Predictive value of triglyceride-glucose index for the occurrence of acute respiratory failure in asthmatic patients of MIMIC-IV database

Abstract This study aims to investigate the association between the triglyceride-glucose (TyG) index and the occurrence of acute respiratory failure in asthma patients. This retrospective observational cohort study utilized data from the Medical Information Mart for Intensive Care IV (MIMIC-IV 2.2) database. The primary outcome was the development of acute respiratory failure in asthma patients. Initially, the Boruta algorithm and SHapley Additive exPansions were applied to preliminarily determine the feature importance of the TyG index, and a risk prediction model was constructed to evaluate its predictive ability. Secondly, Logistic regression proportional hazards models were employed to assess the association between the TyG index and acute respiratory failure in asthma patients. Finally, subgroup analyses were conducted for sensitivity analyses to explore the robustness of the results. A total of 751 asthma patients were included in the study. When considering the TyG index as a continuous variable, logistic regression analysis revealed that in the unadjusted Model 1, the odds ratio (OR) was 2.381 (95% CI: 1.857–3.052; P 0.05). Furthermore, machine learning results indicated that an elevated TyG index was a significant feature predictive of respiratory failure in asthma patients. The baseline risk model achieved an AUC of 0.743 (95% CI: 0.679–0.808; P