Unmasking silent bioenergetic failure in normolactatemic AMI patients: a multicenter validation of the TyG-ACAG index

Scores such as SOFA and blood lactate, which reflect hemodynamic compromise, may fail to capture early metabolic dysfunction in critically ill patients with Acute Myocardial Infarction (AMI). We aimed to develop and validate the TyG-ACAG index—a novel integrated marker of insulin resistance and metabolic acidosis—and evaluate its incremental value for risk stratification. This multicenter, retrospective cohort study was derived in 2,277 adult AMI patients from the eICU-CRD and validated in 738 patients from the MIMIC-IV and Tongji Hospital databases. The TyG-ACAG index was calculated within 24 hours of ICU admission. Associations with all-cause in-hospital mortality were assessed using multivariable logistic regression with Inverse Probability of Treatment Weighting (IPTW). Incremental predictive value over a baseline model (Age + SOFA) was quantified using the Net Reclassification Improvement (NRI). Additionally, we performed discordance analysis and utilized SHAP values from an XGBoost model to interpret feature importance. A high TyG-ACAG index (>129.45) was independently associated with increased in-hospital mortality (OR = 1.723; 95% CI: 1.343–2.212; P < 0.001). The inclusion of the index significantly refined risk stratification, yielding an NRI of 0.381 (P < 0.001) over the baseline model. Crucially, discordance analysis unmasked a high-risk phenotype: patients with clinically normal lactate (<2.0 mmol/L) but elevated TyG-ACAG levels had significantly higher mortality (HR = 3.95; 95% CI: 2.08–7.48; P < 0.001). Machine learning analysis corroborated these findings, ranking the index as the third most important predictor. Subgroup analysis further highlighted its robust prognostic value, particularly in younger patients (<65 years, OR = 11.84). The TyG-ACAG index serves as a robust indicator of bioenergetic failure, providing prognostic information that complements traditional hemodynamic markers. By identifying high-risk individuals masked by normolactatemia, this novel metric effectively bridges the gap in early metabolic risk assessment for critically ill AMI patients.