Mikhail Yadgarov ¹ Giovanni Landoni ^2,3 Levan Berikashvili ¹ Petr Polyakov ¹ Kristina Kadantseva ¹ Anastasia Smirnova ¹ Ivan V. Kuznetsov ¹ Maria M. Shemetova ¹ Alexey A. Yakovlev ¹ Valery V. Likhvantsev ^1,4*

• ¹ Federal Scientific Clinical Center of Reanimatology and Rehabilitation (FSC RR), Moscow, Moscow Oblast, Russia
• ² San Raffaele Scientific Institute (IRCCS), Milan, Lombardy, Italy
• ³ Vita-Salute San Raffaele University, Milan, Lombardy, Italy
• ⁴ I.M. Sechenov First Moscow State Medical University, Moscow, Moscow Oblast, Russia

Background: With machine learning (ML) carving a niche in diverse medical disciplines, its role in sepsis prediction, a condition where the 'golden hour' is critical, is of paramount interest. This study assesses the factors influencing the efficacy of ML models in sepsis prediction, aiming to optimize their use in clinical practice.We searched Medline, PubMed, Google Scholar, and CENTRAL for studies published from inception to October 2023. We focused on studies predicting sepsis in real-time settings in adult patients in any hospital settings without language limits. The primary outcome was area under the curve (AUC) of the receiver operating characteristic. This meta-analysis was conducted according to PRISMA-NMA guidelines and Cochrane Handbook recommendations. A Network Meta-Analysis using the CINeMA approach compared ML models against traditional scoring systems, with metaregression identifying factors affecting model quality.Results: From 3953 studies, 73 articles encompassing 457,932 septic patients and 256 models were analyzed. The pooled AUC for ML models was 0.825 and it significantly outperformed traditional scoring systems. Neural Network and Decision Tree models demonstrated the highest AUC metrics. Significant factors influencing AUC included ML model type, dataset type, and prediction window.This study establishes the superiority of ML models, especially Neural Network and Decision Tree types, in sepsis prediction. It highlights the importance of model type and dataset characteristics for prediction accuracy, emphasizing the necessity for standardized reporting and validation in ML healthcare applications. These findings call for broader clinical implementation to evaluate the effectiveness of these models in diverse patient groups.