Evidence map›Paper›PMID 40822945›Full record

ArticleFrontiers in endocrinology2025

Association between serum anion gap trajectory and mortality in hospitalized patients with sepsis: an analysis of the MIMIC-IV database.

Lijuan Jing, Xiaopeng Shi, Lijun Xu, Xiangmei Zhao, Faliang Li, Lijie Qin

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Article in Frontiers in endocrinology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

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9citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Lijuan JingDepartment of Emergency Medicine, Henan Provincial People's Hospital, Zhengzhou, China.
Xiaopeng ShiDepartment of Emergency Medicine, Henan Provincial People's Hospital, Zhengzhou, China.
Lijun XuDepartment of Emergency Medicine, Henan Provincial People's Hospital, Zhengzhou, China.
Xiangmei ZhaoDepartment of Emergency Medicine, Henan Provincial People's Hospital, Zhengzhou, China.
Faliang LiDepartment of Emergency Medicine, Henan Provincial People's Hospital, Zhengzhou, China.
Lijie QinDepartment of Emergency Medicine, Henan Provincial People's Hospital, Zhengzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Sepsis remains a leading cause of mortality in intensive care units (ICUs), with high morbidity and healthcare costs worldwide. The serum anion gap (AG), a marker of metabolic acidosis, has been associated with adverse outcomes in various critical illnesses. However, the prognostic value of longitudinal AG trajectories in sepsis remains underexplored. This study explored the link between dynamic AG trajectories and all-cause mortality in critically ill septic patients. Methods: A retrospective cohort study utilized data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Adult patients meeting Sepsis-3 criteria for sepsis were included. Group-based trajectory modeling was used to identify AG trajectories during the initial five days of ICU admission. Patients were classified into three trajectory groups: normal-level-stable trajectory (Class 1), high-level-decline trajectory (Class 2), and progressive acidosis trajectory (Class 3). Cox proportional hazards models evaluated the link between AG trajectories and ICU/hospital mortality, controlling for demographic, laboratory, and clinical severity factors. Subgroup and sensitivity analyses were performed to validate the findings. Results: Among 6,110 septic patients, three distinct AG trajectory groups were identified. Patients in Class 3 (decreasing high AG) had the highest mortality, with ICU mortality of 30.61% and hospital mortality of 35.85%, compared to Class 1 (ICU mortality: 14.46%, hospital mortality: 19.41%) and Class 2 (ICU mortality: 21.88%, hospital mortality: 31.88%). In fully adjusted models, Class 3 exhibited a significantly increased risk of ICU mortality [HR=1.72, (95% CI 1.43-2.07), P<0.001] and hospital mortality [HR=1.64, (95% CI 1.39-1.94), P<0.001] relative to Class 1. Subgroup analysis revealed a significant interaction between AG trajectories and heart failure status. Sensitivity analysis excluding patients with malignancies confirmed the robustness of the findings. Conclusion: Continuous monitoring of AG levels is crucial for risk assessment and personalized treatment, as rising AG levels significantly increase mortality risk. These findings underscore the potential of AG trajectories as a dynamic biomarker to improve sepsis management and patient outcomes.

Indexed as

Acid-Base EquilibriumHospital MortalitySepsisAgedBiomarkersCritical IllnessDatabases, FactualFemaleHospitalizationHumansIntensive Care UnitsMaleMiddle AgedPrognosisRetrospective StudiesBiomarkersanion gapintensive care unitmortalitysepsistrajectory analysis

Identifiers

PMID40822945
PMCPMC12353743

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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.