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<p>Hemorrhagic stroke (HS) is a severe condition with high mortality. Identifying high-risk patients for early intervention remains challenging. The triglyceride-glucose-body mass index (TyG-BMI), a simple and cost-effective marker of metabolic dysfunction, has shown potential as a predictor of all-cause mortality (ACM) in critically ill HS patients. This study aims to evaluate TyG-BMI's role in predicting mortality and its clinical utility in risk stratification. This retrospective cohort study analyzed critically ill hemorrhagic stroke patients in the MIMIC-IV database. Kaplan–Meier curves assessed ACM across TyG-BMI groups, and Cox regression models explored the association between TyG-BMI and ACM. Restricted cubic spline (RCS) analysis identified nonlinear relationships, and subgroup analyses examined variations across clinical populations. A total of 1,121 patients with hemorrhagic stroke were included in the final analysis. Multivariable Cox regression demonstrated that patients in the highest TyG-BMI quartile had significantly elevated all-cause mortality risk compared to those in the lowest quartile after full adjustment for clinical confounders (HR 1.891, 95% CI 1.24–2.89; P = 0.003). RCS analysis revealed a predominantly linear association between TyG-BMI and mortality (P for nonlinearity &gt; 0.05). Using a clinically relevant threshold of 211.32 (corresponding to the cohort median), patients with TyG-BMI ≥ 211.32 exhibited 1.82-fold higher mortality risk (95% CI: 1.45–2.28; <i>P</i> &lt; 0.001) compared to those below this threshold. The addition of TyG-BMI to the APACHE-IV scoring system significantly improved predictive accuracy for in-hospital mortality (ΔAUC = 0.118, 95% CI 0.053–0.183; <i>P</i> = 0.002). This study demonstrates that TyG-BMI is an independent risk factor for ACM in critically ill HS patients, providing strong evidence to support its use as a reliable biomarker for identifying high-risk patients with elevated mortality.</p>

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Association of the triglyceride-glucose-body mass index with all-cause mortality in critically ill patients with hemorrhagic stroke: a retrospective cohort study from the MIMIC-IV database

  • 期刊论文

  • 发表日期：

    2026年1月24日

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  • ZhenKun Xiao,
  • YongHong Duan,
  • YiBo Yang,
  • FeiYiFan Wang,
  • Bing Wang,
  • AiHua Liu

摘要

Hemorrhagic stroke (HS) is a severe condition with high mortality. Identifying high-risk patients for early intervention remains challenging. The triglyceride-glucose-body mass index (TyG-BMI), a simple and cost-effective marker of metabolic dysfunction, has shown potential as a predictor of all-cause mortality (ACM) in critically ill HS patients. This study aims to evaluate TyG-BMI's role in predicting mortality and its clinical utility in risk stratification. This retrospective cohort study analyzed critically ill hemorrhagic stroke patients in the MIMIC-IV database. Kaplan–Meier curves assessed ACM across TyG-BMI groups, and Cox regression models explored the association between TyG-BMI and ACM. Restricted cubic spline (RCS) analysis identified nonlinear relationships, and subgroup analyses examined variations across clinical populations. A total of 1,121 patients with hemorrhagic stroke were included in the final analysis. Multivariable Cox regression demonstrated that patients in the highest TyG-BMI quartile had significantly elevated all-cause mortality risk compared to those in the lowest quartile after full adjustment for clinical confounders (HR 1.891, 95% CI 1.24–2.89; P = 0.003). RCS analysis revealed a predominantly linear association between TyG-BMI and mortality (P for nonlinearity > 0.05). Using a clinically relevant threshold of 211.32 (corresponding to the cohort median), patients with TyG-BMI ≥ 211.32 exhibited 1.82-fold higher mortality risk (95% CI: 1.45–2.28; P < 0.001) compared to those below this threshold. The addition of TyG-BMI to the APACHE-IV scoring system significantly improved predictive accuracy for in-hospital mortality (ΔAUC = 0.118, 95% CI 0.053–0.183; P = 0.002). This study demonstrates that TyG-BMI is an independent risk factor for ACM in critically ill HS patients, providing strong evidence to support its use as a reliable biomarker for identifying high-risk patients with elevated mortality.