Albumin-Corrected Anion Gap And Mortality In Septic Intensive Care Patients: An Analysis Using Medical Information Mart For Intensive Care IV

Summary

This retrospective study of 8,286 intensive care unit (ICU) sepsis patients from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database found that a higher albumin-corrected anion gap (ACAG) was significantly associated with increased 30-day hospital and ICU mortality, showing a nonlinear relationship with an inflection point at 23.

Abstract

Early prognostic assessment is crucial for sepsis patients in intensive care units (ICU). This study investigated the relationship between the albumin-corrected anion gap (ACAG) and clinical outcomes in ICU patients with sepsis. This retrospective cohort study analyzed 8,286 sepsis patients from the MIMIC-IV (version 2.2) database. Primary outcomes were 30-day in-hospital mortality and 30-day ICU mortality. The authors used Kaplan-Meier analysis, log-rank tests, multivariable Cox regression, and restricted cubic spline (RCS) regression to evaluate associations between ACAG and the outcome. Subgroup analyses assessed consistency across different factors. Higher ACAG levels were significantly associated with increased mortality risks. As a continuous variable, ACAG independently predicted higher in-hospital mortality in fully adjusted models (HR 1.01, 95% CI 1.01–1.02, p < 0.001). Quartile analysis revealed that Q4 patients (ACAG > 26.62) had 41% higher in-hospital mortality risk than Q1 in fully adjusted models (HR 1.41, 95% CI 1.24–1.49, p < 0.001). For ICU mortality, Q4 showed 54% higher risk than Q1 (HR 1.54, 95% CI 1.41–1.75, p < 0.001), although continuous ACAG was not significant in the fully adjusted model (p = 0.099), suggesting a threshold effect. RCS analysis identified a nonlinear association with an inflection point at ACAG = 23. Subgroup analyses confirmed consistent effects, with significant interactions for body mass index, acute kidney injury, continuous renal replacement therapy, and mechanical ventilation. Higher ACAG levels are significantly associated with increased 30-day mortality in ICU sepsis patients, showing a nonlinear pattern with an inflection point at 23. Monitoring ACAG may aid risk assessment in critically ill sepsis patients, particularly when values exceed this threshold. Further research is needed to elucidate mechanisms and validate clinical utility.

Introduction

Sepsis is a heterogeneous syndrome resulting from an inappropriate body response to infection. Its incidence and fatality rates remain high across various populations and age groups, imposing a significant burden on the global healthcare system¹. Sepsis and septic shock are critical conditions characterized by inadequate tissue perfusion and hypoxia, leading to multiple organ dysfunction and posing life-threatening risks^2,3. Despite advancements in the diagnosis and treatment of sepsis, it continues to be a major cause of high patient mortality, second only to severe cardiac conditions<....

Protocol

Data source and study design

A retrospective cohort study was performed using data sourced from version 2.2 of the Medical Information Mart for Intensive Care IV database. This database comprises two in-house systems: a comprehensive hospital-wide electronic health record (EHR) and an ICU-specific clinical information system, covering data from 2008 to 2024¹⁸. This study was performed in compliance with institutional guidelines. The MIMIC-IV database received approval from the Massachusetts Institute of Technology and the Beth Israel Deaconess Medical Center. This study was reviewed and approved by t....

Results

Among adult patients in the MIMIC-IV database, 22,517 subjects met the eligibility criteria. Initially, 152 prognostic factors were extracted from the database. Following data cleaning, 105 predictors with over 30% missing data were excluded. Ultimately, 47 prognostic factors were included in the model for analysis.

Characteristics of included patients

A total of 8,286 sepsis patients who met the study’s inclusion criteria were included. The screeni.......

Discussion

This study demonstrates that a higher albumin-corrected anion gap (ACAG) is significantly associated with increased 30-day mortality in patients with severe sepsis. A key novel finding is the nonlinear relationship between ACAG and mortality, with an inflection point at a value of 23, above which the risk escalates disproportionately. Subgroup analyses revealed significant effect modification by body mass index (BMI), acute kidney injury (AKI), continuous renal replacement therapy (CRRT) use, and mechanical ventilation, .......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Database management tool	Navicat Premium (version 16)	https://www.navicat.com	Used for executing SQL queries and extracting data from the MIMIC-IV database.
MIMIC-IV database (version 2.2)	MIT Laboratory for Computational Physiology	https://mimic.mit.edu	Source of de-identified electronic health records for critically ill patients; access requires completion of CITI training.
PostgreSQL	PostgreSQL Global Development Group	https://www.postgresql.org	Relational database management system used for querying the MIMIC-IV database.
R package: ggplot2	R Foundation for Statistical Computing	https://cran.r-project.org/package=ggplot2	Used for generating publication-quality figures, including Kaplan-Meier curves and RCS plots.
R package: mice	R Foundation for Statistical Computing	https://cran.r-project.org/package=mice	Used for multiple imputation of missing data under the missing at random assumption.
R package: rms	R Foundation for Statistical Computing	https://cran.r-project.org/package=rms	Used for restricted cubic spline (RCS) regression to model nonlinear relationships.
R package: survival	R Foundation for Statistical Computing	https://cran.r-project.org/package=survival	Provides functions for Kaplan-Meier survival analysis and Cox proportional hazards regression.
R software (version 4.3.0)	R Foundation for Statistical Computing	RRID: SCR_001905	Statistical computing environment for all data analyses, including Cox regression, RCS, and subgroup analyses.