ArticleBMC medical informatics and decision making2026
Lactate-albumin ratio predicts in-hospital mortality in critically Ill patients with congestive heart failure and diabetes.
Article in BMC medical informatics and decision making, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
objectiveTo evaluate the predictive utility of the initial lactate-to-albumin ratio (LAR) measured within 24 h of admission for in-hospital all-cause mortality in critically ill patients with congestive heart failure (CHF) and diabetes mellitus (DM).
methodsA retrospective cohort study was performed using the Medical Information Mart for Intensive Care IV (MIMIC-IV; n = 960) and the eICU Collaborative Research Database (eICU-CRD; n = 1,850). Kaplan-Meier curves, Cox regression, restricted cubic splines (RCS), subgroup analyses, and five machine learning models were applied, with predictive performance assessed via receiver operating characteristic (ROC), calibration curves, and decision curve analysis (DCA).
resultsThe highest LAR quartile (Q4) was associated with higher in-hospital mortality (MIMIC-IV: 50.83%; eICU-CRD: 29.71%) than lower quartiles (all P < 0.001). LAR was identified as an independent predictor of in-hospital mortality (MIMIC-IV: HR = 1.878, P = 0.009; eICU-CRD: HR = 3.141, P < 0.001). RCS analysis revealed a positive association between LAR and in-hospital mortality. The most rapid slope change occurred at LAR = 2.73 in the MIMIC-IV cohort (nonlinear P = 0.072) and at LAR = 2.50 in the eICU-CRD cohort (nonlinear P < 0.001). For both outcomes, higher discriminative performance was observed for LAR than for lactate alone in both cohorts. Model performance was further improved when incorporating into machine learning models.
conclusionInitial LAR is a reliable predictor of in-hospital mortality in critically ill CHF-DM patients.
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