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Background: Acute coronary syndrome (ACS) and diabetes mellitus (DM) are the leading health risks for the elderly. Triglyceride-glucose (TyG) index is a novel and reliable indicator of insulin resistance (IR). This study aims to explore the relationship between the TyG index and all-cause mortality in oldest-old patients with ACS and DM., Methods: Seven hundred twenty hospitalized patients with ACS aged ≥ 80 years were enrolled, and 699 patients signed informed consent for the study. During the follow-up period, 37 were lost to follow-up, and the follow-up rate was 94.7%. 231 ACS patients with DM were selected for the study's analyses. Kaplan-Meier curve, Cox regression model and receiver operating characteristic (ROC) curve were used to analyze the association between the TyG index and all-cause mortality., Results: The mean age of participants was 81.58 ± 1.93 years, and 32.47% were women. Compared to TyG tertile 1, the Hazard Ratio (HR) [95% confidence interval (CI)] of all-cause mortality was 2.04 (1.09, 3.81) for TyG tertile 3 in the fully adjusted model. For the TyG index per standard deviation (SD) increment, the HR (95% CI) of all-cause mortality was 1.44 (1.13, 1.83). Further, the association between the TyG index and all-cause mortality was dose-response (P for trend = 0.026). ROC curve analyses indicated that the TyG index outperformed FBG and TG in the prediction of mortality risk and improved the prognostic value of the Gensini score combined with LVEF., Conclusion: The TyG index predicts the risk of all-cause mortality in the oldest-old ACS patients with DM., (© 2023. The Author(s).)

Purpose: To explore predictors for readmission within 6 months of ACS patients, and to build a prediction model, and generate a nomogram., Methods: The retrospective cohort study included 498 patients with ACS in the Second Medical Center of the Chinese People's Liberation Army General Hospital between January 2016 and March 2019. Univariate and multivariate logistic regression with odds ratios (OR) and two-sided 95% confidence interval (CI) analysis were used to investigate predictors for readmission within 6 months. The cohort was randomly divided into training cohort to develop a prediction model, and the validation cohort to validate the model. The receiver operating characteristic curve (ROC) and the calibration curve was used to assess discriminative power and calibration., Results: Eighty-three ACS patients were readmitted within six months, with a readmission rate of 16.67%. Predictors included ACS type, treatment, hypertension, SUA, length of stay, statins, and adverse events occurred during hospitalization were used to form a six-month readmission prediction model for readmission within 6 months in ACS patients. The area under the curve (AUC) of the model was 0.788 (95%CI: 0.735-0.878) and 0.775 (95%CI: 0.686-0.865) in the training cohort and the validation cohort, respectively. Calibration curves showed the good calibration of the prediction model. Decision-curve analyses and clinical impact curve also demonstrated that it was clinically valuable., Conclusion: We used seven readily available predictors to develop a prediction model for readmission within six months after treatment in ACS patients, which could be used to identify high-risk patients for ACS readmission., (© 2022. The Author(s).)