Saturation effect of anion gap in predicting long-term mortality in critically ill patients with chronic obstructive pulmonary disease (COPD): A retrospective cohort study based on the MIMIC-IV database
Clinical Snapshot
PICO Framework
| P — Population | Critically ill patients with COPD admitted to intensive care units (n=2594) |
| I — Intervention | Anion gap measurement at ICU admission |
| C — Comparator | Different anion gap levels (quartile analysis) |
| O — Outcomes | 365-day mortality following ICU admission |
Bottom Line
This large retrospective cohort study demonstrates that anion gap is an independent predictor of 365-day mortality in critically ill COPD patients, with a saturation effect at 14 mmol/L. While the association is statistically significant (HR 1.03 per mmol/L), the clinical effect size is modest. The study's strength lies in its large sample size and robust statistical methodology, but generalisability is limited by single-database design and lack of external validation. For Australian intensivists, anion gap could serve as an additional prognostic marker in COPD patients, particularly when elevated above 14 mmol/L, but should complement rather than replace established prognostic tools. The findings support incorporating anion gap into routine risk assessment, though the modest effect size suggests it's most useful as part of a broader prognostic framework rather than a standalone predictor.
Key Findings
P Value: P < 0.05
Effect Size: Adjusted hazard ratio 1.03 per 1 mmol/L increase in anion gap
Primary Outcome: 365-day mortality: 36.24% of critically ill COPD patients
Nnt Or Sensitivity: Quartile 4 vs Q1: HR 1.49 (1.19-1.87); saturation threshold at 14 mmol/L
Confidence Interval: 95% CI: 1.01-1.05
Clinical Application
Highly feasible - anion gap is routine laboratory test available in all Australian ICUs Relevant to Australian intensive care practice; aligns with ANZICS guidelines for COPD management and prognostication Critically ill COPD patients requiring ICU admission
Abstract
The relationship between anion gap (AG) and long-term mortality in intensive care unit has been widely reported, but whether this association exists in critically ill patients with chronic obstructive pulmonary disease (COPD) is still unknown. The data of this study were collected from the Medical Information Mart for Intensive Care-IV. First of all, we used the Cox regression analysis and Kaplan-Meier curves to measure the relationship between AG and 365-day mortality for critically ill patients with COPD. Next, a restricted cubic spline was used to analyze the relationship between AG and mortality. Finally, age, sex, weight, hypertension, type 2 diabetes mellitus, heart failure, myocardial infarction, and chronic kidney disease were considered for subgroup analysis. A total of 2594 eligible subjects were sampled, of which 36.24% died within 365 days of intensive care unit admission. Cox regression analysis, after adjusting for confounders, demonstrated a significant association between AG and 365-day mortality in patients with COPD (hazard ratio = 1.03, 95% confidence interval: 1.01-1.05. P < .05). Stratifying AG into quartiles revealed higher levels of AG associated with an increased risk of death (Q1: 1.00, Q2: 1.34 [1.09-1.66], Q3: 1.44 [1.17-1.78], and Q4: 1.49 [1.19-1.87]). Additionally, restricted cubic spline analysis indicated a nonlinear relationship, with a critical value of AG at 14 mmol/L. Subgroup analysis highlighted AG as a significant predictor of long-term mortality in COPD patients across different subgroups, with an interaction effect observed in the subgroup with type 2 diabetes mellitus. In critically ill patients with COPD, there was a significant positive association between AG and 365-day mortality. In addition, there is a saturation effect at AG of 14 mmol/L.
References
- 1.Xiu, X., Yang, Z., & Chen, X. (2026). Saturation effect of anion gap in predicting long-term mortality in critically ill patients with chronic obstructive pulmonary disease (COPD): A retrospective cohort study based on the MIMIC-IV database. Medicine, 105(16), e41364. https://doi.org/10.1097/MD.0000000000041364
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