Machine learning for predicting mortality in patients with sepsis and its associated complications: A systematic review and meta-analysis
Hanxi Lv, Yan Gao, Aiping Yu
Abstract
Sepsis remains a leading cause of death in intensive care units worldwide and may be accompanied by complex complications, including encephalopathy, coagulopathy, acute kidney injury, myocardial dysfunction, and delirium, each of which substantially increases the risk of poor outcomes.
Introduction
As revised by the Sepsis-3.0 consensus, sepsis is defined as life-threatening organ dysfunction caused by a dysregulated host response to infection [1]. To date, it remains a leading cause of death among patients in intensive care units (ICUs).
Materials and methods
The protocol for this study was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (Supplementary Table S1 in S1 File) [17].
Results
A total of 9,550 records were identified in the initial search. After screening titles and abstracts, 1,050 records were selected for further assessment, of which 909 were excluded. The full texts of the remaining 141 records were sought, and 6 were excluded because the full text could not be obtained.
Discussion
The overall performance of machine learning models for predicting short-term mortality in patients with sepsis was moderate, with a pooled AUC of 0.82 in the training set (46 studies, 15 model types) and 0.79 in the validation set (50 studies, 25 model types), representing a decrease of approximately 3.7% from the training set to the validation set, suggesting a certain degree of generalizability.
Conclusion
This meta-analysis shows that machine learning models have moderate discriminative ability for predicting short-term mortality in patients with sepsis. LR, the most extensively evaluated model, shows stable performance.
Citation: Lv H, Gao Y, Yu A (2026) Machine learning for predicting mortality in patients with sepsis and its associated complications: A systematic review and meta-analysis. PLoS One 21(11): e0362057. https://doi.org/10.1371/journal.pone.0362057
Editor: Benjamin M. Liu, Children’s National Hospital, George Washington University, UNITED STATES OF AMERICA
Received: August 25, 2026; Accepted: October 20, 2026; Published: November 2, 2026
Copyright: © 2026 Lv et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All basic information and statistical analysis data generated or analyzed during this study are included in Supplementary Table S16.
Funding: The author(s) received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
