Diagnostic performance of machine-learning algorithms for sepsis prediction: An updated meta-analysis.

Background: Early identification of sepsis has been shown to significantly improve patient prognosis.

Objective: Therefore, the aim of this meta-analysis is to systematically evaluate the diagnostic efficacy of machine-learning algorithms for sepsis prediction.

Methods: Systematic searches were conducted in PubMed, Embase and Cochrane databases, covering literature up to December 2023. The keywords included machine learning, sepsis and prediction. After screening, data were extracted and analysed from studies meeting the inclusion criteria. Key evaluation metrics included sensitivity, specificity and the area under the curve (AUC) for diagnostic accuracy.

Results: The meta-analysis included a total of 21 studies with a data sample size of 4,158,941. Overall, the pooled sensitivity was 0.82 (95% confidence interval [CI] = 0.70-0.90; P< 0.001; I2= 99.7%), the specificity was 0.91 (95% CI = 0.86-0.94; P< 0.001; I2= 99.9%), and the AUC was 0.94 (95% CI = 0.91-0.96). The subgroup analysis revealed that in the emergency department setting (6 studies), the pooled sensitivity was 0.79 (95% CI = 0.68-0.87; P< 0.001; I2= 99.6%), the specificity was 0.94 (95% CI 0.90-0.97; P< 0.001; I2= 99.9%), and the AUC was 0.94 (95% CI = 0.92-0.96). In the Intensive Care Unit setting (11 studies), the sensitivity was 0.91 (95% CI = 0.75-0.97; P< 0.001; I2= 98.3%), the specificity was 0.85 (95% CI = 0.75-0.92; P< 0.001; I2= 99.9%), and the AUC was 0.93 (95% CI = 0.91-0.95). Due to the limited number of studies in the in-hospital and mixed settings (n< 3), no pooled analysis was performed.

Conclusion: Machine-learning algorithms have demonstrated excellent diagnostic accuracy in predicting the occurrence of sepsis, showing potential for clinical application.