Prognostic significance of albumin corrected anion gap in patients with acute pancreatitis: a novel perspective

This study aims to explore the relationship between the albumin-corrected anion gap (ACAG) and short- and long-term all-cause mortality (ACM) in patients with acute pancreatitis (AP) managed in the intensive care unit (ICU). We conducted a retrospective analysis utilizing data extracted from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database. This study sought to investigate the correlation between ACAG and ACM among patients diagnosed with AP across various disease stages. R statistical software was used to identify the optimal thresholds for ACAG. Kaplan-Meier survival curves and multivariate Cox proportional hazards regression models were employed to assess the association between ACAG and short- and long-term ACM of AP. The predictive ability, sensitivity, specificity, and area under the curve (AUC) of ACAG for short- and long-term ACM in AP were investigated using receiver operating characteristic analysis. Subgroup analyses were also conducted. A cohort comprising 605 participants was included in this study. The ideal threshold for ACAG identified by R statistical software was 21.5. Cox proportional hazards modeling revealed that there was an independent association between patients with AP with ACAG ≥ 21.5 and ACM at 3, 7, 10, 14, 28, 90, and 180 days and 1 year before and after adjustment for confounders. Survival curves demonstrated that patients with ACAG ≥ 21.5 had lower survival rates at 3, 7, 10, 14, 28, 90, and 180 days and 1 year. In addition, ACAG showed superior performance, with a larger AUC than the anion gap, albumin, and Systemic Inflammatory Response Syndrome score and Sequential Organ Failure Assessment at 3, 7, 10, 14, 28, 90, and 180 days and 1 year. Subgroup analysis revealed no significant interaction between ACAG and any subgroups Elevated levels of ACAG were found to be associated with increased short- and long-term ACM in patients with AP, and ACAG may be an independent predictor of ACM at different disease stages.