作者
Huijun Jin,Xuefeng Xu,Chun Ma,Xinghai Hao,Jinglan Zhang
摘要
We determined utilizing a sepsis participant cohort whether there is a significant association between TyG-BMI (triglyceride glucose body mass index) and mortality rates at any stage. Herein, a historical cohort investigation approach was adopted, using information provided by the Medical Information Mart for Intensive Care-IV (MIMIC-IV). We categorized the included individuals in accordance with their TyG-BMI data quartiles, and the primary outcomes were mortality during the hospital stay and death rate due to any reason at postadmission day 28, 90, and 365. To evaluate TyG-BMI mortality's relationship with sepsis-induced mortality risk, we employed restricted cubic spline regression (RCS) and Cox regression models. Additionally, we confirmed TyG-BMI's significant predictive value for mortality via machine learning methods. Furthermore, we performed subgroup analyses to investigate possible differences among various patient groups. The cohort included 4759 individuals, aged 63.9 ± 15.0 years, involving 2885 males (60.6%). The rates of death that took place during hospital stay and at 28, 90 and 365 days postadmission were respectively 19.60%, 24.70%, 28.80%, and 35.20%. As reflected by Cox models, TyG-BMI was negatively associated with mortality risk at various intervals: in-hospital [hazard ratio (HR) 0.47 (0.39–0.56), P = 0.003], 28 days postadmission [HR 0.42 (0.35–0.49), P < 0.001], 90 days postadmission [HR 0.41 (0.35–0.48), P < 0.001], and 365 days postadmission [HR 0.41 (0.35–0.47), P < 0.001]. Additionally, the relationship between TyG-BMI and death rates was L-shaped, as reflected by the RCS, with a TyG-BMI of 249 being the turning point. Among sepsis patients in critical care, TyG-BMI is negatively correlated with mortality possibility at various intervals: during hospital stay and 28 days, 90 days, and one year postadmission. TyG-BMI is a beneficial parameter for categorizing risk levels among sepsis patients and for predicting their mortality risk within one year.