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Comparative evaluation of the Manchester Triage System and emergency severity index in predicting critical events in the emergency department

Revista

BMC Emergency Medicine

Fecha de publicación

25 de noviembre de 2025

BMC Emerg Med. 2025 Nov 24. doi: 10.1186/s12873-025-01420-8. Online ahead of print.

BACKGROUND: Given the increasing number of patients presenting to Emergency Departments (EDs), the use of effective and reliable triage tools is essential. Such systems enable rapid assessment of the urgency of medical intervention, which contributes to improved workflow, optimized resource allocation, and potentially better clinical outcomes. This study presents a direct comparison of two of the most widely used triage systems globally: the Manchester Triage System (MTS) and the Emergency Severity Index (ESI), evaluating their classification agreement and predictive value for critical events in the ED population.

METHODS: This retrospective study included 1,072 patients who were concurrently assessed using both systems during a transitional six-month period in which both MTS and ESI were applied in parallel at the study hospital. The correlation between triage categories assigned by each system was analyzed, as well as their association with predefined critical events.

RESULTS: A moderate level of classification agreement was observed between the two systems (Cohen’s kappa = 0.51; Spearman’s rho = 0.49). ESI assigned over 80% of patients to priority level 3, whereas MTS distributed patients more evenly between levels 3 and 4. Both systems demonstrated a statistically significant association between higher acuity levels and an increased risk of critical events-lower category numbers (i.e., higher priority) corresponded with a greater likelihood of severe complications.

CONCLUSIONS: The results confirm the effectiveness of both triage systems in assessing patients’ clinical condition while highlighting important differences in their classification structures. These findings may inform the choice of triage system in clinical practice and underscore the need for further research on optimization and potential integration with artificial intelligence-based decision support tools.

PubMed:41286624 | DOI:10.1186/s12873-025-01420-8

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El idioma original es este artículo es el inglés. Mediante el sistema de traducción automático de la IA de emergencing, el contenido se ha traducido al español. Esta es una traducción no supervisada por lo que puede que alguna parte del contenido no refleje con exactitud la publicación original del autor/autores.