individuals to undertake lifestyle adjustments that can reduce the progression or incidence of MACEs.

Purpose The postoperative survival of patients with acute type A aortic dissection (aTAAD) remains unsatisfactory. The current study developed an easy-to-use survival prediction model and calculator. Methods A total of 496 patients with aTAAD undergoing surgical repair were included in this study. The systemic immune-inflammation index (SII) and other clinical features were collected and subjected to logistic and Cox regression analyses. The survival prediction model was based on Cox regression analyses and exhibited as a nomogram. For convenience of use, the nomogram was further developed into calculator software. Results We demonstrated that a higher preoperative SII was associated with in-hospital death (OR: 4.116, p < 0.001) and a higher postoperative overall survival rate (HR: 2.467, p < 0.001) in aTAAD patients undergoing surgical repair. A survival prediction model and calculator based on SII and four other clinical features were developed. The overall C-index of the model was 0.743. The areas under the curves (AUCs) of the 1- and 3-month and 1- and 3-year survival probabilities were 0.73, 0.71, 0.71 and 0.72, respectively. The model also showed good calibration and clinical utility. Conclusion Preoperative SII is significantly associated with postoperative survival. Based on SII and other clinical features, we created the first easy-to-use prediction model and calculator for predicting the postoperative survival rate in aTAAD patients, which showed good prediction performance.