Evidence map›Paper›PMID 40615949›Full record

ArticleBMC nephrology2025

Albumin-corrected anion gap predicts acute kidney injury in critically ill patients with acute pancreatitis: a retrospective cohort study.

Zheng Wang, Haoyu Zhang, Xiaozhou Xie, Feng Cao, Fei Li

Abstract read
In one paragraph

Article in BMC nephrology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
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4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Zheng Wang *Department of General Surgery, Xuanwu Hospital, Capital Medical University, No.45 Changchun Street, Xicheng District, Beijing, 100053, China.
Haoyu Zhang *Department of General Surgery, Xuanwu Hospital, Capital Medical University, No.45 Changchun Street, Xicheng District, Beijing, 100053, China.
Xiaozhou XieDepartment of General Surgery, Xuanwu Hospital, Capital Medical University, No.45 Changchun Street, Xicheng District, Beijing, 100053, China.
Feng CaoDepartment of General Surgery, Xuanwu Hospital, Capital Medical University, No.45 Changchun Street, Xicheng District, Beijing, 100053, China.
Fei LiDepartment of General Surgery, Xuanwu Hospital, Capital Medical University, No.45 Changchun Street, Xicheng District, Beijing, 100053, China. feili36@ccmu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAcute kidney injury (AKI) is a common and serious complication of acute pancreatitis. Early detection of patients at high risk for AKI is critical to avoiding complications and lowering mortality. This study looked into the relationship between albumin-corrected anion gap (ACAG) and AKI in critically ill patients with AP.

methodsAll eligible patients from the Medical Information Marketplace for Critical Care IV database were considered for this study. Kaplan-Meier curves estimated the cumulative risk of AKI and in-hospital mortality. Restricted cubic splines (RCS) and multivariate Cox regression models explored the relationship between ACAG and AKI. Subgroup analyses evaluated the result robustness.

resultsA total of 714 patients were included (mean ACAG: 20.15 ± 5.64; in-hospital mortality: 13.87%; AKI incidence: 70.87%). Kaplan-Meier analysis revealed that the ACAG ≥ 20.25 group exhibited significantly higher AKI incidence and renal replacement therapy(RRT) requirement risk (P < 0.001). Multivariable Cox regression demonstrated ACAG independently associated with AKI risk in AP patients-both as a continuous (HR: 1.12 [95% CI: 1.04-1.21], P = 0.004) and categorical variable (HR: 1.89 [95% CI: 1.11-3.23], P = 0.020). Restricted cubic spline modeling confirmed a linear association between ACAG and increasing AKI risk.

conclusionElevated ACAG levels (≥ 20.25 mmol/L) at ICU admission is associated with a high risk of AKI in critically ill patients with AP.

Indexed as

Acid-Base EquilibriumAcute Kidney InjuryPancreatitisSerum AlbuminAdultAgedCohort StudiesCritical IllnessFemaleHospital MortalityHumansIncidenceMaleMiddle AgedRetrospective StudiesSerum AlbuminAcute kidney injuryAcute pancreatitisAlbumin-corrected anion gapIntensive care unit

Identifiers

PMID40615949
PMCPMC12232150

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.