Scholarlink

<p><?tk 3?>This study aims to evaluate the relationship between triglyceride-glucose (TyG) index variability and in-hospital mortality across two intensive care unit (ICU) databases (MIMIC-IV and SWH) and to determine if TyG variability provides more prognostic value than baseline TyG. This retrospective observational study utilized data from the MIMIC-IV (2008–2019) and SWH (2016–2023) ICU cohorts. TyG metrics, including baseline (TyG-BL), median (TyG-Median), mean (TyG-Average), standard deviation (TyG-STD), range (TyG-Range), and coefficient of variation (TyG-CV), were calculated. The association between TyG metrics and all-cause in-hospital mortality was evaluated using multivariable logistic regression. Nonlinear relationships were explored using restricted cubic splines (RCS). Cox proportional hazards models and subgroup analyses were conducted as sensitivity analyses, and post-discharge mortality at 7&#xa0;days, 1&#xa0;year, and 2&#xa0;years was additionally assessed. A total of 2,208 ICU patients were included (MIMIC: n = 1707; SWH: n = 501). In MIMIC, TyG variability metrics (TyG-STD, TyG-Range) were independently associated with mortality, with TyG-STD showing an odds ratio (OR) of 1.66 (95% confidence interval [CI] 1.06–2.61, <i>P</i> = 0.027) and TyG-Range an OR of 1.24 (95% CI 1.05–1.46, <i>P</i> = 0.011). TyG variability metrics in SWH showed similar trends, but associations attenuated after full adjustment. Kaplan–Meier analysis demonstrated clear survival curve separation for TyG variability metrics in MIMIC, while the SWH cohort showed weaker separation. RCS analysis revealed a nonlinear relationship between TyG metrics and mortality risk in MIMIC, with a steeper increase in risk at higher TyG values. In Cox analyses, baseline TyG was associated with the timing of in-hospital death in MIMIC, whereas TyG variability metrics were not. No significant associations were observed between TyG metrics and post-discharge mortality. TyG variability, rather than a single baseline TyG measurement, is associated with in-hospital mortality in critically ill patients, particularly in the MIMIC cohort. These findings suggest that dynamic metabolic instability may provide clinically relevant prognostic information beyond static measurements. Further prospective and multicenter studies are warranted to validate the role of TyG variability in ICU risk stratification.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Association of triglyceride-glucose index fluctuation with in-hospital all-cause mortality in critically ill patients: a multidatabase retrospective study

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月18日

查看全文
  • Zuzhi Chen,
  • Xiang Xiang,
  • Haoran Xu,
  • Ting Zhao,
  • Weiguang Zhang,
  • Xiaofei Xie,
  • Zhi Dou,
  • Changlin Yin

摘要

This study aims to evaluate the relationship between triglyceride-glucose (TyG) index variability and in-hospital mortality across two intensive care unit (ICU) databases (MIMIC-IV and SWH) and to determine if TyG variability provides more prognostic value than baseline TyG. This retrospective observational study utilized data from the MIMIC-IV (2008–2019) and SWH (2016–2023) ICU cohorts. TyG metrics, including baseline (TyG-BL), median (TyG-Median), mean (TyG-Average), standard deviation (TyG-STD), range (TyG-Range), and coefficient of variation (TyG-CV), were calculated. The association between TyG metrics and all-cause in-hospital mortality was evaluated using multivariable logistic regression. Nonlinear relationships were explored using restricted cubic splines (RCS). Cox proportional hazards models and subgroup analyses were conducted as sensitivity analyses, and post-discharge mortality at 7 days, 1 year, and 2 years was additionally assessed. A total of 2,208 ICU patients were included (MIMIC: n = 1707; SWH: n = 501). In MIMIC, TyG variability metrics (TyG-STD, TyG-Range) were independently associated with mortality, with TyG-STD showing an odds ratio (OR) of 1.66 (95% confidence interval [CI] 1.06–2.61, P = 0.027) and TyG-Range an OR of 1.24 (95% CI 1.05–1.46, P = 0.011). TyG variability metrics in SWH showed similar trends, but associations attenuated after full adjustment. Kaplan–Meier analysis demonstrated clear survival curve separation for TyG variability metrics in MIMIC, while the SWH cohort showed weaker separation. RCS analysis revealed a nonlinear relationship between TyG metrics and mortality risk in MIMIC, with a steeper increase in risk at higher TyG values. In Cox analyses, baseline TyG was associated with the timing of in-hospital death in MIMIC, whereas TyG variability metrics were not. No significant associations were observed between TyG metrics and post-discharge mortality. TyG variability, rather than a single baseline TyG measurement, is associated with in-hospital mortality in critically ill patients, particularly in the MIMIC cohort. These findings suggest that dynamic metabolic instability may provide clinically relevant prognostic information beyond static measurements. Further prospective and multicenter studies are warranted to validate the role of TyG variability in ICU risk stratification.