Early Warning Score Trend Analysis: A Data-Driven Approach for Emergency Medical Services

Tamara Krafft, Fabian Stieler, Bernhard Bauer

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Keywords: Emergency Medical Services, Prehospital Care, Risk Stratification, Early Warning Scores, Clinical Decision Support, Machine Learning

TL;DR: Short‐term trends in preintervention early warning scores during emergency medical services encounters encode measurable patterns that moderately predict return of spontaneous circulation in out-of-hospital cardiac arrests.

Abstract:

Early recognition of clinical deterioration is crucial for timely intervention, especially during Emergency Medical Services (EMS) encounters.
Early Warning Scores (EWS) translate raw vital signs into a clinically transparent risk scale. However, research on prehospital EWS applications is limited and often focuses on in-hospital outcomes and single snapshots, neglecting short-term risk trajectories. This paper explores whether EWS trends, captured just before initial EMS intervention, convey additional information and can predict the return of spontaneous circulation (ROSC) during out-of-hospital cardiac arrest encounters.
In a retrospective study of 4,394 cardiac arrest encounters from the 2021–2023 National EMS Information System, we recalculated eight different EWS models at every documented vital sign measurement and derived time-normalized preintervention features, including slope, mean, area under the EWS curve (AUC), and exponentially weighted average (EWA). Informational value was quantified with nonparametric tests and L1-regularized logistic regression models targeting prehospital ROSC.
Our findings demonstrate that short-term EWS dynamics encode measurable patterns of clinical deterioration, achieving moderate predictive performance (AUROC: 0.665) and advancing the current understanding of prehospital risk assessment. These results highlight the potential of incorporating vital sign trajectories into real-time, data-driven decision-support tools for EMS and motivate further exploration with more flexible, AI-based modeling approaches.

Track: 4. Clinical Informatics

Registration Id: 86N2NT79SWJ

Submission Number: 22