Abstract
Background: Sepsis constitutes a systemic dysregulated host response to infection and remains a predominant cause of ICU mortality globally. Given the limitations of conventional prognostic models (e.g., SOFA and APACHE II), incorporating variably subjective parameters, there is a pressing need to identify robust, objective biomarkers for early mortality risk stratification. This investigation delineated the prognostic significance of the lactate-to-albumin ratio (LAR) in predicting 28-day all-cause mortality (28-DACM) among critically ill septic cases.
Methods: It was attempted to undertake a retrospective analysis utilizing the MIMIC-IV database (2008–2019), comprising 5,398 adult cases who met Sepsis-3 diagnostic criteria. Clinical and laboratory data within the initial 24-h post-ICU admission were extracted. The LASSO regression algorithm was implemented as a regularization technique to mitigate multicollinearity, enhance model generalizability, and facilitate high-dimensional feature selection. It was attempted to evaluate the prognostic utility of LAR through Kaplan-Meier (KM) survival estimation, receiver operating characteristic (ROC) curve analysis, and multivariate logistic regression modeling.
Results: LAR values were remarkably escalated in non-survivors relative to survivors (median, 0.9 vs. 0.6; P < 0.001). ROC curve analysis unveiled that LAR outperformed lactate (AUC: 63.52%), albumin (AUC: 43.34%), and the SOFA score (AUC: 59.87%), achieving the highest discriminatory capacity (AUC: 64.71%; 95% CI: 62.85–66.58%). An optimal LAR threshold of 1.032 was identified, attaining sensitivity and specificity of 45.1% and 76.6%, respectively. KM analysis uncovered remarkably attenuated 28-day survival in cases with LAR ≥1.032 (P < 0.001). Multivariate logistic regression confirmed LAR as an independent predictor of 28-DACM (OR = 1.32; P < 0.001), following adjusting for confounding variables.
Conclusions: The LAR serves as a clinically accessible, objective biomarker with superior prognostic performance relative to established indicators in association with sepsis. Its integration into early risk assessment algorithms may enhance prognostication and inform timely therapeutic decision-making. Prospective, multicenter investigations are warranted to validate its external generalizability and clinical utility.
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