Evidence map›Paper›PMID 42424360›Full record

ArticlePloS one2026

A blood gas parameter-based assessment model for predicting poor prognosis in sepsis: A retrospective analysis of the MIMIC-IV and eICU-CRD.

Xiao Chen, Huichang Zhuo, Yunpiao Wang, Daxuan Wang, Xiaoqin Li, Jiandong Lin, Xiuyu Liao, Xian Lin, Xiao Lin

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Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

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9 authors.

Xiao ChenDepartment of Intensive Care Unit, The First Affliated Hospital of Fujian Medical University, Fuzhou, Fujian, China.
Huichang ZhuoDepartment of Intensive Care Unit, The First Affliated Hospital of Fujian Medical University, Fuzhou, Fujian, China.
Yunpiao WangSchool of Nursing and Medicine, Minjiang Teachers College, Fuzhou, Fujian, China.
Daxuan WangShengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, Fuzhou, Fujian, China.
Xiaoqin LiShengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, Fuzhou, Fujian, China.
Jiandong LinDepartment of Intensive Care Unit, The First Affliated Hospital of Fujian Medical University, Fuzhou, Fujian, China.
Xiuyu LiaoDepartment of Intensive Care Unit, The First Affliated Hospital of Fujian Medical University, Fuzhou, Fujian, China.
Xian LinShengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, Fuzhou, Fujian, China.ORCID https://orcid.org/0000-0001-9043-2648
Xiao LinDepartment of Intensive Care Unit, The First Affliated Hospital of Fujian Medical University, Fuzhou, Fujian, China.

Funding

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6 · The paper itself

Abstract

backgroundBlood gas parameters are associated with sepsis prognosis. This study aimed to develop an assessment model based on blood gas parameters for predicting patient outcomes.

methodsData were retrospectively extracted from the Medical Information Mart for Intensive Care IV (MIMIC-IV) and electronic Intensive Care Unit Collaborative Research Database (eICU-CRD). A sepsis assessment model was developed using the MIMIC-IV cohort, followed by internal validation in patients with septic shock from the same database and external validation in patients with sepsis from the eICU-CRD. Bioinformatics and machine learning clarified the relationship between the model and the primary outcome of 28-day mortality in patients with sepsis and septic shock.

resultsThe sepsis assessment blood gas 3 (SABG-3), an assessment model incorporating PO2, base excess (BE), and lactate, was developed and validated as an independent predictor of 28-day mortality in patients with MIMIC-IV sepsis (odds ratio: 1.559; 95% confidence interval: 1.464-1.659; P < 0.001). Its prognostic performance was internally validated in patients with MIMIC-IV sepsis and externally validated in patients with eICU-CRD sepsis. High-risk patients identified by SABG-3 exhibited greater illness severity than low-risk ones. Sensitivity analyses across five methods confirmed the prognostic value of SABG-3 for intensive care unit patients with sepsis and septic shock. A SABG-3-derived nomogram proved superior to existing scales.

conclusionWe developed and validated a novel assessment model and nomogram to evaluate sepsis prognosis rapidly and to identify patients who may benefit from intensified treatment.

Indexed as

Blood Gas AnalysisSepsisAgedFemaleHumansIntensive Care UnitsMachine LearningMaleMiddle AgedPrognosisRetrospective StudiesShock, Septic

Identifiers

PMID42424360
PMCPMC13349094

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