Evidence map›Paper›PMID 42369138›Full record

ArticleFrontiers in medicine2026

Dynamic prognostic prediction in sepsis using longitudinal blood gas trajectories: development and external validation.

Le-Run Zong, Qian-Yu Bi, Yang Liu, Shu-Jiao Lu, Cun-Yang Li, Ze-Kun Wei, Te-Jin Ba, Li Kong, Fei-Hu Zhang

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Article in Frontiers in medicine, 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.

Le-Run ZongShandong University of Traditional Chinese Medicine, Jinan, China.
Qian-Yu BiDepartment of Emergency Center, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China.
Yang LiuDepartment of Emergency Center, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China.
Shu-Jiao LuShandong University of Traditional Chinese Medicine, Jinan, China.
Cun-Yang LiShandong University of Traditional Chinese Medicine, Jinan, China.
Ze-Kun WeiShandong University of Traditional Chinese Medicine, Jinan, China.
Te-Jin BaDepartment of Emergency and Critical Care Medicine, International Mongolian Medical Hospital of Inner Mongolia Autonomous Region, Hohhot, China.
Li KongDepartment of Emergency Center, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China.
Fei-Hu ZhangDepartment of Emergency Center, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To develop and externally validate a dynamic prognostic framework for sepsis based on longitudinal arterial blood gas trajectories, and to assess whether ABG-derived dynamic features provide incremental prognostic information beyond conventional clinical variables. Methods: This retrospective observational study used MIMIC-IV for model development and internal validation, and eICU-CRD for independent external validation. Adult ICU patients with sepsis and sufficient repeated ABG measurements within 168 h were included. Eight ABG-related variables were analyzed: PaO Results: After applying the longitudinal ABG completeness criteria, 1,378 patients from MIMIC-IV and 647 from the eICU-CRD external validation cohort were included. Clinically interpretable trajectories were identified for all eight ABG variables, suggesting dynamic physiological heterogeneity in sepsis. For post-landmark in-hospital mortality prediction, models integrating clinical variables, ABG trajectory posterior probabilities, and ABG summary statistics showed moderate discrimination. In MIMIC-IV internal validation, ROC-AUCs using data accrued up to days 3, 5, and 7 were 0.792, 0.796, and 0.808, respectively; the corresponding values in eICU-CRD external validation were 0.658, 0.679, and 0.703. Compared with the clinical reference model, adding ABG-derived dynamic features yielded small-to-moderate improvements in external validation, with ROC-AUC increases of 0.036, 0.044, and 0.055 across the three landmarks. However, gains were smaller in internal validation and not uniformly robust across settings. Incident septic shock analyses were limited by few post-landmark events, particularly in eICU-CRD, and were considered exploratory. Conclusion: Early longitudinal ABG trajectories in sepsis may capture dynamic physiological heterogeneity and provide limited complementary information beyond conventional clinical variables. Given the modest incremental benefit and inconsistent performance across cohorts, ABG-derived dynamic features should be regarded as adjunctive to clinical severity scores and clinical judgment. Their clinical utility requires prospective validation.

Indexed as

arterial blood gasdynamic predictiongrowth mixture modelin-hospital mortalitysepsistrajectory analysis

Identifiers

PMID42369138
PMCPMC13303747

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