Albumin-corrected anion gap as a predictor of 28-day mortality in acute respiratory distress syndrome: A machine learning-based retrospective study

Background: Acute Respiratory Distress Syndrome (ARDS) remains a critical condition associated with high mortality rates, prolonged hospitalization, and reduced quality of life despite advances in critical care. The albumin-corrected anion gap (ACAG), an emerging biomarker reflecting acid-base disturbances, has been linked to poor outcomes in various critical illnesses. However, its prognostic value for mortality in ARDS patients remains unexplored. Methods: This retrospective study analyzed data from ARDS patients admitted to intensive care units (ICUs) in the MIMIC-IV database. Patients were stratified into quartiles (Q1–Q4) based on ACAG levels. The association between ACAG and 28-day all-cause mortality was comprehensively evaluated using restricted cubic splines, Kaplan–Meier survival analysis, and Cox proportional hazards regression. We employed the Boruta algorithm and LASSO (Least Absolute Shrinkage and Selection Operator) regression to identify key predictive factors. Six machine learning algorithms were used to develop predictive models, with performance assessed by the area under the ROC curve (AUC). Results: Higher ACAG levels were significantly associated with increased 28-day mortality risk in ARDS patients (P