Indicators of insulin resistance as predictors of 28-day mortality in patients with VA-ECMO: a retrospective study
Year: 2025
Type: article
Study design: Observational Study
Abstract: Background: 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. Methods: 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). Results: < 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. Conclusion: 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.
Source: Frontiers in Medicine
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,801
OpenAlex ID: W4410607403