Advances in Data-Driven Early Warning Systems for Sepsis Recognition and Intervention in Emergency Care: A Systematic Review of Diagnostic Performance and Clinical Outcomes

Abstract

Competing Interests: Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare the following: Payment/services info: All authors have declared that no financial support was received from any organization for the submitted work. Financial relationships: All authors have declared that they have no financial relationships at present or within the previous three years with any organizations that might have an interest in the submitted work. Other relationships: All authors have declared that there are no other relationships or activities that could appear to have influenced the submitted work.; Sepsis is a life-threatening condition, and early recognition in the emergency department (ED) is crucial for reducing mortality. However, traditional ED sepsis screening tools (e.g., Systemic Inflammatory Response Syndrome (SIRS) and quick Sequential Organ Failure Assessment (qSOFA)) often lack sensitivity for early detection. Machine learning (ML)-based early warning systems have been proposed to address this gap by analyzing complex clinical data in real time. To address this, we systematically reviewed studies (2015-2025) assessing ML-based sepsis warning systems in adult EDs. Databases searched included PubMed, Embase, Scopus, and Web of Science. Included studies reported diagnostic performance and/or clinical outcomes. Overall, a total of 11 studies (primarily retrospective, with a few prospective) were included. ML models using vital signs, laboratory results, and electronic health record (EHR) data demonstrated high discrimination for sepsis, with area under the receiver operating characteristic curve (AUROC) values often exceeding 0.80, and outperformed traditional scoring tools. Many provided earlier warnings, often two to four hours before sepsis was clinically recognized. Implementation studies showed that ML-based alerts expedited treatment; one multi-center system reduced time to antibiotics by ~1.8 hours when alerts were promptly addressed. Some reports noted reduced organ failure and mortality. However, evidence of improved patient outcomes remains inconsistent, likely due to study heterogeneity and limited prospective validation. ML-based early warning systems show strong potential for improving sepsis recognition and treatment in EDs. Further multi-center trials are needed to confirm their impact on outcomes and guide safe implementation. (Copyright © 2025, Al-Juhani et al.)