Association between initial patient acuity and the predictive performance of the MREMS: A nationwide retrospective cohort study.
The American journal of emergency medicine · 2025-07-12 · Observational study
Abstract
INTRODUCTION: The ability to accurately predict short-term mortality in the prehospital setting is critical for optimizing emergency medical service (EMS) care. This study aimed to evaluate the predictive capability of the modified Rapid Emergency Medical Score (mREMS) across varying initial patient acuity levels and its implications for clinical decision-making. METHODS: This multicenter observational study analyzed data from the National Emergency Medical Services Information System (NEMSIS) in the United States. All consecutive EMS activations of adult patients (>18 years) classified into three levels of initial patient acuity (Red, Yellow, Green) were included. The collected variables included epidemiological data, vital signs, and outcomes. The primary outcome assessed was short-term mortality. RESULTS: A total of 2,419,687 EMS activations were analyzed, with 1,590,578 patients (65.7 %) with lower acuity (green), 608,117 (25.2 %) with emergent (yellow), and 220,983 (9.1 %) with critical (red) acuity level. The mortality rate was significantly greater in Red patients (4.17 %) than in Yellow (0.49 %) and Green (0.21 %) patients (p < 0.001). The predictive capability for short-term mortality presented an AUC of 0.855 (95 %CI: 0.838-0.872), 0.762 (95 %CI: 0.740-0.784), and 0.720 (95 %CI: 0.700-0.740), for Red, Yellow, and green, respectively. CONCLUSIONS: This study demonstrated that mREMS is an effective tool for predicting short-term mortality in the prehospital setting, particularly for patients with high initial patient acuity. Its application can facilitate the prioritization of interventions and enhance clinical decision-making, ultimately contributing to improved patient outcomes across all levels of initial patient acuity.
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Tags
Decision support tools · Early warning scores · Mortality & survival
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