## 4 Discussion  
This study investigated the predictive value of individual NEWS parameters for short- and medium-term mortality in ED patients, using decision tree analysis as a machine learning technique. Our findings reveal that the standard NEWS may not optimally reflect mortality risk in the ED context. The decision trees generated in this study identified alternative stratifications of vital signs, often diverging from the original NEWS scoring system. Among all parameters, respiratory indicators consistently emerged as the strongest predictors. For 48 h mortality, the key factor was the need for \(\text{FiO}_{2}\), followed by \(\text{SpO}_{2}\). For 7- and 30-day mortality, \(\text{SpO}_{2}\) was the primary split variable, particularly when falling below 94%. The prognostic relevance of respiratory parameters is further supported by a recent study that explored the role of the ROX index during the initial assessment of patients presenting to the ED [29]. The study showed that this simple index, defined as the ratio of peripheral oxygen saturation (\(\text{SpO}_{2}\)) to the fraction of inspired oxygen (\(\text{FiO}_{2}\)), divided by the respiratory rate (RR), is a valuable and straightforward predictor of hospitalization and 30-day mortality for ED patients [29]. Despite this evidence being confirmed in the literature, to date, no studies have demonstrated it using the powerful techniques of machine learning. A detailed assessment of the decision tree branches of this study emphasized the relevance of consciousness status (AVPU scale) in identifying high-risk patients across all outcomes. This suggests the need to reconsider the equal weighting of all parameters in the original NEWS for ED patients. The findings showed that for high-risk patients, fewer than three vital parameters--oxygen saturation, need for supplemental oxygen, and consciousness level--were consistently identified as key indicators. This aligns with the hypothesis proposed by other researchers, suggesting that in urgent conditions, fewer but more critical parameters may be sufficient for rapid risk stratification [30, 31, 32]. Conversely, the decision trees revealed that for low-risk patients, a comprehensive assessment of all vital signs is necessary to gain a complete understanding of their clinical status. Furthermore, when comparing the ranges provided by the NEWS scale with those generated by the decision tree, we observed that the cut-offs rarely align with the original NEWS scoring system [6]. For instance, when looking at the AVPU scale, the classification diverged from the original NEWS, which assigns zero points for \"Alert\" and three points for any alteration (\"V,\" \"P,\" or \"U\") [6]. Instead, the decision trees in this study demonstrated a more refined risk stratification based on specific outcomes, with varying levels of risk depending on the consciousness status category. Similarly, when examining peripheral oxygen saturation for the 48 h outcome, the decision tree identified three distinct ranges, <94%, 94-96%, and >96%, which are different from the four categories reported in the NEWS scale (\(\leq\)91%, 92-93%, 94-95%, and >96%) [6]. Likewise, for respiratory rate, the decision tree analysis revealed different cut-offs, with a minimum and maximum risk range between 15 and 20 breaths per minute, as opposed to the NEWS scale, which defines a range from <8 to >25 breaths per minute. Another noteworthy result emerged when assessing SBP. While the NEWS stratifies SBP into five levels, the decision tree for 48 h mortality risk employed a binary categorization: below/equal or above 110 mmHg [6]. For the other two outcomes, the categorization range varied between 110 and 125 mmHg, unlike the NEWS scale, which includes values up to 220 mmHg. An important factor to consider in this study is the use of mortality as an outcome, which may not be the most suitable choice due to the limited incidence of short- and medium-term deaths in the ED context.