Machine learning for predicting mortality in adult critically ill patients with Sepsis: A systematic review

Abstract

Introduction: Various Machine Learning (ML) models have been used to predict sepsis-associated mortality. We conducted a systematic review to evaluate the methodologies employed in studies to predict mortality among patients with sepsis.

Methods: Following a pre-established protocol registered at the International Prospective Register of Systematic Reviews, we performed a comprehensive search of databases from inception to February 2024. We included peer-reviewed articles reporting predicting mortality in critically ill adult patients with sepsis.

Results: Among the 1822 articles, 31 were included, involving 1,477,200 adult patients with sepsis. Nineteen studies had a high risk of bias. Among the diverse ML models, Logistic regression and eXtreme Gradient Boosting were the most frequently used, in 22 and 16 studies, respectively. Nine studies performed internal and external validation. Compared with conventional scoring systems such as SOFA, the ML models showed slightly higher performance in predicting mortality (AUROC ranges: 0.62–0.90 vs. 0.47–0.86).

Conclusions: ML models demonstrate a modest improvement in predicting sepsis-associated mortality. The certainty of these findings remains low due to the high risk of bias and significant heterogeneity. Studies should include comprehensive methodological details on calibration and hyperparameter selection, adopt a standardized definition of sepsis, and conduct multicenter prospective designs along with external validations.