Zhou You, Cheng Zhi, Gu Pingping, Zhang Yu, Xu Wanying, Wang Xin
Department of Critical Care Medicine, The Second Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Binhai County People's Hospital, Yancheng, China.
Front Med (Lausanne). 2025 May 22;12:1559780. doi: 10.3389/fmed.2025.1559780. eCollection 2025.
Insulin resistance is closely related to adverse outcomes in critical illness, but its predictive value in patients with cardiogenic shock receiving venous-arterial extracorporeal membrane oxygenation (VA-ECMO) remains unclear.
Patients with cardiogenic shock who received VA-ECMO treatment were retrospectively included. To evaluate the associations of insulin resistance indicators such as triglyceride-glucose (TyG), metabolic score for insulin resistance (METS-IR), triglyceride-to-high-density-lipoprotein cholesterol ratio (TG/HDL-C), and triglyceride glucose-body mass index (TyG-BMI) with 28-day mortality. A multi-stage modeling strategy was adopted. Firstly, risk factors were screened through univariate and multivariate Cox regression; Further combine Least Absolute Shrinkage and Selection Operator (LASSO) regression (L1 regularization), random forest and gradient boosting machine (GBM) for multi-method feature screening, and use ridge regression (L2 regularization) to control collinearity to construct a joint prediction model; Finally, the model efficacy was verified through C-index, time-dependent receiver operating characteristic (ROC) curve, calibration curve, decision curve analysis (DCA), net reclassification improvement index (NRI), and comprehensive discriminant improvement index (IDI).
TyG, METS-IR, TG/HDL-C, and TyG-BMI independently predicted an increased risk of death (all < 0.01). Five core predictors were determined through multi-method screening: Sequential Organ Failure Assessment (SOFA) score, TyG, TG/HDL-C, hypertension, and diabetes. The joint model performed excellently in both the training set and the validation set (Training set: area under curve (AUC) = 0.923, C index = 0.847, NRI = 0.699, IDI = 0.175); Validation set: AUC = 0.901, C index = 0.846, NRI = 0.574, IDI = 0.148), and the DCA and calibration curve show its good efficacy.
Insulin resistance indicators (TyG, TG/HDL-C) can independently and gradually predict the risk of death in patients with VA-ECMO. The model combined with indicators such as SOFA score has high discriminative power and clinical practicability. This provides new evidence for risk stratification based on the integration of metabolism and organ function, supporting the research exploration of targeted intervention for insulin resistance to improve prognosis.
胰岛素抵抗与危重症的不良结局密切相关,但其在接受静脉-动脉体外膜肺氧合(VA-ECMO)治疗的心源性休克患者中的预测价值尚不清楚。
回顾性纳入接受VA-ECMO治疗的心源性休克患者。评估甘油三酯-葡萄糖(TyG)、胰岛素抵抗代谢评分(METS-IR)、甘油三酯与高密度脂蛋白胆固醇比值(TG/HDL-C)以及甘油三酯葡萄糖-体重指数(TyG-BMI)等胰岛素抵抗指标与28天死亡率的相关性。采用多阶段建模策略。首先,通过单因素和多因素Cox回归筛选危险因素;进一步结合最小绝对收缩和选择算子(LASSO)回归(L1正则化)、随机森林和梯度提升机(GBM)进行多方法特征筛选,并使用岭回归(L2正则化)控制共线性以构建联合预测模型;最后,通过C指数、时间依赖性受试者工作特征(ROC)曲线、校准曲线、决策曲线分析(DCA)、净重新分类改善指数(NRI)和综合判别改善指数(IDI)验证模型效能。
TyG、METS-IR、TG/HDL-C和TyG-BMI均独立预测死亡风险增加(均P<0.01)。通过多方法筛选确定了五个核心预测因素:序贯器官衰竭评估(SOFA)评分、TyG、TG/HDL-C、高血压和糖尿病。联合模型在训练集和验证集上均表现出色(训练集:曲线下面积(AUC)=0.923,C指数=0.847,NRI=0.699,IDI=0.175;验证集:AUC=0.901,C指数=0.846,NRI=0.574,IDI=0.148),DCA和校准曲线显示其效能良好。
胰岛素抵抗指标(TyG、TG/HDL-C)可独立且逐步预测VA-ECMO患者的死亡风险。结合SOFA评分等指标的模型具有较高的判别力和临床实用性。这为基于代谢与器官功能整合的风险分层提供了新证据,支持针对胰岛素抵抗进行靶向干预以改善预后的研究探索。