Predictive value of the serum anion gap for 28-day in-hospital all-cause mortality in sepsis patients with acute kidney injury: a retrospective analysis of the MIMIC-IV database.

Abstract

The kidney is one of the most vulnerable organs in sepsis patients, which mainly manifests as sepsis-associated acute kidney injury (SA-AKI). The case fatality rate of SA-AKI is high, and thus, predicting the risk of SA-AKI-related death is hugely significant. Anion gap (AG) is an important indicator in critical illness patients. The present study aimed to analyze the predictive value of the AG for the short-term prognosis of SA-AKI patients. SA-AKI patient data from the Medical Information Mart for Intensive Care (MIMIC-IV) database were collected retrospectively. Hospitalized septic patients who meet the inclusion criteria were included in the final analysis. All laboratory test parameters only included the data generated within the first 24 hours after the patient entered the intensive care unit (ICU) and the extreme value. Univariate and multivariate logistic regression analyses were performed to analyze the risk factors related to the death of SA-AKI patients within 28 days during hospitalization in the ICU. A total of 3,684 SA-AKI patients were included, including 3,305 patients with low AG (<18 mmol/L) and 379 patients with high AG (≥18 mmol/L). Among these patients, 497 cases (13.5%) died during hospitalization, including 376 cases (11.4%) in the low AG group and 121 cases (31.9%) in the high AG group. Multivariate logistic regression analysis showed that elevated AG increased the risk of death in SA-AKI patients within 28 days during hospitalization in the ICU (odds ratio =1.2, 95% confidence interval: 1.2-1.3). Further analysis showed that the risk of death of SA-AKI patients within 28 days during hospitalization in the ICU was increased when AG ≥14 mmol/L. The relationship between AG level and the risk of death of SA-AKI patients during hospitalization was S-shaped. In clinical practice, AG levels can serve as a valuable predictor of the death risk of SA-AKI patients during hospitalization.