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ORIGINAL RESEARCH article
Front. Med.
Sec. Intensive Care Medicine and Anesthesiology
Volume 12 - 2025 | doi: 10.3389/fmed.2025.1575237
Distinct Immunological Signatures Define Three Sepsis Recovery Trajectories: A Multi-cohort Machine Learning Study
Provisionally accepted- 1 Department of Critical Care Medicine, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- 2 Zhuzhou Lukou District People's Hospital, Zhuzhou, China
What are the distinct recovery trajectories of critically ill patients with sepsis, and can machine learning models predict these trajectories using early clinical and immunological markers?In this cohort study of 24,450 patients with sepsis, three distinct recovery trajectories were identified: rapid recovery (42.3%), slow recovery (35.8%), and deterioration (21.9%). A machine learning model incorporating clinical and immunological markers within 24 hours of ICU admission predicted these trajectories with an AUROC of 0.85 (95% CI, 0.83-0.87).Early identification of sepsis recovery trajectories may enable personalized treatment strategies and improved resource allocation in critical care settings.
Keywords: Sepsis, Recovery trajectory, machine learning, Immunological Signatures, Critically ill
Received: 12 Feb 2025; Accepted: 28 Mar 2025.
Copyright: © 2025 Zhang, Long, Wu, Tan, Qu and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Rui Zhang, Department of Critical Care Medicine, Ruijin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.