Prognostic value of the triglyceride-glucose index combined with atherogenic index of plasma for all-cause mortality in critically ill patients with chronic heart failure: a machine learning-driven retrospective cohort study

BACKGROUND: The triglyceride-glucose index (TyG) and atherogenic index of plasma (AIP) are emerging metabolic biomarkers associated with cardiovascular diseases. However, their combination prognostic value in patients with critical chronic heart failure (CHF) remains unclear. This study aimed to evaluate the combined predictive effect of these two biomarkers and clarify their interactive patterns in this association. METHODS: 1,238 patients were recruited via the Medical Information Mart for Intensive Care IV (MIMIC-IV) database, with a median age of 71 years. Multivariable Cox regression, Kaplan-Meier analysis, and receiver operating characteristic (ROC) curve were employed to explore associations between TyG, AIP, and mortality. Mediation analysis was applied to assess their bidirectional mediation effects. Additionally, we developed a machine learning-driven prediction model, which was further utilized to evaluate the two indicators' incremental predictive value. RESULTS: 1,238 patients were included, with 478 (38.61%) dying during follow-up. Fully adjusted Cox regression models revealed that the high TyG and high AIP group was associated with the highest risk of mortality relative to the low TyG and low AIP group (14-day HR 2.18, 95% Cl: 1.44-3.31; 365-day HR 1.92, 95% Cl: 1.38-2.68). ROC analyses demonstrated that the combined TyG-AIP outperformed either marker alone in predicting mortality at all follow-up observation time points (all P < 0.05). Mediation analysis revealed that TyG mediated the effect of AIP on mortality across all time frames, with a more pronounced effect at 365 days (65.47%) than at 14 days (37.32%). In contrast, AIP served as a mediator in the association between TyG and short-term mortality only (14-day: 30.39%; 30-day: 25.76%). The random forest model confirmed that the incorporation of both the TyG index and AIP remarkably improved predictive capability, corroborating their combined incremental value. CONCLUSION: Combined elevation of TyG and AIP was independently related to an elevated risk of mortality in patients with critical CHF. Combined assessment of these biomarkers may facilitate the early recognition of high-risk subjects and support stage-specific metabolic interventions.