Association between albumin-corrected anion gap and 60-day all-cause mortality in critically ill shock patients

Albumin-corrected anion gap ACAG can accurately reflect the patient's acid-base status and metabolic balance, and may have the potential to predict the prognosis of shock. The purpose of this study was to evaluate the predictive value of ACAG for 60-day all-cause mortality in shock patients admitted to the intensive care unit.

We collected data from the Critical Care Medical Information Market IV (MIMIC-IV, version 3.1) database for patients who met a clear diagnosis of shock and recorded absolute values of anion gap and albumin after admission. Then, we used the ROC curve to evaluate the predictive relationship of ACAG on 60-day all-cause mortality in shock patients and derived the risk threshold, and used different methods to further evaluate the risk threshold. Kaplan-Meier survival curves were plotted to assess 60-day survival probabilities. We employed the change-in-estimate criterion to select confounding factors, ultimately incorporating SAPSII and LAC along with ACAG to construct a Cox proportional hazards model with time-dependent covariates.

We ultimately included 10,144 eligible patients from the MIMIC-IV database and described a non-linear association between ACAG and 60-day all-cause mortality. ACAG achieved an AUC of 0.68 (95% CI 0.67-0.69) for predicting 60-day mortality, with significantly superior discriminatory performance to AG (DeLong test, p < 0.001). We found that 19.25 is the risk threshold for the ACAG indicator. Kaplan-Meier survival curve showed that the 60-day survival probability in the high ACAG group was significantly lower than that in the low ACAG normal group (P < 0.001). In the Cox proportional hazards model with time-dependent covariates, the main effect of ACAG indicated that patients in the high ACAG group had a 72.5% higher risk of death compared to those in the low ACAG group (HR = 1.725, 95% CI 1.472-2.023, p < 0.001), while the time-dependent effect of ACAG was not statistically significant (p = 0.08).By combining ACAG with each scoring system (LODS, OASIS, SOFA, SAPSII) separately, it was found that the combination was better than the single ACAG value in predicting 60-day all-cause mortality in shock patients.

ACAG, derived from a rapid and low-cost formula, serves as an early predictor of mortality in shock patients. Its clinical utility is evidenced by a statistically significant association with 60-day mortality in high ACAG patients admitted to the ICU. Furthermore, integrating ACAG with established scoring systems could improve predictive accuracy.