Relationship between albumin-corrected anion gap and short- and medium-term all-cause mortality in heart failure patients with a single ICU admission
Bibliographic record
Abstract
Studies examining the role of albumin-corrected anion gap (ACAG), an emerging promising prognostic biomarker for critical illnesses, in predicting mortality of ICU patients with heart failure (HF) are limited. We aimed to analyze the relationship between ACAG and short- and medium-term all-cause mortality in HF patients with a single ICU admission. Data on HF patients in the Medical Information Mart for Intensive Care-IV (MIMIC-Ⅳ) database were extracted and analyzed. The restricted cubic spline (RCS) model, Kaplan–Meier curve, univariate and multivariate Cox regression, propensity score matching, and mediation analysis were used to assess the association between ACAG concentrations at admission and 30-day and 365-day mortality. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the predictive ability of ACAG for mortality. A total of 4,821 patients were included in this study. The RCS model showed a linear relationship between ACAG and mortality. Based on this result, patients were divided into two groups: ACAG ≥18 mmol/L and ACAG <18 mmol/L. The Kaplan–Meier curve and multivariate Cox regression analysis demonstrated a positive association between ACAG and mortality at both time points. Propensity score matching showed 30-day and 365-day mortality rates in the high ACAG group remained significantly higher compared to the low ACAG group. SAPS II, lactate, BUN, creatinine, and hematocrit partially mediated the association between ACAG and the risk of all-cause mortality. ACAG had an AUC value of 0.647 in predicting mortality. Lactate, the most common and clinically significant unmeasured anion, contributing to ACAG elevation in critical illnesses, was found negatively associated with SpO2 and hemoglobin but positively associated with heart rate, ALT, AST, creatinine, and blood urea nitrogen. In conclusion, there is a significant positive association between ACAG and short- and medium-term all-cause mortality in HF patients with a single ICU admission. The ACAG index should be combined with other clinical markers to ensure accurate risk stratification. Clinicians should be cautious in solely relying on ACAG for decision-making.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".