Machine learning for predicting mortality in patients with sepsis and its associated complications: A systematic review and meta-analysis.

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. Machine learning, a promising approach for prognostic prediction, can capture complex nonlinear relationships among clinical risk factors. However, its predictive performance in sepsis and its specific complications remains unclear.

We systematically searched PubMed, Embase, Web of Science, and the Cochrane Library for relevant literature through April 2026. Studies that aimed to develop or validate machine learning models to predict mortality in adult patients with sepsis and its associated complications were included. The random-effects or bivariate model was used to calculate the AUC, sensitivity, and specificity.

Overall, machine learning models show moderate predictive performance for short-term mortality in sepsis, with a combined AUC of 0.82 for the training set (46 studies, 15 models) and 0.79 for the validation set (50 studies, 25 models). Logistic regression is the most widely evaluated model, demonstrating stable performance (validation AUC = 0.81). Integrated methods, including random forest and XGBoost, performed equally well, with AUCs between 0.81 and 0.83 in both groups. Although LightGBM and DL-CMT achieved the highest AUCs in the training and validation sets, respectively, both estimates were based on a single study. Among the complications, the evidence for sepsis-associated encephalopathy (AUC = 0.81, 3 studies) and sepsis-induced coagulopathy (AUC = 0.80, 4 studies) is the most conclusive and best demonstrated. Sepsis-associated acute kidney injury showed moderate and stable performance (validation AUC = 0.79, 4 studies). Sepsis-induced cardiomyopathy showed significant attenuation from training to validation (AUC 0.84 vs 0.78, all from a single study), and random forest performed well in training (AUC = 0.98) but was not widely reported. Septic shock reached the highest AUC (0.95), but this was based on a single study. The predictive utility of sepsis-associated delirium is limited (AUC = 0.67, 1 study), and sepsis-induced myocardial injury is moderate (AUC = 0.77, 1 study).For long-term mortality, evidence is extremely lacking. Only SAE (AUC 0.88 during training and 0.86 during validation) and SCRS (combined AUC 0.76) provided single study estimates, ruling out reliable conclusions.

In this meta-analysis, machine learning models showed moderate predictive performance for short-term mortality in sepsis. Among complications, SAE and SIC had the strongest evidence and best performance, whereas SAD showed limited utility. Evidence for long-term mortality was extremely scarce. These findings support the potential role of machine learning in early prognostic risk stratification, yet they also highlight the urgent need for rigorous external validation and higher-quality studies, particularly for under-investigated complications.