## 4 Discussion
Previous studies indicate a very low mortality rate,which could lead predictive models to perform better than expected precisely because of the rarity of the outcome [33]. For instance, [PERSON] et al. evaluated the use of the EWS in a prehospital setting, which is generally associated with more severe events than the ED, and reported a 3-day mortality rate of 2.8% (6170/219, 323) and a 7-day mortality rate of 4.3% (9344/219, 323) [34]. In contrast, in the ED setting, a recent Italian retrospective study reported a 30-day mortality rate of 1.0% (1025/98, 443), considering all patients regardless of age [35]. Remarkably, selecting such a rare outcome may have limited the performance of basic statistical techniques, suggesting that alternative outcomes for the evaluation of NEWS might be more appropriate. Nonetheless, the present study aimed to use the most widely studied and utilized outcome in relation to the NEWS while employing more advanced and accurate statistical methods [8]. Our study suggests that revising cut-off points based on models derived from machine learning techniques could better reflect the ED population and improve risk stratification. Such an approach may allow for more tailored and accurate predictions, providing more timely intervention and consequently improving patient outcomes. This is particularly relevant given that the ED population differs significantly from the inpatient population for which the NEWS was originally validated [36]. These findings were not intended to support the implementation of decision tree tools in ED triage systems, but rather to highlight that the current structure and cut-offs used in the NEWS may not optimally reflect the risk stratification needs of emergency settings. Our results suggest the need for rethinking how these parameters are used, possibly through new approaches that prioritize interpretability and clinical relevance. In the ED context, patients often exhibit altered vital signs due to non-life-threatening conditions, such as anxiety or metabolic disorders, which may wrongly trigger high NEWSs. Conversely, certain life-threatening conditions, like sepsis, trauma or chest pain, may not immediately present with significant changes in vital signs, leading to underestimation of risk [37; 38; 39]. Additionally, deaths for ED patients with near-normal vital signs are more likely to result from an event or process that is yet to happen and will evolve rapidly [40]. This contrasts with inpatient settings, where changes in vital signs observed during clinical assessments are more likely the result of a pathophysiological process that has already started and evolves in the wards [19]. Research on various ED presentations, such as transient loss of consciousness or fever, has demonstrated that there may be no significant differences in vital sign ranges between patients with diverse outcomes when assessed using the NEWS categories [41; 42]. This suggests that despite the presence of underlying clinical conditions, vital signs may not exhibit significant alterations or differences between high-risk and low-risk ED clinical presentations. Therefore, the original NEWS may not accurately detect changes in vital signs specific to the ED population, underscoring the need for adaptations tailored to this dynamic and complex setting. These adaptations will primarily serve to promptly identify patients needing immediate intervention to prevent clinical deterioration while safely distinguishing them from those who can safely wait. This adaptation aligns with the unique dynamics of the ED, where continuous monitoring of all patients is not feasible and will adhere to the original purpose of the NEWS: to prevent adverse events by responding to their triggers with the appropriate interventions [18].