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Background Early risk stratification is crucial for improving outcomes in critically ill patients with acute myocardial infarction (AMI). Albumin-derived composite indices hold promise as convenient and effective predictive tools, but their relative efficacy and clinical utility remain unclear. Methods This two-cohort retrospective analysis utilized a derivation cohort from the MIMIC-IV public database and an external validation cohort from the ICU of Guizhou Medical University Affiliated Hospital. Six albumin-derived composite indices were evaluated. Statistical analyses employed Cox proportional hazards regression models to assess their association with mortality. Predictive performance was compared using the area under the receiver operating characteristic curve (AUC) and Delong’s test. A multivariate risk prediction model was developed based on key prognostic variables selected by multiple machine learning algorithms. Results The study included 4,850 critically ill AMI patients (4,210 in the derivation cohort, 640 in the validation cohort). Multivariable-adjusted analysis identified the red cell distribution width to Albumin Ratio (RAR), Urea nitrogen to Albumin Ratio (UAR), and Lactate Dehydrogenase to Albumin Ratio (LDAR) as independent predictors of 28-day ICU mortality. Among these, LDAR demonstrated the strongest predictive ability, with an AUC of 0.702 in the derivation cohort, a finding robustly validated externally (AUC = 0.703). Subgroup analysis indicated consistent predictive value across most populations but revealed a significant interaction with hyperlipidemia. Incorporating LDAR into traditional critical illness scores (e.g., APACHE II, SOFA) significantly improved their predictive discrimination (all Delong’s test p 0.05). A comprehensive model integrating 7 key variables (including LDAR, urea nitrogen, and lactate) selected by machine learning showed good and robust discriminative performance in both internal and external validation (AUCs of 0.767 and 0.735, respectively), significantly outperforming five traditional risk scores (all Delong’s test p 0.05). Conclusion Among the six albumin-derived composite indices, LDAR offers the best independent and incremental predictive value for 28-day ICU mortality in critically ill AMI patients. Its interaction with hyperlipidemia suggests potential for targeted risk stratification. The machine learning model incorporating LDAR and other variables demonstrates robust performance, providing a promising tool for the early clinical identification of high-risk patients.
Meng et al. (Fri,) studied this question.