Comparative Statistical Performance of Prehospital Scoring Systems With and Without Age Adjustments for Predicting Multiple Trauma Outcomes: An Observational Study

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We assess the predictive performance of six prehospital trauma scoring systems across multiple outcomes, and explore the inclusion of simple age categorisations on predictive validity. Methods: This retrospective observational study used nationwide, linked prehospital data from the National Ambulance Service (NAS) and in-hospital data from the Irish Major Trauma Audit (MTA) where the inclusion criteria are based on Injury Severity Score (ISS), length of stay and injury details. 101,114 adult trauma patients were included. Six trauma scoring systems were applied to the data and predictive performance was compared for three outcomes: major trauma (ISS >15), MTA inclusion, and in-hospital mortality. For tools not originally including age, prior validated age categories were added and predictive performance re-assessed. Results: All trauma scores performed suboptimally in predicting ISS>15 (C-statistics 0.61-0.71). For MTA inclusion, no model performed acceptably (≤0.64). Mortality prediction was acceptable/good (0.71-0.87). Including simple age categorisations into existing tools reduced each tool’s predictive accuracy for ISS>15, yielded modest improvements for MTA inclusion, and improved mortality prediction. Conclusions: Trauma scoring systems were most effective at predicting mortality, especially when age was included. However, they were less effective for identifying major trauma (ISS >15) or MTA inclusion, even with age adjustments. The New Trauma Score, which includes oxygen saturation, showed the best overall performance for major trauma, supporting broader use in prehospital triage. Trauma Prehospital Care Scoring Systems Mortality Triage Global Health Figures Figure 1 Introduction Traumatic injuries are among the leading causes of mortality worldwide, accounting for an estimated 4.4 million deaths annually [ 1 ]. To facilitate timely identification of severe injury, trauma networks rely on triage tools and scoring systems to inform care level and transport decisions. Trauma triage tools and scoring systems are quantitative models that estimate injury severity based on physiological, anatomical, or composite parameters. These tools are critical in the prehospital setting, where rapid, evidence-based decisions are required to allocate finite emergency resources effectively. A recent systematic review by Donnelly et al. [ 2 ] identified over 50 trauma triage tools with heterogeneous scoring systems, validated in adult populations. They found that while a broad range of physiological and injury characteristics are used in these tools, there was little consensus on the optimal set of predictive variables or how these were operationalised. Among these tools, systolic blood pressure and the Glasgow Coma Scale (GCS) are the most commonly used parameters. GCS remains a cornerstone in trauma assessment due to its well-established prognostic value in both isolated traumatic brain injury (TBI) and polytrauma [ 3 , 4 ]. However, when used as a standalone measure, GCS is more effective at predicting outcomes in patients with TBI than in those with major trauma without neurological involvement [ 5 ]. In Western Europe, including in Ireland, trauma incidents are often associated with low-energy mechanisms, with falls contributing to nearly half of all injury cases and continuing to be the leading cause of trauma [ 6 ]. Notably, data from the national Major Trauma Audit (MTA), which uses inclusion criteria based on Injury Severity Score (ISS), length of stay and injury characteristics (see Appendix A), show that MTA patients are almost evenly divided between those under 65 and those aged 65 and older [ 7 , 8 ]. There is a tendency to under-triage older adult patients with major trauma, as illustrated by the lower rates of pre-alerted systems and lower numbers received by trauma teams [ 9 ]. Current guidance from the Health Service Executive (HSE) has flagged inconsistencies in how older trauma patients are pre-alerted or escalated to trauma teams [ 10 ]. The challenge of accurately assessing trauma severity in the prehospital setting is compounded in older adults, who in Ireland, have longer hospital stays, and experience significantly worse outcomes [ 9 ]. A growing body of evidence shows that older adults experience disproportionately worse outcomes following trauma, even when injuries are anatomically similar to those sustained by younger individuals [ 7 , 11 ]. This is largely attributed to factors such as frailty [ 12 ], reduced physiological reserves, co-existing medical conditions, and the use of multiple prescribed medications [ 13 ]. Age has consistently been identified as an independent predictor of mortality, prolonged hospitalisation, and poor functional recovery, even after adjustment for ISS and other injury characteristics [ 3 , 7 , 11 ]. The 2022 Major Trauma Audit (MTA) in Ireland [ 14 ] draws attention to this growing cohort of older individuals whose injury burden and care needs are not adequately captured by existing triage systems. Many widely used trauma scoring systems do not incorporate age, despite robust evidence supporting its prognostic significance [ 3 , 7 , 11 ]. These findings highlight the urgent need to assess age-related vulnerabilities in prehospital trauma assessment tools [ 2 , 8 , 15 ]. Existing research has evaluated the predictive accuracy of trauma triage tools primarily for mortality among patients with high-energy trauma [ 16 , 17 ], and those with severe injuries [ 18 – 21 ]. Fewer studies have examined the performance in broader, more representative prehospital populations [ 22 ], or considered a subsequent classification of ‘major trauma’ as an outcome in addition to mortality. The ISS is often used as the reference for injury severity, defining major trauma as patients scoring > 15 [ 23 ]. The ISS is derived from comprehensive retrospective anatomical assessments that typically require imaging, surgical exploration, or complete diagnostic workups. As such, it cannot be applied in the prehospital environment where decisions must be made rapidly and with limited diagnostic information [ 24 , 25 ]. Furthermore, not all countries use ISS > 15 as the sole threshold for major trauma. In Ireland, ISS > 15 is just one of the inclusion criteria for the Major Trauma Audit (MTA) [ 26 ], which was established in 2016. The criteria also include patients with a hospital stay of more than 3 days, those admitted to critical care, patients transferred for specialist care and those who died in the emergency department. A more detailed list of criteria can be found in Appendix A and in [ 14 ]. In this study, we focused on six trauma scoring systems that utilise prehospital physiological variables, while exploring their predictive power for three clinically significant, binary, outcomes: major trauma (ISS > 15), inclusion in the Major Trauma Audit (MTA) and in-hospital mortality. Additionally, we explored the predictive power of adding age into scoring systems which do not include age as a predictor in order to give epidemiological insight into the best prehospital trauma scoring system for Ireland. Methodology Study Design and Setting We performed a retrospective observational study using nationwide, linked, data from the National Ambulance Service (NAS) and the Irish Major Trauma Audit (MTA) covering the period from January 1, 2020 to December 31, 2022. The NAS is the statutory provider of prehospital emergency and intermediate care for Ireland. In parallel, Dublin Fire Brigade delivers emergency medical services to residents in much of the Dublin metropolitan area, but their data was not included due to the use of paper-based records. The NAS receives over 350,000 ambulance calls annually and employs more than 2,000 personnel across 102 locations. Emergency calls are initiated via the 999/112 emergency call system and triaged using the Medical Priority Dispatch System™ (MPDS®), supported by a Computer Aided Dispatch (CAD) system. Attending first responders document patient demographics and physiological parameters in real time via electronic Patient Care Reports (ePCR). Currently in Ireland, trauma patients often present to acute hospitals regardless of its trauma expertise, resulting in frequent inter-hospital transfers and delayed treatment. The trauma Steering Report [ 10 ] details a shift toward a hub-and-spoke regional network, with Major Trauma Centres based in Dublin and Cork, supported by multiple Trauma Units, including a Trauma Unit with specialist services in Galway. This system is designed to provide a full spectrum of trauma care. The recommended Health Services Executive (HSE) Trauma Triage Tool (TTT) [ 27 ] aims to enable first responders to triage patients based on physiology and injury criteria, allowing them to bypass Trauma Units if major trauma is suspected, and the patients can be transported to a major trauma centre within 45 minutes. Participants Trauma cases were identified in the NAS dataset based on a working diagnosis that includes “trauma” recorded by paramedics on scene. We included adult patients aged 16 and over who were transported to hospital. Additionally, patients with incomplete physiological data were excluded, as well as those who were dead on arrival or where resuscitation was ceased. To ensure data accuracy, we further excluded patients with abnormal physiological values, which were deemed to be recording errors, with the acceptable ranges shown in Table C1 . Figure 1 outlines the data cleaning process and final cohort selection. Identifying Trauma Scoring Systems Trauma scoring systems were selected based on their reliance on routinely collected, prehospital physiological data within the NAS dataset. From the systematic review by Donnelly et al. [ 2 ], we identified the Revised Trauma Score (RTS) [ 28 ], Glasgow Coma Scale, Age and Systolic Blood Pressure score (GAP) [ 29 ] and the modified rapid emergency medicine score (mRES) [ 30 ]. Additionally, we included the New Trauma Score (NTS) [ 31 ] and a recent score: the Revised Glasgow Coma Scale, Age and Systolic Blood Pressure score (R-GAP) [ 32 ]. These were compared to the current HSE Trauma Triage Tool (TTT) [ 27 ] based solely on its physiological parameters. The RTS, NTS and TTT do not include age whereas the GAP, R-GAP and mRES include age. Details of how to calculate each trauma scoring system can be found in Appendix B. Variables and Measures Key variables required to compute trauma scoring systems included Glasgow Coma Scale (GCS), systolic blood pressure (SBP), respiratory rate (RR), heart rate (HR), peripheral oxygen saturation (SpO₂), and age. For scoring systems such as the NTS, GAP, and R-GAP, GCS was used as a raw score with the standard clinical range of 3–15. Each trauma score was calculated using the corresponding parameters and coding systems as per their original formulations. A higher score generally indicated a better prognosis, except for the Modified Rapid Emergency Medicine Score (mRES) and the NAS Trauma Triage Tool (TTT), where higher scores represent poorer prognosis; therefore, to allow direct comparison, the scores of these tools were reverse coded so that higher scores uniformly indicated better prognosis. Outcomes: We evaluated three trauma-related outcomes: Major trauma , defined as an Injury Severity Score (ISS) > 15, an internationally recognised threshold [ 25 ]. MTA inclusion , defined retrospectively by criteria from the National Office of Clinical Audit (NOCA); full inclusion criteria are detailed in Appendix C. Mortality , recorded in-hospital. Statistical Methods and Analysis Descriptive statistics were calculated for all study variables. Continuous variables were summarised as means and standard deviations (SD) and compared using two-sample t-tests. Categorical variables were summarised as counts and percentages; between-group differences were evaluated using the chi-square (χ²) test. We employed Firth's penalised likelihood binary logistic to estimate odds ratios (ORs) and corresponding 95% confidence intervals (CIs) for each trauma scoring system across the three outcomes. This approach was chosen over conventional binary logistic regression due to the rarity of the outcomes, ISS > 15 (1.4%), MTA inclusion (4.1%) and mortality (0.3%). Standard logistic regression can produce biased estimates under such conditions, often producing inflated ORs, overly narrow CIs, or convergence issues due to data separation [ 33 ]. In contrast, Firth regression mitigates small-sample bias and provides more stable, reliable estimates in the presence of sparse event data. Since each trauma scoring system uses different scoring ranges, to facilitate comparisons across tools, the trauma scores were standardised by converting them into z-scores (with mean = 0, SD = 1). For each trauma tool, a score of 0 means exactly average, a score of 1 means the person’s trauma score is 1 standard deviation above the mean, and a negative trauma score means a below average score. The OR from the models represents the change in odds of the outcome per one standard deviation increase in the standardised score. Discriminative performance was assessed using the concordance (C-) statistic. Values 0.9 excellent [ 34 ]. We then assessed the prognostic value of age, by incorporating age into scoring systems that do not include it, using two classification schemes: as per the GAP model [ 29 ], which assigns + 3 points for patients under 60 and 0 for those 60 or older; and the R-GAP model [ 32 ] which assigns + 3 for patients under 50, 0 for ages 50–70, and − 3 for those over 70. These age scores were included alongside the original scores to determine whether predictive accuracy improved with the addition of age. All analyses were performed using Stata version 18.5 (StataCorp, College Station, TX, USA). Bias We limited inclusion to patients with complete physiological data to ensure comparability across scoring systems, performing no imputation. Patients without complete prehospital records, particularly those deceased before physiological data could be collected, were excluded. This potentially introduces survivorship bias. To further protect data quality, patients with implausible physiology were excluded as shown in Appendix C (Table C1 ). Results Participants Of the 150,480 patients in the NAS dataset with a preliminary diagnosis of trauma, 127,737 were adult trauma patients transported to a hospital in Ireland between 2020 and 2022. After excluding cases with incomplete physiological data, which is required for the trauma scoring systems’ calculation, the final cohort included 101,114 patients. The data cleaning process is outlined in Fig. 1 . Table 1 Patient characteristics for each outcome with the t-statistic and p-value stated to determine significance between each outcome. Variable Total ISS > 15 ISS ≤ 15 t-statistic (p-value) MTA Patient Non-MTA patients t-statistic (p-value) Deceased Patients Surviving Patients t-statistic (p-value) Total 101,114 1,422 (1.4%) 99,692 (98.6%) 4,190 (4.1%) 96,924 (95.6%) 284 (0.3%) 101,830 (99.7%) Age 59.7 (23.2) 62.8 (21.2) 59.6 (23.2) 5.1 (< .001) 65.4 (20.0) 59.4 (23.3) 16.5 (< .001) 76.5 (17.3) 59.6 (23.2) 12.3 (< .001) SpO2 96.2 (5.1) 94.2 (7.9) 96.3 (5.1) 14.7 (< .001) 94.9 (6.7) 96.3 (5.0) 17.7 (< .001) 91.7 (11.5) 96.2 (5.1) 15.0 (< .001) GCS 14.8 (1.0) 13.3 (3.3) 14.8 (0.9) 60.0 (< .001) 14.2 (2.2) 14.8 (0.9) 36.7 (< .001) 11.4 (4.6) 14.8 (0.9) 58.8 (< .001) Systolic Blood Pressure 142.1 (25.1) 139.8 (30.6) 142.1 (25.0) 3.4 (< .001) 141.4 (28.7) 142.1 (24.9) 1.7 (0.09) 146.0 (38.9) 142.1 (25.0) 2.7 (.008) Heart Rate 84.1 (18.1) 85.1 (22.2) 84.1 (18.0) 2.1 (0.036) 84.8 (20.2) 84.1 (18.0) 2.62 (0.009) 85.2 (26.4) 84.1 (18.1) 1.0 (0.3) Respiration Rate 18.1 (4.4) 19.3 (6.4) 18.0 (4.4) 10.5 (< .001) 19.0 (5.7) 18.0 (4.3) 14.4 (< .001) 18.9 (6.7) 18.0 (4.4) 3.2 (.001) *Sex † 98,062 1,422 † 96,640 ‡ 94.3 (< .001) 4,190 † 93,872 ‡ 13.4 (< .001) 284 † 97,778 ‡ 0.5 (0.5) Male 49,808 (50.8%) 904 (63.6%) 48,904 (50.6%) 2,244 (53.6%) 47,564 (50.7%) 150 (52.8%) 49,658 (50.8%) Female 48,254 (49.2%) 518 (36.4%) 47,736 (49.4%) 1,946 (46.4%) 46,308 (49.3%) 134 (47.2%) 48,120 (49.2%) Mechanism of injury Total 58,337 1,422 56,915 § 4,190 54,147 § 284 58,053 § Fall 41,037 (70.3%) 993 (69.8%) 40,044 (70.4%) 3,203 (76.5%) 37,834 (69.9%) 243 (85.6%) 40,794 (70.3%) Motor Vehicle Incident 8,823 (15.1%) 292 (20.6%) 8,531 (15.0%) 688 (16.4%) 8,135 (15.0%) 22 (7.7%) 8,801 (15.2%) Other 8,477 (14.5%) 137 (9.6%) 8,340 (14.6%) 299 (7.1%) 8,178 (15.1%) 19 (6.7%) 8,458 (14.5%) † In total 3,057 patients are missing a sex variable. ‡ A χ² test was performed and quoted. § 100% of target patients have an MOI ‖ MTA inclusion is based on ISS, injury and length of stay criteria (appendix A). Table 2 Firth regression OR with 95% CI and C-statistic for each of the trauma scoring systems and outcomes. ISS > 15 MTA inclusion Deceased C-statistic ISS > 15 C-statistic MTA C-Statistic Deceased RTS equation 1.34 [1.31, 1.36] 1.22 [1.20, 1.24] 1.44 [1.40, 1.47] 0.61 0.54 0.71 T-RTS 1.33 [1.30, 1.36] 1.22 [1.20, 1.24] 1.40 [1.36, 1.44] 0.61 0.54 0.71 NTS equation 1.54 [1.51, 1.58] 1.37 [1.34, 1.39] 1.72 [1.67, 1.77] 0.71 0.62 0.83 T-NTS 1.53 [1.50, 1.56] 1.35 [1.33, 1.37] 1.68 [1.64, 1.74] 0.70 0.61 0.81 GAP 1.79 [1.73, 1.85] 1.49 [1.46, 1.53] 2.34 [2.23, 2.45] 0.67 0.61 0.86 R-GAP 1.92 [1.84, 2.01] 1.54 [1.50, 1.59] 3.68 [3.40, 3.98] 0.65 0.61 0.87 TTT 1.53 [1.50, 1.57] 1.42 [1.40, 1.45] 1.66 [1.60, 1.72] 0.64 0.59 0.72 mRES 1.77 [1.69, 1.85] 1.51 [1.47, 1.55] 3.04 [2.82, 3.28] 0.65 0.61 0.86 *All logistic regression p-values are significant with p < .001. ‖ MTA inclusion is based on ISS, injury and length of stay criteria (appendix A). Table 3 Firth regression ORs with 95% CI and C-statistics (change score from Table 2 ) when incorporating age as a categorical variable into various tools. Including age as GAP Scoring ISS > 15 OR C-statistic ISS > 15 (change) MTA OR C-Statistic MTA (change) Deceased OR C-statistic Deceased (change) T-RTS 1.62 [1.54, 1.71] 0.60 (-0.01) 1.44 [1.39, 1.48] 0.59 (+ 0.05) 4.02 [3.61, 4.48] 0.82 (+ 0.11) T-NTS 1.86 [1.80, 1.92] 0.67 (-0.03) 1.54 [1.50, 1.58] 0.62 (+ 0.01) 2.51 [2.38, 2.63] 0.86 (+ 0.05) TTT 1.53 [1.49, 1.56] 0.60 (-0.04) 1.41 [1.39, 1.44] 0.63 (+ 0.04) 1.69 [1.62, 1.75] 0.81 (+ 0.09) Including age as R-GAP Scoring ISS > 15 OR C-statistic ISS > 15 (change) MTA OR C-Statistic MTA (change) Deceased OR C-statistic Deceased (change) T-RTS 1.40 [1.33, 1.48] 0.59 (-0.02) 1.39 [1.34, 1.43] 0.58 (+ 0.04) 8.50 [6.96, 10.39] 0.83 (+ 0.12) T-NTS 1.97 [1.88, 2.07] 0.65 (-0.05) 1.57 [1.52, 1.62] 0.61 (+ 0) 4.06 [3.74, 4.41] 0.88 (+ 0.07) TTT 1.48 [1.39, 1.56] 0.60 (-0.04) 1.48 [1.42, 1.53] 0.60 (+ 0.01) 6.44 [5.23, 7.92] 0.79 (+ 0.07) *All logistic regression p-values are significant with p < .001. ‖ MTA inclusion is based on ISS, injury and length of stay criteria (appendix A). Baseline Characteristics and Physiology Table 1 presents baseline characteristics and physiological parameters stratified by outcome. Mechanism of injury was recorded in 57.7% of all cases; however, this variable was available for all patients with ISS > 15, those included in the Major Trauma Audit (MTA), and the deceased. Among patients with a known mechanism of injury, the majority (70.3%) sustained injuries from falls. Significant sex differences were observed in the ISS > 15 vs ≤ 15 groups and MTA vs non-MTA groups, with males overrepresented in both. No significant sex differences were observed in the mortality outcome. Statistically significant age differences were observed across all three outcome groups when compared to their respective counterparts: mortality patients, were, on average, 16.9 years older than survivors; those included in the MTA were 6 years older than non-MTA patients; and patients with ISS > 15 were 3.2 years older than non-major trauma patients. Glasgow Coma Scale (GCS) was significantly lower in patients with ISS > 15, MTA inclusion, and in-hospital death (all p < 0.001). Systolic blood pressure (SBP) did not differ significantly between the MTA and non-MTA groups, while heart rate, which is used only in the mRES, showed no significant difference between deceased and surviving patients. Oxygen saturation (SpO₂) was found to be significant across all patient groups, and its statistical significance was second only to GCS across all variables. Comparative Performance of Trauma Scoring Systems Table 2 presents results from logistic regression models using standardised trauma scores as predictors. In the prediction of major trauma (ISS > 15), all scoring systems demonstrated poor discrimination (C-statistic < 0.70) except the NTS and T-NTS, which achieved acceptable performance (C-statistics 0.71 and 0.70, respectively). However, the R-GAP had the largest OR, demonstrating changes in the R-GAP score were more strongly associated with higher odds of major trauma relative to other scoring systems - despite its poor discriminatory ability. All of the trauma scoring systems were poor at predicting MTA inclusion, with C-statistics varying from 0.54 for the RTS and T-RTS to 0.62 for the NTS. For mortality prediction, trauma scores that incorporated age, GAP, R-GAP and mRES, achieved the highest C-statistics and standardised odds ratios (ORs), with R-GAP outperforming all other scoring systems. Among scores without age, the NTS had the best performance. Overall, predictive validity ranged from acceptable to good for mortality, but remained poor for MTA inclusion and poor for ISS > 15 prediction with the exception of the NTS and T-NTS. Incorporating Age into Trauma Scoring Systems We examined the effects of incorporating two age classification schemes into trauma scoring systems that do not originally include it, from the GAP and R-GAP models (age coding is detailed in Appendix B Table A2). Results are presented in Table 3 . The RTS and NTS were not modified to include age, as both are derived from regression-based equations calibrated on specific datasets. Incorporating age would require re-estimating the original model coefficients through a new regression analysis, which was beyond the scope of this study and would compromise comparability with the validated scores. Including age had a negative impact on predictive performance for identifying ISS > 15. All C-Statistics, shown in Table 3 , were in the poor range when age was included using either system. The T-NTS C-statistic also decreased from the acceptable to the poor range. Overall, the tools performed better in predicting ISS > 15 when age was omitted. For MTA inclusion, incorporating age only modestly improved each model’s performance. Notably, the T-RTS models, using the GAP adjustment, showed a 0.05 increase in C-statistic, reaching 0.59. However, none of the age-incorporated models achieved acceptable discrimination. Age adjustment had a marked impact on mortality prediction, with all models showing substantial gains in discriminative performance following the inclusion of age categories. The C-statistic for the T-RTS improved by + 0.12 with the R-GAP age inclusion, moving from the acceptable to the good range. The T-NTS demonstrated the highest C-statistic = 0.88, slightly outperforming the R-GAP trauma scoring system in Table 2 (C-statistic = 0.87), and emerging as the best predictor of in-hospital mortality. Additionally, the age-adjusted T-NTS (OR = 4.06) produced a higher standardised odds ratio for mortality compared to the R-GAP model (OR = 3.68) reflecting a stronger association with mortality and a superior discrimination ability. This highlights the effect size and classification performance of the age adjusted T-NTS. The currently recommended TTT showed notable improvements in mortality prediction with the inclusion of age, reaching the acceptable range for both GAP and R-GAP adjustments. However, age inclusion only modestly improved the TTT’s performance in predicting MTA inclusion and, actually worsened its performance in predicting ISS > 15. Discussion Key Findings and Interpretation In this nationwide retrospective study, six validated prehospital trauma scoring systems were evaluated using Irish trauma data from 2020–2022. For the first time, the prehospital trauma scoring systems were assessed for their ability to simultaneously predict three key outcomes: ISS > 15, MTA inclusion, and mortality. Overall, the results showed that no trauma scoring system predicted ISS > 15 and MTA inclusion satisfactorily, suggesting that the usefulness of these tools for bypassing local hospitals, or predicting major trauma, may be limited. This study contributes to international literature by demonstrating how existing scores perform across population-wide data. All scoring systems performed best when predicting mortality. The addition of age consistently improved the trauma scoring systems’ performance, transforming all scores into good predictors of mortality. The age-adjusted versions of the T-RTS, T-NTS, and the TTT showed substantial increases in discriminatory power. The highest-performing model overall was the age-adjusted T-NTS, closely followed by the R-GAP. Compared to published literature, the age-adjusted T-NTS (C-statistic = 0.88) for mortality prediction is broadly consistent. The original study developing the NTS [ 31 ] included 3,623 patients for derivation and 3,106 patients for validation, drawn from the trauma registry of a tertiary hospital in the Republic of Korea over two years. They reported a C-statistic of 0.94 on the derivation cohort and a C-statistic of 0.92 on the validation cohort. Furthermore, two studies conducted on 2,570 patients with multitrauma, and 47,971 injuries, both caused by traffic accidents, reported C-statistics of 0.94 and 0.88 respectively. [ 17 , 21 ]. Our study expands this literature by providing results for trauma that is not exclusively traffic-related. Comparable to our study, Sewalt et al. [ 22 ] analysed data from 154,476 trauma patients across the United Kingdom, reporting a C-statistic of 0.79 for mortality using the mRES tool. Our age-adjusted results somewhat outperformed those results. They also considered ISS > 15 as an outcome, and reported a maximum C-statistic of 0.74 using the Kampala Trauma Score, compared to our maximum of 0.71 for the NTS. Unfortunately, we were unable to use the Kampala Trauma Score as we don’t have sufficient neurologic data. When predicting MTA inclusion, none of the models achieved acceptable discrimination, even when age was incorporated. MTA inclusion criteria which include intensive care/high dependency unit admission, inter-hospital transfer, or extended hospital stays, reflects a broader clinical judgment and resource utilisation pattern that is not well captured by prehospital physiological data alone. Taken together, these results suggest that the highest performing pre-hospital trauma scoring systems are only achieving acceptable levels of discrimination for ISS > 15 prediction which may be insufficient form appropriate triaging. The strong performance of age-adjusted models in predicting mortality, contrasted with their poorer performance for ISS, underscores a key conceptual distinction: ISS reflects anatomical injury, while mortality is more influenced by physiological reserve and vulnerability, especially in older adults. Age, therefore, improves mortality prediction but does not enhance identification of major trauma as defined by ISS. Importantly, all trauma scoring systems include systolic blood pressure and GCS the latter emerged as the most consistently discriminative physiological variable across all outcomes, supporting its central role in trauma severity assessment [ 4 ]. Oxygen saturation is only present in the NTS and the mRES. We find the role of oxygen saturation may be a valuable and underutilised predictor of both major trauma and mortality as its present in the highest performing tool to predict all outcomes. This nationwide analysis of prehospital trauma triage performance draws on population-wide data, providing a comprehensive overview across a diverse patient cohort. Unlike previous studies that have primarily focused on severely injured patients and mortality prediction [ 18 – 21 ], this study evaluates the broader application of trauma scoring systems to predict major trauma (ISS > 15) and MTA inclusion. As trauma scoring systems continue to evolve, these findings provide important insights into how age impacts their predictive value across multiple outcomes. The inclusion of less severely injured patients and broader outcomes improves the generalisability of our results to real-world prehospital decision making. Studies applying trauma scoring systems to large-scale datasets with multiple outcome measures and not just mortality would advance understanding of how prehospital physiological parameters can most effectively guide trauma triage in diverse health systems. Future research should investigate the under- and over-triage rates associated with each scoring system in the Irish context. This would allow for a more precise evaluation of triage performance at the individual patient level across all three outcomes assessed. Clinical and Policy Implications Our results suggest that the current best pre-hospital trauma scoring system is the New Trauma Score (NTS) and its triage variant (T-NTS), which demonstrated superior predictive performance for identifying major trauma and for mortality when age was included. These tools consistently outperform the currently recommended TTT in Ireland. However, further work should be done to identify or create a trauma scoring system that achieves at least good discrimination (C-statistic > 0.8) before widespread adoption. Both the NTS and T-NTS rely on simple, readily available physiological parameters, enhancing their practicality in the prehospital setting. While the T-NTS is straightforward to implement, no current tool provides acceptable discrimination for predicting major trauma or MTA inclusion at a population-wide level. This suggests that either the tools and the variables within literature are problematic [ 2 ], but could also mean that the outcome being modelled is not optimal. To develop a truly effective trauma scoring system, patient profiles must first be clearly defined to identify those who would benefit most from a direct transfer to a major trauma centre within an acceptable timeframe. These profiles may not match the current MTA criteria or have ISS > 15 threshold. Convening a panel of expert clinicians alongside Public and Patient Involvement (PPI) representatives could help establish these patient profiles, following a similar approach to that used by Fuller et al. [ 35 ] in the United Kingdom. Only then can tools be designed to reliably predict these outcomes and support bypass of local trauma units. As Ireland advances towards its national hub-and-spoke trauma system, these findings provide important evidence to guide prehospital triage practices. They confirm that existing scores predict mortality well, but also highlight their limitations in predicting ISS > 15 or MTA inclusion. Strengths and Limitations While this study provides insight into the predictive validity of prehospital trauma scoring systems on an Irish population, the generalisation to other trauma systems may differ. Differences in EMS infrastructure, data collection, and population density may find different results - especially in low-resource settings or systems without routine electronic prehospital data. All of the trauma scoring systems with the exception of the TTT were validated on mortality prediction, so modelling alternative outcomes is a strength of the current work. Due to missing physiological data, we were only able to model 25% of all deceased patients and 43% of all those who died in hospital. However, this is still the most comprehensive analysis undertaken to date. A major limitation of the Irish trauma system is that the Dublin Fire Brigade (DFB), in addition to the NAS, transport patients in the city of Dublin, and, since the DFB operate a paper record system, we were unable to include this data. Finally, the analysis focused solely on physiological inputs which means the full decision-making complexity of the TTT, including qualitative judgements around injury type and mechanism, could not be modelled precisely. Additionally, variability in paramedics’ determination of trauma could have meant that some patients with minor trauma may have been included, even when their primary diagnosis was related to another condition. Conclusions In this study, the trauma scoring systems demonstrated their strongest predictive performance for mortality, with substantial improvements observed when age was incorporated into models that did not originally include it. However, only the New Trauma Score (NTS) and Triage-NTS (T-NTS) achieved acceptable discrimination for identifying major trauma (ISS > 15), yet their performance declined when age was added. Prediction of MTA inclusion was poor across all models, with only modest gains from incorporating age. Despite these limitations, all scoring systems retain significant clinical utility for prehospital mortality prediction across a nationwide trauma population. However, the findings also highlight that including age is beneficial for predicting mortality but it does not enhance the identification of patients with anatomical injury severity (ISS > 15), and may even impair it. There is clear need to refine the use of prehospital physiological data to more accurately predict severe trauma, though the optimal approach is likely to vary across healthcare systems. Declarations Funding This research is funded by the Health Research Board (HRB) un­der Grant Number SDAP-2021-006. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors have no other competing interests. Acknowledgements The authors would like to thank all audit coordinators and everyone in the National Ambulance Service for their continued work in collecting and preparing data, the Health Service Executive (HSE) and Richard Murray for his Patient and Public Involvement. Declaration of Interest Statements All authors declare that they have no conflict of interest. The authors confirm that the research was conducted in the absence of any personal, commercial or financial relationships. Data Availability The data used in this study are not publicly available due to their sensitive nature and privacy concerns. The data was provided by the Major Trauma Audit and the National Ambulance Service in Ireland. 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1","display":"","copyAsset":false,"role":"figure","size":59797,"visible":true,"origin":"","legend":"\u003cp\u003eSTROBE flow diagram detailing the rational for patient exclusion.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7517406/v1/7d0d3fb60d8ddb8165db97e6.png"},{"id":97178262,"identity":"9431b882-890a-41cf-b4e7-358d30b1d9a5","added_by":"auto","created_at":"2025-12-01 16:06:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1327372,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7517406/v1/e800e85c-c749-4033-8e3b-2503a481461c.pdf"},{"id":92733598,"identity":"cdbccd3e-5aef-443f-ad92-845243d2be37","added_by":"auto","created_at":"2025-10-03 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To facilitate timely identification of severe injury, trauma networks rely on triage tools and scoring systems to inform care level and transport decisions. Trauma triage tools and scoring systems are quantitative models that estimate injury severity based on physiological, anatomical, or composite parameters. These tools are critical in the prehospital setting, where rapid, evidence-based decisions are required to allocate finite emergency resources effectively. A recent systematic review by Donnelly et al. [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] identified over 50 trauma triage tools with heterogeneous scoring systems, validated in adult populations. They found that while a broad range of physiological and injury characteristics are used in these tools, there was little consensus on the optimal set of predictive variables or how these were operationalised. Among these tools, systolic blood pressure and the Glasgow Coma Scale (GCS) are the most commonly used parameters. GCS remains a cornerstone in trauma assessment due to its well-established prognostic value in both isolated traumatic brain injury (TBI) and polytrauma [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. However, when used as a standalone measure, GCS is more effective at predicting outcomes in patients with TBI than in those with major trauma without neurological involvement [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn Western Europe, including in Ireland, trauma incidents are often associated with low-energy mechanisms, with falls contributing to nearly half of all injury cases and continuing to be the leading cause of trauma [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Notably, data from the national Major Trauma Audit (MTA), which uses inclusion criteria based on Injury Severity Score (ISS), length of stay and injury characteristics (see Appendix A), show that MTA patients are almost evenly divided between those under 65 and those aged 65 and older [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. There is a tendency to under-triage older adult patients with major trauma, as illustrated by the lower rates of pre-alerted systems and lower numbers received by trauma teams [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Current guidance from the Health Service Executive (HSE) has flagged inconsistencies in how older trauma patients are pre-alerted or escalated to trauma teams [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The challenge of accurately assessing trauma severity in the prehospital setting is compounded in older adults, who in Ireland, have longer hospital stays, and experience significantly worse outcomes [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. A growing body of evidence shows that older adults experience disproportionately worse outcomes following trauma, even when injuries are anatomically similar to those sustained by younger individuals [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. This is largely attributed to factors such as frailty [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], reduced physiological reserves, co-existing medical conditions, and the use of multiple prescribed medications [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Age has consistently been identified as an independent predictor of mortality, prolonged hospitalisation, and poor functional recovery, even after adjustment for ISS and other injury characteristics [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The 2022 Major Trauma Audit (MTA) in Ireland [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] draws attention to this growing cohort of older individuals whose injury burden and care needs are not adequately captured by existing triage systems. Many widely used trauma scoring systems do not incorporate age, despite robust evidence supporting its prognostic significance [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. These findings highlight the urgent need to assess age-related vulnerabilities in prehospital trauma assessment tools [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eExisting research has evaluated the predictive accuracy of trauma triage tools primarily for mortality among patients with high-energy trauma [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], and those with severe injuries [\u003cspan additionalcitationids=\"CR19 CR20\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Fewer studies have examined the performance in broader, more representative prehospital populations [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], or considered a subsequent classification of \u0026lsquo;major trauma\u0026rsquo; as an outcome in addition to mortality. The ISS is often used as the reference for injury severity, defining major trauma as patients scoring\u0026thinsp;\u0026gt;\u0026thinsp;15 [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The ISS is derived from comprehensive retrospective anatomical assessments that typically require imaging, surgical exploration, or complete diagnostic workups. As such, it cannot be applied in the prehospital environment where decisions must be made rapidly and with limited diagnostic information [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Furthermore, not all countries use ISS\u0026thinsp;\u0026gt;\u0026thinsp;15 as the sole threshold for major trauma. In Ireland, ISS\u0026thinsp;\u0026gt;\u0026thinsp;15 is just one of the inclusion criteria for the Major Trauma Audit (MTA) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], which was established in 2016. The criteria also include patients with a hospital stay of more than 3 days, those admitted to critical care, patients transferred for specialist care and those who died in the emergency department. A more detailed list of criteria can be found in Appendix A and in [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn this study, we focused on six trauma scoring systems that utilise prehospital physiological variables, while exploring their predictive power for three clinically significant, binary, outcomes: major trauma (ISS\u0026thinsp;\u0026gt;\u0026thinsp;15), inclusion in the Major Trauma Audit (MTA) and in-hospital mortality. Additionally, we explored the predictive power of adding age into scoring systems which do not include age as a predictor in order to give epidemiological insight into the best prehospital trauma scoring system for Ireland.\u003c/p\u003e"},{"header":"Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Design and Setting\u003c/h2\u003e\u003cp\u003eWe performed a retrospective observational study using nationwide, linked, data from the National Ambulance Service (NAS) and the Irish Major Trauma Audit (MTA) covering the period from January 1, 2020 to December 31, 2022. The NAS is the statutory provider of prehospital emergency and intermediate care for Ireland. In parallel, Dublin Fire Brigade delivers emergency medical services to residents in much of the Dublin metropolitan area, but their data was not included due to the use of paper-based records. The NAS receives over 350,000 ambulance calls annually and employs more than 2,000 personnel across 102 locations. Emergency calls are initiated via the 999/112 emergency call system and triaged using the Medical Priority Dispatch System\u0026trade; (MPDS\u0026reg;), supported by a Computer Aided Dispatch (CAD) system. Attending first responders document patient demographics and physiological parameters in real time via electronic Patient Care Reports (ePCR).\u003c/p\u003e\u003cp\u003eCurrently in Ireland, trauma patients often present to acute hospitals regardless of its trauma expertise, resulting in frequent inter-hospital transfers and delayed treatment. The trauma Steering Report [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] details a shift toward a hub-and-spoke regional network, with Major Trauma Centres based in Dublin and Cork, supported by multiple Trauma Units, including a Trauma Unit with specialist services in Galway. This system is designed to provide a full spectrum of trauma care. The recommended Health Services Executive (HSE) Trauma Triage Tool (TTT) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] aims to enable first responders to triage patients based on physiology and injury criteria, allowing them to bypass Trauma Units if major trauma is suspected, and the patients can be transported to a major trauma centre within 45 minutes.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eParticipants\u003c/h3\u003e\n\u003cp\u003eTrauma cases were identified in the NAS dataset based on a working diagnosis that includes \u0026ldquo;trauma\u0026rdquo; recorded by paramedics on scene. We included adult patients aged 16 and over who were transported to hospital. Additionally, patients with incomplete physiological data were excluded, as well as those who were dead on arrival or where resuscitation was ceased. To ensure data accuracy, we further excluded patients with abnormal physiological values, which were deemed to be recording errors, with the acceptable ranges shown in Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003eC1\u003c/span\u003e. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e outlines the data cleaning process and final cohort selection.\u003c/p\u003e\n\u003ch3\u003eIdentifying Trauma Scoring Systems\u003c/h3\u003e\n\u003cp\u003eTrauma scoring systems were selected based on their reliance on routinely collected, prehospital physiological data within the NAS dataset. From the systematic review by Donnelly et al. [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], we identified the Revised Trauma Score (RTS) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], Glasgow Coma Scale, Age and Systolic Blood Pressure score (GAP) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] and the modified rapid emergency medicine score (mRES) [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Additionally, we included the New Trauma Score (NTS) [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] and a recent score: the Revised Glasgow Coma Scale, Age and Systolic Blood Pressure score (R-GAP) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. These were compared to the current HSE Trauma Triage Tool (TTT) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] based solely on its physiological parameters. The RTS, NTS and TTT do not include age whereas the GAP, R-GAP and mRES include age. Details of how to calculate each trauma scoring system can be found in Appendix B.\u003c/p\u003e\n\u003ch3\u003eVariables and Measures\u003c/h3\u003e\n\u003cp\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eKey variables required to compute trauma scoring systems included Glasgow Coma Scale (GCS), systolic blood pressure (SBP), respiratory rate (RR), heart rate (HR), peripheral oxygen saturation (SpO₂), and age. For scoring systems such as the NTS, GAP, and R-GAP, GCS was used as a raw score with the standard clinical range of 3\u0026ndash;15. Each trauma score was calculated using the corresponding parameters and coding systems as per their original formulations. A higher score generally indicated a better prognosis, except for the Modified Rapid Emergency Medicine Score (mRES) and the NAS Trauma Triage Tool (TTT), where higher scores represent poorer prognosis; therefore, to allow direct comparison, the scores of these tools were reverse coded so that higher scores uniformly indicated better prognosis.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eOutcomes:\u003c/h3\u003e\n\u003cp\u003eWe evaluated three trauma-related outcomes:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eMajor trauma\u003c/b\u003e, defined as an Injury Severity Score (ISS)\u0026thinsp;\u0026gt;\u0026thinsp;15, an internationally recognised threshold [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eMTA inclusion\u003c/b\u003e, defined retrospectively by criteria from the National Office of Clinical Audit (NOCA); full inclusion criteria are detailed in Appendix C.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eMortality\u003c/b\u003e, recorded in-hospital.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Methods and Analysis\u003c/h2\u003e\u003cp\u003eDescriptive statistics were calculated for all study variables. Continuous variables were summarised as means and standard deviations (SD) and compared using two-sample t-tests. Categorical variables were summarised as counts and percentages; between-group differences were evaluated using the chi-square (χ\u0026sup2;) test. We employed Firth's penalised likelihood binary logistic to estimate odds ratios (ORs) and corresponding 95% confidence intervals (CIs) for each trauma scoring system across the three outcomes. This approach was chosen over conventional binary logistic regression due to the rarity of the outcomes, ISS\u0026thinsp;\u0026gt;\u0026thinsp;15 (1.4%), MTA inclusion (4.1%) and mortality (0.3%). Standard logistic regression can produce biased estimates under such conditions, often producing inflated ORs, overly narrow CIs, or convergence issues due to data separation [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. In contrast, Firth regression mitigates small-sample bias and provides more stable, reliable estimates in the presence of sparse event data.\u003c/p\u003e\u003cp\u003eSince each trauma scoring system uses different scoring ranges, to facilitate comparisons across tools, the trauma scores were standardised by converting them into z-scores (with mean\u0026thinsp;=\u0026thinsp;0, SD\u0026thinsp;=\u0026thinsp;1). For each trauma tool, a score of 0 means exactly average, a score of 1 means the person\u0026rsquo;s trauma score is 1 standard deviation above the mean, and a negative trauma score means a below average score. The OR from the models represents the change in odds of the outcome per one standard deviation increase in the standardised score. Discriminative performance was assessed using the concordance (C-) statistic. Values\u0026thinsp;\u0026lt;\u0026thinsp;0.7 indicate poor discriminative ability, 0.7\u0026ndash;0.8 are considered acceptable, 0.8\u0026ndash;0.9 good, and \u0026gt;\u0026thinsp;0.9 excellent [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWe then assessed the prognostic value of age, by incorporating age into scoring systems that do not include it, using two classification schemes: as per the GAP model [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], which assigns\u0026thinsp;+\u0026thinsp;3 points for patients under 60 and 0 for those 60 or older; and the R-GAP model [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] which assigns\u0026thinsp;+\u0026thinsp;3 for patients under 50, 0 for ages 50\u0026ndash;70, and \u0026minus;\u0026thinsp;3 for those over 70. These age scores were included alongside the original scores to determine whether predictive accuracy improved with the addition of age. All analyses were performed using Stata version 18.5 (StataCorp, College Station, TX, USA).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eBias\u003c/h3\u003e\n\u003cp\u003eWe limited inclusion to patients with complete physiological data to ensure comparability across scoring systems, performing no imputation. Patients without complete prehospital records, particularly those deceased before physiological data could be collected, were excluded. This potentially introduces survivorship bias. To further protect data quality, patients with implausible physiology were excluded as shown in Appendix C (Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003eC1\u003c/span\u003e).\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eParticipants\u003c/h2\u003e\u003cp\u003eOf the 150,480 patients in the NAS dataset with a preliminary diagnosis of trauma, 127,737 were adult trauma patients transported to a hospital in Ireland between 2020 and 2022. After excluding cases with incomplete physiological data, which is required for the trauma scoring systems\u0026rsquo; calculation, the final cohort included 101,114 patients. The data cleaning process is outlined in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePatient characteristics for each outcome with the t-statistic and p-value stated to determine significance between each outcome.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eISS\u0026thinsp;\u0026gt;\u0026thinsp;15\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eISS\u0026thinsp;\u0026le;\u0026thinsp;15\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003et-statistic\u003c/p\u003e\u003cp\u003e(p-value)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMTA Patient\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNon-MTA patients\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003et-statistic\u003c/p\u003e\u003cp\u003e(p-value)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eDeceased\u003c/p\u003e\u003cp\u003ePatients\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eSurviving\u003c/p\u003e\u003cp\u003ePatients\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003et-statistic\u003c/p\u003e\u003cp\u003e(p-value)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e101,114\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,422 (1.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e99,692 (98.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4,190 (4.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e96,924 (95.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e284 (0.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e101,830 (99.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e59.7 (23.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e62.8 (21.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e59.6 (23.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.1 (\u0026lt;\u0026thinsp;.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e65.4 (20.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e59.4 (23.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e16.5 (\u0026lt;\u0026thinsp;.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e76.5 (17.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e59.6 (23.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e12.3 (\u0026lt;\u0026thinsp;.001)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSpO2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e96.2 (5.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e94.2 (7.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e96.3 (5.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e14.7 (\u0026lt;\u0026thinsp;.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e94.9 (6.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e96.3 (5.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e17.7 (\u0026lt;\u0026thinsp;.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e91.7 (11.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e96.2 (5.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e15.0 (\u0026lt;\u0026thinsp;.001)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGCS\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14.8 (1.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.3 (3.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14.8 (0.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e60.0 (\u0026lt;\u0026thinsp;.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e14.2 (2.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e14.8 (0.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e36.7 (\u0026lt;\u0026thinsp;.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e11.4 (4.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e14.8 (0.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e58.8 (\u0026lt;\u0026thinsp;.001)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSystolic Blood\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003ePressure\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e142.1 (25.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e139.8 (30.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e142.1 (25.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.4 (\u0026lt;\u0026thinsp;.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e141.4 (28.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e142.1 (24.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.7 (0.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e146.0 (38.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e142.1 (25.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e2.7 (.008)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHeart Rate\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e84.1 (18.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e85.1 (22.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e84.1 (18.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.1 (0.036)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e84.8 (20.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e84.1 (18.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.62 (0.009)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e85.2 (26.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e84.1 (18.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1.0 (0.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRespiration Rate\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18.1 (4.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19.3 (6.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e18.0 (4.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e10.5 (\u0026lt;\u0026thinsp;.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e19.0 (5.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e18.0 (4.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e14.4 (\u0026lt;\u0026thinsp;.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e18.9 (6.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e18.0 (4.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e3.2 (.001)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e*Sex\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026dagger; 98,062\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,422\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026dagger; 96,640\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026Dagger; 94.3 (\u0026lt;\u0026thinsp;.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4,190\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026dagger; 93,872\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026Dagger; 13.4 (\u0026lt;\u0026thinsp;.001)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e284\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026dagger; 97,778\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u0026Dagger; 0.5 (0.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMale\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e49,808 (50.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e904 (63.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48,904 (50.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2,244 (53.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e47,564 (50.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e150 (52.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e49,658 (50.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFemale\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e48,254 (49.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e518 (36.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e47,736 (49.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1,946 (46.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e46,308 (49.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e134 (47.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e48,120 (49.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMechanism of injury\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e58,337\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,422\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e56,915\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026sect;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4,190\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e54,147\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026sect;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e284\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e58,053\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u0026sect;\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFall\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41,037 (70.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e993 (69.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e40,044 (70.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3,203 (76.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e37,834 (69.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e243 (85.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e40,794 (70.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMotor Vehicle\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eIncident\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8,823 (15.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e292 (20.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8,531 (15.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e688 (16.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8,135 (15.0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e22 (7.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e8,801 (15.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOther\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8,477 (14.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e137 (9.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8,340 (14.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e299 (7.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8,178 (15.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e19 (6.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e8,458 (14.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"11\" nameend=\"c11\" namest=\"c1\"\u003e\u003cp\u003e\u0026dagger; In total 3,057 patients are missing a sex variable.\u003c/p\u003e\u003cp\u003e\u0026Dagger; A χ\u0026sup2; test was performed and quoted.\u003c/p\u003e\u003cp\u003e\u0026sect;\u0026nbsp;100% of target patients have an MOI\u003c/p\u003e\u003cp\u003e‖ MTA inclusion is based on ISS, injury and length of stay criteria (appendix A).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eFirth regression OR with 95% CI and C-statistic for each of the trauma scoring systems and outcomes.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eISS\u0026thinsp;\u0026gt;\u0026thinsp;15\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMTA inclusion\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDeceased\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eC-statistic ISS\u0026thinsp;\u0026gt;\u0026thinsp;15\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eC-statistic MTA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eC-Statistic Deceased\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRTS equation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.34\u003c/p\u003e\u003cp\u003e[1.31, 1.36]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.22\u003c/p\u003e\u003cp\u003e[1.20, 1.24]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.44\u003c/p\u003e\u003cp\u003e[1.40, 1.47]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.71\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eT-RTS\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.33\u003c/p\u003e\u003cp\u003e[1.30, 1.36]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.22\u003c/p\u003e\u003cp\u003e[1.20, 1.24]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.40\u003c/p\u003e\u003cp\u003e[1.36, 1.44]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.71\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNTS equation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.54\u003c/p\u003e\u003cp\u003e[1.51, 1.58]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.37\u003c/p\u003e\u003cp\u003e[1.34, 1.39]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.72\u003c/p\u003e\u003cp\u003e[1.67, 1.77]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eT-NTS\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.53\u003c/p\u003e\u003cp\u003e[1.50, 1.56]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.35\u003c/p\u003e\u003cp\u003e[1.33, 1.37]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.68\u003c/p\u003e\u003cp\u003e[1.64, 1.74]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGAP\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.79\u003c/p\u003e\u003cp\u003e[1.73, 1.85]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.49\u003c/p\u003e\u003cp\u003e[1.46, 1.53]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.34\u003c/p\u003e\u003cp\u003e[2.23, 2.45]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eR-GAP\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.92\u003c/p\u003e\u003cp\u003e[1.84, 2.01]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.54\u003c/p\u003e\u003cp\u003e[1.50, 1.59]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.68\u003c/p\u003e\u003cp\u003e[3.40, 3.98]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTTT\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.53\u003c/p\u003e\u003cp\u003e[1.50, 1.57]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.42\u003c/p\u003e\u003cp\u003e[1.40, 1.45]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.66\u003c/p\u003e\u003cp\u003e[1.60, 1.72]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003emRES\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.77\u003c/p\u003e\u003cp\u003e[1.69, 1.85]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.51\u003c/p\u003e\u003cp\u003e[1.47, 1.55]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.04\u003c/p\u003e\u003cp\u003e[2.82, 3.28]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003e*All logistic regression p-values are significant with p\u0026thinsp;\u0026lt;\u0026thinsp;.001.\u003c/p\u003e\u003cp\u003e‖ MTA inclusion is based on ISS, injury and length of stay criteria (appendix A).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e Firth regression ORs with 95% CI and C-statistics (change score from Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) when incorporating age as a categorical variable into various tools.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eIncluding age as GAP Scoring\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eISS\u0026thinsp;\u0026gt;\u0026thinsp;15 OR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eC-statistic ISS\u0026thinsp;\u0026gt;\u0026thinsp;15\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(change)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eMTA OR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eC-Statistic MTA (change)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eDeceased OR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003eC-statistic Deceased (change)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eT-RTS\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.62\u003c/p\u003e\u003cp\u003e[1.54, 1.71]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.60 (-0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.44\u003c/p\u003e\u003cp\u003e[1.39, 1.48]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.59 (+\u0026thinsp;0.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.02\u003c/p\u003e\u003cp\u003e[3.61, 4.48]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.82 (+\u0026thinsp;0.11)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eT-NTS\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.86\u003c/p\u003e\u003cp\u003e[1.80, 1.92]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.67 (-0.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.54\u003c/p\u003e\u003cp\u003e[1.50, 1.58]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.62 (+\u0026thinsp;0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.51\u003c/p\u003e\u003cp\u003e[2.38, 2.63]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.86 (+\u0026thinsp;0.05)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTTT\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.53\u003c/p\u003e\u003cp\u003e[1.49, 1.56]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.60 (-0.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.41\u003c/p\u003e\u003cp\u003e[1.39, 1.44]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.63 (+\u0026thinsp;0.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.69\u003c/p\u003e\u003cp\u003e[1.62, 1.75]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.81 (+\u0026thinsp;0.09)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eIncluding age as R-GAP Scoring\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eISS\u0026thinsp;\u0026gt;\u0026thinsp;15 OR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eC-statistic ISS\u0026thinsp;\u0026gt;\u0026thinsp;15\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e(change)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003eMTA OR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eC-Statistic MTA (change)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003eDeceased OR\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003eC-statistic Deceased (change)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eT-RTS\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.40\u003c/p\u003e\u003cp\u003e[1.33, 1.48]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.59 (-0.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.39\u003c/p\u003e\u003cp\u003e[1.34, 1.43]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.58 (+\u0026thinsp;0.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.50\u003c/p\u003e\u003cp\u003e[6.96, 10.39]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.83 (+\u0026thinsp;0.12)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eT-NTS\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.97\u003c/p\u003e\u003cp\u003e[1.88, 2.07]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.65 (-0.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.57\u003c/p\u003e\u003cp\u003e[1.52, 1.62]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.61 (+\u0026thinsp;0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.06\u003c/p\u003e\u003cp\u003e[3.74, 4.41]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.88 (+\u0026thinsp;0.07)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTTT\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.48\u003c/p\u003e\u003cp\u003e[1.39, 1.56]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.60 (-0.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.48\u003c/p\u003e\u003cp\u003e[1.42, 1.53]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.60 (+\u0026thinsp;0.01)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.44\u003c/p\u003e\u003cp\u003e[5.23, 7.92]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.79 (+\u0026thinsp;0.07)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003e*All logistic regression p-values are significant with p\u0026thinsp;\u0026lt;\u0026thinsp;.001.\u003c/p\u003e\u003cp\u003e‖ MTA inclusion is based on ISS, injury and length of stay criteria (appendix A).\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eBaseline Characteristics and Physiology\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents baseline characteristics and physiological parameters stratified by outcome. Mechanism of injury was recorded in 57.7% of all cases; however, this variable was available for all patients with ISS\u0026thinsp;\u0026gt;\u0026thinsp;15, those included in the Major Trauma Audit (MTA), and the deceased. Among patients with a known mechanism of injury, the majority (70.3%) sustained injuries from falls. Significant sex differences were observed in the ISS\u0026thinsp;\u0026gt;\u0026thinsp;15 vs\u0026thinsp;\u0026le;\u0026thinsp;15 groups and MTA vs non-MTA groups, with males overrepresented in both. No significant sex differences were observed in the mortality outcome. Statistically significant age differences were observed across all three outcome groups when compared to their respective counterparts: mortality patients, were, on average, 16.9 years older than survivors; those included in the MTA were 6 years older than non-MTA patients; and patients with ISS\u0026thinsp;\u0026gt;\u0026thinsp;15 were 3.2 years older than non-major trauma patients.\u003c/p\u003e\u003cp\u003eGlasgow Coma Scale (GCS) was significantly lower in patients with ISS\u0026thinsp;\u0026gt;\u0026thinsp;15, MTA inclusion, and in-hospital death (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Systolic blood pressure (SBP) did not differ significantly between the MTA and non-MTA groups, while heart rate, which is used only in the mRES, showed no significant difference between deceased and surviving patients. Oxygen saturation (SpO₂) was found to be significant across all patient groups, and its statistical significance was second only to GCS across all variables.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eComparative Performance of Trauma Scoring Systems\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents results from logistic regression models using standardised trauma scores as predictors. In the prediction of major trauma (ISS\u0026thinsp;\u0026gt;\u0026thinsp;15), all scoring systems demonstrated poor discrimination (C-statistic\u0026thinsp;\u0026lt;\u0026thinsp;0.70) except the NTS and T-NTS, which achieved acceptable performance (C-statistics 0.71 and 0.70, respectively). However, the R-GAP had the largest OR, demonstrating changes in the R-GAP score were more strongly associated with higher odds of major trauma relative to other scoring systems - despite its poor discriminatory ability. All of the trauma scoring systems were poor at predicting MTA inclusion, with C-statistics varying from 0.54 for the RTS and T-RTS to 0.62 for the NTS. For mortality prediction, trauma scores that incorporated age, GAP, R-GAP and mRES, achieved the highest C-statistics and standardised odds ratios (ORs), with R-GAP outperforming all other scoring systems. Among scores without age, the NTS had the best performance. Overall, predictive validity ranged from acceptable to good for mortality, but remained poor for MTA inclusion and poor for ISS\u0026thinsp;\u0026gt;\u0026thinsp;15 prediction with the exception of the NTS and T-NTS.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eIncorporating Age into Trauma Scoring Systems\u003c/h2\u003e\u003cp\u003eWe examined the effects of incorporating two age classification schemes into trauma scoring systems that do not originally include it, from the GAP and R-GAP models (age coding is detailed in Appendix B Table A2). Results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The RTS and NTS were not modified to include age, as both are derived from regression-based equations calibrated on specific datasets. Incorporating age would require re-estimating the original model coefficients through a new regression analysis, which was beyond the scope of this study and would compromise comparability with the validated scores.\u003c/p\u003e\u003cp\u003eIncluding age had a negative impact on predictive performance for identifying ISS\u0026thinsp;\u0026gt;\u0026thinsp;15. All C-Statistics, shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, were in the poor range when age was included using either system. The T-NTS C-statistic also decreased from the acceptable to the poor range. Overall, the tools performed better in predicting ISS\u0026thinsp;\u0026gt;\u0026thinsp;15 when age was omitted.\u003c/p\u003e\u003cp\u003eFor MTA inclusion, incorporating age only modestly improved each model\u0026rsquo;s performance. Notably, the T-RTS models, using the GAP adjustment, showed a 0.05 increase in C-statistic, reaching 0.59. However, none of the age-incorporated models achieved acceptable discrimination.\u003c/p\u003e\u003cp\u003eAge adjustment had a marked impact on mortality prediction, with all models showing substantial gains in discriminative performance following the inclusion of age categories. The C-statistic for the T-RTS improved by +\u0026thinsp;0.12 with the R-GAP age inclusion, moving from the acceptable to the good range. The T-NTS demonstrated the highest C-statistic\u0026thinsp;=\u0026thinsp;0.88, slightly outperforming the R-GAP trauma scoring system in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e (C-statistic\u0026thinsp;=\u0026thinsp;0.87), and emerging as the best predictor of in-hospital mortality. Additionally, the age-adjusted T-NTS (OR\u0026thinsp;=\u0026thinsp;4.06) produced a higher standardised odds ratio for mortality compared to the R-GAP model (OR\u0026thinsp;=\u0026thinsp;3.68) reflecting a stronger association with mortality and a superior discrimination ability. This highlights the effect size and classification performance of the age adjusted T-NTS.\u003c/p\u003e\u003cp\u003eThe currently recommended TTT showed notable improvements in mortality prediction with the inclusion of age, reaching the acceptable range for both GAP and R-GAP adjustments. However, age inclusion only modestly improved the TTT\u0026rsquo;s performance in predicting MTA inclusion and, actually worsened its performance in predicting ISS\u0026thinsp;\u0026gt;\u0026thinsp;15.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eKey Findings and Interpretation\u003c/h2\u003e\u003cp\u003eIn this nationwide retrospective study, six validated prehospital trauma scoring systems were evaluated using Irish trauma data from 2020\u0026ndash;2022. For the first time, the prehospital trauma scoring systems were assessed for their ability to simultaneously predict three key outcomes: ISS\u0026thinsp;\u0026gt;\u0026thinsp;15, MTA inclusion, and mortality. Overall, the results showed that no trauma scoring system predicted ISS\u0026thinsp;\u0026gt;\u0026thinsp;15 and MTA inclusion satisfactorily, suggesting that the usefulness of these tools for bypassing local hospitals, or predicting major trauma, may be limited. This study contributes to international literature by demonstrating how existing scores perform across population-wide data.\u003c/p\u003e\u003cp\u003eAll scoring systems performed best when predicting mortality. The addition of age consistently improved the trauma scoring systems\u0026rsquo; performance, transforming all scores into good predictors of mortality. The age-adjusted versions of the T-RTS, T-NTS, and the TTT showed substantial increases in discriminatory power. The highest-performing model overall was the age-adjusted T-NTS, closely followed by the R-GAP. Compared to published literature, the age-adjusted T-NTS (C-statistic\u0026thinsp;=\u0026thinsp;0.88) for mortality prediction is broadly consistent. The original study developing the NTS [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] included 3,623 patients for derivation and 3,106 patients for validation, drawn from the trauma registry of a tertiary hospital in the Republic of Korea over two years. They reported a C-statistic of 0.94 on the derivation cohort and a C-statistic of 0.92 on the validation cohort. Furthermore, two studies conducted on 2,570 patients with multitrauma, and 47,971 injuries, both caused by traffic accidents, reported C-statistics of 0.94 and 0.88 respectively. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Our study expands this literature by providing results for trauma that is not exclusively traffic-related.\u003c/p\u003e\u003cp\u003eComparable to our study, Sewalt et al. [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] analysed data from 154,476 trauma patients across the United Kingdom, reporting a C-statistic of 0.79 for mortality using the mRES tool. Our age-adjusted results somewhat outperformed those results. They also considered ISS\u0026thinsp;\u0026gt;\u0026thinsp;15 as an outcome, and reported a maximum C-statistic of 0.74 using the Kampala Trauma Score, compared to our maximum of 0.71 for the NTS. Unfortunately, we were unable to use the Kampala Trauma Score as we don\u0026rsquo;t have sufficient neurologic data. When predicting MTA inclusion, none of the models achieved acceptable discrimination, even when age was incorporated. MTA inclusion criteria which include intensive care/high dependency unit admission, inter-hospital transfer, or extended hospital stays, reflects a broader clinical judgment and resource utilisation pattern that is not well captured by prehospital physiological data alone. Taken together, these results suggest that the highest performing pre-hospital trauma scoring systems are only achieving acceptable levels of discrimination for ISS\u0026thinsp;\u0026gt;\u0026thinsp;15 prediction which may be insufficient form appropriate triaging.\u003c/p\u003e\u003cp\u003eThe strong performance of age-adjusted models in predicting mortality, contrasted with their poorer performance for ISS, underscores a key conceptual distinction: ISS reflects anatomical injury, while mortality is more influenced by physiological reserve and vulnerability, especially in older adults. Age, therefore, improves mortality prediction but does not enhance identification of major trauma as defined by ISS. Importantly, all trauma scoring systems include systolic blood pressure and GCS the latter emerged as the most consistently discriminative physiological variable across all outcomes, supporting its central role in trauma severity assessment [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Oxygen saturation is only present in the NTS and the mRES. We find the role of oxygen saturation may be a valuable and underutilised predictor of both major trauma and mortality as its present in the highest performing tool to predict all outcomes.\u003c/p\u003e\u003cp\u003eThis nationwide analysis of prehospital trauma triage performance draws on population-wide data, providing a comprehensive overview across a diverse patient cohort. Unlike previous studies that have primarily focused on severely injured patients and mortality prediction [\u003cspan additionalcitationids=\"CR19 CR20\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], this study evaluates the broader application of trauma scoring systems to predict major trauma (ISS\u0026thinsp;\u0026gt;\u0026thinsp;15) and MTA inclusion. As trauma scoring systems continue to evolve, these findings provide important insights into how age impacts their predictive value across multiple outcomes. The inclusion of less severely injured patients and broader outcomes improves the generalisability of our results to real-world prehospital decision making. Studies applying trauma scoring systems to large-scale datasets with multiple outcome measures and not just mortality would advance understanding of how prehospital physiological parameters can most effectively guide trauma triage in diverse health systems. Future research should investigate the under- and over-triage rates associated with each scoring system in the Irish context. This would allow for a more precise evaluation of triage performance at the individual patient level across all three outcomes assessed.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eClinical and Policy Implications\u003c/h2\u003e\u003cp\u003eOur results suggest that the current best pre-hospital trauma scoring system is the New Trauma Score (NTS) and its triage variant (T-NTS), which demonstrated superior predictive performance for identifying major trauma and for mortality when age was included. These tools consistently outperform the currently recommended TTT in Ireland. However, further work should be done to identify or create a trauma scoring system that achieves at least good discrimination (C-statistic\u0026thinsp;\u0026gt;\u0026thinsp;0.8) before widespread adoption. Both the NTS and T-NTS rely on simple, readily available physiological parameters, enhancing their practicality in the prehospital setting. While the T-NTS is straightforward to implement, no current tool provides acceptable discrimination for predicting major trauma or MTA inclusion at a population-wide level. This suggests that either the tools and the variables within literature are problematic [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], but could also mean that the outcome being modelled is not optimal.\u003c/p\u003e\u003cp\u003eTo develop a truly effective trauma scoring system, patient profiles must first be clearly defined to identify those who would benefit most from a direct transfer to a major trauma centre within an acceptable timeframe. These profiles may not match the current MTA criteria or have ISS\u0026thinsp;\u0026gt;\u0026thinsp;15 threshold. Convening a panel of expert clinicians alongside Public and Patient Involvement (PPI) representatives could help establish these patient profiles, following a similar approach to that used by Fuller et al. [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] in the United Kingdom. Only then can tools be designed to reliably predict these outcomes and support bypass of local trauma units. As Ireland advances towards its national hub-and-spoke trauma system, these findings provide important evidence to guide prehospital triage practices. They confirm that existing scores predict mortality well, but also highlight their limitations in predicting ISS\u0026thinsp;\u0026gt;\u0026thinsp;15 or MTA inclusion.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eStrengths and Limitations\u003c/h2\u003e\u003cp\u003eWhile this study provides insight into the predictive validity of prehospital trauma scoring systems on an Irish population, the generalisation to other trauma systems may differ. Differences in EMS infrastructure, data collection, and population density may find different results - especially in low-resource settings or systems without routine electronic prehospital data. All of the trauma scoring systems with the exception of the TTT were validated on mortality prediction, so modelling alternative outcomes is a strength of the current work. Due to missing physiological data, we were only able to model 25% of all deceased patients and 43% of all those who died in hospital. However, this is still the most comprehensive analysis undertaken to date. A major limitation of the Irish trauma system is that the Dublin Fire Brigade (DFB), in addition to the NAS, transport patients in the city of Dublin, and, since the DFB operate a paper record system, we were unable to include this data. Finally, the analysis focused solely on physiological inputs which means the full decision-making complexity of the TTT, including qualitative judgements around injury type and mechanism, could not be modelled precisely. Additionally, variability in paramedics\u0026rsquo; determination of trauma could have meant that some patients with minor trauma may have been included, even when their primary diagnosis was related to another condition.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this study, the trauma scoring systems demonstrated their strongest predictive performance for mortality, with substantial improvements observed when age was incorporated into models that did not originally include it. However, only the New Trauma Score (NTS) and Triage-NTS (T-NTS) achieved acceptable discrimination for identifying major trauma (ISS\u0026thinsp;\u0026gt;\u0026thinsp;15), yet their performance declined when age was added. Prediction of MTA inclusion was poor across all models, with only modest gains from incorporating age. Despite these limitations, all scoring systems retain significant clinical utility for prehospital mortality prediction across a nationwide trauma population. However, the findings also highlight that including age is beneficial for predicting mortality but it does not enhance the identification of patients with anatomical injury severity (ISS\u0026thinsp;\u0026gt;\u0026thinsp;15), and may even impair it. There is clear need to refine the use of prehospital physiological data to more accurately predict severe trauma, though the optimal approach is likely to vary across healthcare systems.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cu\u003eFunding\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research is funded by the Health Research Board (HRB) un­der Grant Number SDAP-2021-006. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors have no other competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eAcknowledgements\u0026nbsp;\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank all audit coordinators and everyone in the National Ambulance Service for their continued work in collecting and preparing data, the Health Service Executive (HSE) and Richard Murray for his Patient and Public Involvement. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eDeclaration of Interest Statements\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors declare that they have no conflict of interest. The authors confirm that the research was conducted in the absence of any personal, commercial or financial relationships.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eData Availability\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in this study are not publicly available due to their sensitive nature and privacy concerns. The data was provided by the Major Trauma Audit and the National Ambulance Service in Ireland.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cu\u003eGenerative AI Declaration\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors did not use generative AI tools or services to assist with preparation or editing of this work. The authors take full responsibility for the content of this publication.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003e\u003cstrong\u003eWorld Health Organization.\u003c/strong\u003e\u003cem\u003ePreventing injuries and violence: an overview\u003c/em\u003e. Geneva: World Health Organization; 2022 [accessed 2025 May 19]. Available from: https://iris.who.int/bitstream/handle/10665/361331/9789240047136-eng.pdf?sequence=1\u003c/li\u003e\n\u003cli\u003eDonnelly NA, Brent L, Hickey P, Masterson S, Deasy C, Moloney J, Linvill M, Zaidan R, Simpson A, Doyle F. Substantial heterogeneity in trauma triage tool characteristic operationalization for identification of major trauma: a hybrid systematic review. Eur J Trauma Emerg Surg. 2025;51(1):74.\u003c/li\u003e\n\u003cli\u003eNewgard CD, Richardson D, Holmes JF, Rea TD, Hsia RY, Mann NC, Staudenmayer K, Barton ED, Bulger EM, Haukoos JS, Western Emergency Services Translational Research Network (WESTRN) Investigators. Physiologic field triage criteria for identifying seriously injured older adults. Prehosp Emerg Care. 2014;18(4):461\u0026ndash;470.\u003c/li\u003e\n\u003cli\u003eTeasdale G, Maas A, Lecky F, Manley G, Stocchetti N, Murray G. The Glasgow Coma Scale at 40 years: standing the test of time. Lancet Neurol. 2014;13(8):844\u0026ndash;854.\u003c/li\u003e\n\u003cli\u003eOsler T, Cook A, Glance LG, Lecky F, Bouamra O, Garrett M, Buzas JS, Hosmer DW. The differential mortality of Glasgow Coma Score in patients with and without head injury. Injury. 2016 Sep 1;47(9):1879-85.\u003c/li\u003e\n\u003cli\u003eHaagsma JA, Charalampous P, Ariani F, Gallay A, Moesgaard Iburg K, Nena E, Ngwa CH, Rommel A, Zelviene A, Abegaz KH, Al Hamad H. 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Injury. 2025;56(6):112343.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eTrauma Steering Group.\u003c/strong\u003e\u003cem\u003eA trauma system for Ireland: report of the Trauma Steering Group\u003c/em\u003e [Internet]. Dublin: Department of Health; 2018 [accessed 2025 May 19]. Available from: https://assets.gov.ie/static/documents/a-trauma-system-for-ireland-report-of-the-trauma-steering-group.pdf\u003c/li\u003e\n\u003cli\u003eKojima M, Endo A, Shiraishi A, Otomo Y. Age-related characteristics and outcomes for patients with severe trauma: analysis of Japan\u0026rsquo;s nationwide trauma registry. Ann Emerg Med. 2019;73(3):281\u0026ndash;290.\u003c/li\u003e\n\u003cli\u003eGuidet B, De Lange DW, Boumendil A, Leaver S, Watson X, Boulanger C, Szczeklik W, Artigas A, Morandi A, Andersen F, Zafeiridis T. The contribution of frailty, cognition, activity of daily life and comorbidities on outcome in acutely admitted patients over 80 years in European ICUs: the VIP2 study. Intensive Care Med. 2020;46:57\u0026ndash;69.\u003c/li\u003e\n\u003cli\u003eAtinga A, Shekkeris A, Fertleman M, Batrick N, Kashef E, Dick E. Trauma in the elderly patient. Br J Radiol. 2018;91(1087):20170739\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eNational Office of Clinical Audit (NOCA).\u003c/strong\u003e\u003cem\u003eMTA older adult report 2017\u0026ndash;2021\u003c/em\u003e [Internet]. Dublin: NOCA; 2024 [accessed 2025 May 19]. Available from: https://www.noca.ie/documents/major-trauma-audit-report-focused-on-older-adults-2017-2021/\u003c/li\u003e\n\u003cli\u003eSammy I, Lecky F, Sutton A, Leaviss J, O\u0026rsquo;Cathain A. Factors affecting mortality in older trauma patients\u0026mdash;a systematic review and meta-analysis. Injury. 2016;47(6):1170\u0026ndash;1183.\u003c/li\u003e\n\u003cli\u003eJavali RH, Patil A, Srinivasarangan M. Comparison of injury severity score, new injury severity score, revised trauma score and trauma and injury severity score for mortality prediction in elderly trauma patients. Indian J Crit Care Med. 2019;23(2):73.\u003c/li\u003e\n\u003cli\u003eKenarangi T, Rahmani F, Yazdani A, Ahmadi GD, Lotfi M, Khalaj TA. Comparison of GAP, R-GAP, and new trauma score (NTS) systems in predicting mortality of traffic accidents that injure hospitals at Mashhad University of Medical Sciences. Heliyon. 2024;10(16):e26030\u003c/li\u003e\n\u003cli\u003eFarzan N, Ghomi SY, Mohammadi AR. A retrospective study on evaluating GAP, MGAP, RTS and ISS trauma scoring system for the prediction of mortality among multiple trauma patients. Ann Med Surg. 2022;76:103564.\u003c/li\u003e\n\u003cli\u003eMerchant AA, Shaukat N, Ashraf N, Hassan S, Jarrar Z, Abbasi A, Ahmed T, Atiq H, Khan UR, Khan NU, Mushtaq S. Which curve is better? A comparative analysis of trauma scoring systems in a South Asian country. Trauma Surg Acute Care Open. 2023;8(1):e001169.\u003c/li\u003e\n\u003cli\u003eMohammed Z, Saleh Y, AbdelSalam EM, Mohammed NB, El-Bana E, Hirshon JM. Evaluation of the Revised Trauma Score, MGAP, and GAP scoring systems in predicting mortality of adult trauma patients in a low-resource setting. BMC Emerg Med. 2022;22(1):90.\u003c/li\u003e\n\u003cli\u003eKhajoei R, Abadi MZ, Dehesh T, Heydarpour N, Shokohian S, Rahmani F. Predictive value of the Glasgow Coma Scale, age, and arterial blood pressure and the new trauma score indicators to determine the hospital mortality of multiple trauma patients. Arch Trauma Res. 2021;10(2):86\u0026ndash;91.\u003c/li\u003e\n\u003cli\u003eSewalt CA, Venema E, Wiegers EJ, Lecky FE, Schuit SC, den Hartog D, Steyerberg EW, Lingsma HF. Trauma models to identify major trauma and mortality in the prehospital setting. Br J Surg. 2020;107(4):373\u0026ndash;380.\u003c/li\u003e\n\u003cli\u003eBaker SP, O\u0026apos;Neill B. The injury severity score: an update. J Trauma Acute Care Surg. 1976;16(11):882\u0026ndash;885\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003ePalmer C.\u003c/strong\u003e Major trauma and the injury severity score\u0026mdash;where should we set the bar? \u003cem\u003eIn: Annual Proceedings/Association for the Advancement of Automotive Medicine\u003c/em\u003e. 2007;51:13.\u003c/li\u003e\n\u003cli\u003ePalmer CS, Gabbe BJ, Cameron PA. Defining major trauma using the 2008 Abbreviated Injury Scale. Injury. 2016;47(1):109\u0026ndash;115.\u003c/li\u003e\n\u003cli\u003eDeasy C, Cronin M, Cahill F, Geary U, Houlihan P, Woodford M, Lecky F, Mealy K, Crowley P, Major Trauma Audit Governance Committee. Implementing major trauma audit in Ireland. Injury. 2016 1;47(1):166-72.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eHealth Service Executive.\u003c/strong\u003e\u003cem\u003eTrauma triage tool and trauma access protocol\u003c/em\u003e [Internet]. Dublin: Health Service Executive; 2022 [accessed 2025 May 19]. Available from: https://www.hse.ie/eng/about/who/acute-hospitals-division/trauma-services/resources/trauma-triage-tool-and-trauma-access-protocol.pdf\u003c/li\u003e\n\u003cli\u003eChampion HR, Sacco WJ, Copes WS, Gann DS, Gennarelli TA, Flanagan ME. A revision of the Trauma Score. J Trauma Acute Care Surg. 1989;29(5):623\u0026ndash;629.\u003c/li\u003e\n\u003cli\u003eKondo Y, Abe T, Kohshi K, Tokuda Y, Cook EF, Kukita I. Revised trauma scoring system to predict in-hospital mortality in the emergency department: Glasgow Coma Scale, Age, and Systolic Blood Pressure score. Crit Care. 2011;15: R191.\u003c/li\u003e\n\u003cli\u003eMiller RT, Nazir N, McDonald T, Cannon CM. The modified rapid emergency medicine score: a novel trauma triage tool to predict in-hospital mortality. Injury. 2017;48(9):1870\u0026ndash;1877.\u003c/li\u003e\n\u003cli\u003eJeong JH, Park YJ, Kim DH, Kim TY, Kang C, Lee SH, Lee SB, Kim SC, Lim D. The new trauma score (NTS): a modification of the revised trauma score for better trauma mortality prediction. BMC Surg. 2017;17:40.\u003c/li\u003e\n\u003cli\u003eSepehri Majd P, Alimohammadi Siyabani A, Ebrahimi Bakhtavar H, Rahmani F. A new method to predict the in-hospital outcome of multi-trauma patients: R-GAP. J Emerg Pract Trauma. 2022;8(2):128\u0026ndash;133.\u003c/li\u003e\n\u003cli\u003eFirth D. Bias reduction of maximum likelihood estimates. Biometrika. 1993 Mar 1;80(1):27-38\u003c/li\u003e\n\u003cli\u003eWhite N, Parsons R, Collins G, Barnett A. Evidence of questionable research practices in clinical prediction models. BMC Med. 2023;21(1):339\u003c/li\u003e\n\u003cli\u003eFuller G, Keating S, Turner J, Miller J, Holt C, Smith JE, Lecky F. Injured patients who would benefit from expedited major trauma centre care: a consensus-based definition for the United Kingdom. British paramedic journal. 2021 Dec 1;6(3):7-14.\u003c/li\u003e\n\u003cli\u003eBrasel KJ, Guse C, Gentilello LM, Nirula R. Heart rate: is it truly a vital sign?. Journal of Trauma and Acute Care Surgery. 2007 Apr 1;62(4):812-7.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-trauma-and-emergency-surgery","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejot","sideBox":"Learn more about [European Journal of Trauma and Emergency Surgery](http://link.springer.com/journal/68)","snPcode":"68","submissionUrl":"https://submission.nature.com/new-submission/68/3","title":"European Journal of Trauma and Emergency Surgery","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Trauma, Prehospital Care, Scoring Systems, Mortality, Triage, Global Health","lastPublishedDoi":"10.21203/rs.3.rs-7517406/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7517406/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose:\u003c/strong\u003e Prehospital trauma scoring systems have traditionally used mortality as the primary outcome, but trauma is complex condition with diverse presentations and outcomes, each of which are moderated by age. We assess the predictive performance of six prehospital trauma scoring systems across multiple outcomes, and explore the inclusion of simple age categorisations on predictive validity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e This retrospective observational study used nationwide, linked prehospital data from the National Ambulance Service (NAS) and in-hospital data from the Irish Major Trauma Audit (MTA) where the inclusion criteria are based on Injury Severity Score (ISS), length of stay and injury details. 101,114 adult trauma patients were included. Six trauma scoring systems were applied to the data and predictive performance was compared for three outcomes: major trauma (ISS \u0026gt;15), MTA inclusion, and in-hospital mortality. For tools not originally including age, prior validated age categories were added and predictive performance re-assessed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e All trauma scores performed suboptimally in predicting ISS\u0026gt;15 (C-statistics 0.61-0.71). For MTA inclusion, no model performed acceptably (≤0.64). Mortality prediction was acceptable/good (0.71-0.87). Including simple age categorisations into existing tools reduced each tool’s predictive accuracy for ISS\u0026gt;15, yielded modest improvements for MTA inclusion, and improved mortality prediction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e Trauma scoring systems were most effective at predicting mortality, especially when age was included. However, they were less effective for identifying major trauma (ISS \u0026gt;15) or MTA inclusion, even with age adjustments. The New Trauma Score, which includes oxygen saturation, showed the best overall performance for major trauma, supporting broader use in prehospital triage.\u003c/p\u003e","manuscriptTitle":"Comparative Statistical Performance of Prehospital Scoring Systems With and Without Age Adjustments for Predicting Multiple Trauma Outcomes: An Observational Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-03 16:20:44","doi":"10.21203/rs.3.rs-7517406/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-13T04:25:06+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-13T00:14:59+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-05T22:23:48+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-30T15:38:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-30T14:06:18+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-29T13:44:15+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-28T14:18:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"64399255232398431842230798790487770166","date":"2025-09-28T13:40:35+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-26T11:47:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"73278152152721038657811537481587824835","date":"2025-09-26T10:36:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"168503603995347899867553744494456178507","date":"2025-09-25T20:17:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"148149073826593378055498741969620757783","date":"2025-09-24T11:59:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"248722444240297408167495371524306052656","date":"2025-09-24T04:49:29+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-24T01:30:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"86741858797260515123993795114491420148","date":"2025-09-24T00:04:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"326468176260977309130216719204547830053","date":"2025-09-23T12:08:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"89981222699817864536313749653733757700","date":"2025-09-22T00:22:44+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-21T23:56:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-19T07:31:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-06T02:41:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Journal of Trauma and Emergency Surgery","date":"2025-09-02T11:44:55+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-trauma-and-emergency-surgery","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejot","sideBox":"Learn more about [European Journal of Trauma and Emergency Surgery](http://link.springer.com/journal/68)","snPcode":"68","submissionUrl":"https://submission.nature.com/new-submission/68/3","title":"European Journal of Trauma and Emergency Surgery","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"1ab33c5d-3439-4751-a046-ee8d3fa3d8e4","owner":[],"postedDate":"October 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-01T16:00:02+00:00","versionOfRecord":{"articleIdentity":"rs-7517406","link":"https://doi.org/10.1007/s00068-025-03032-0","journal":{"identity":"european-journal-of-trauma-and-emergency-surgery","isVorOnly":false,"title":"European Journal of Trauma and Emergency Surgery"},"publishedOn":"2025-11-25 15:56:51","publishedOnDateReadable":"November 25th, 2025"},"versionCreatedAt":"2025-10-03 16:20:44","video":"","vorDoi":"10.1007/s00068-025-03032-0","vorDoiUrl":"https://doi.org/10.1007/s00068-025-03032-0","workflowStages":[]},"version":"v1","identity":"rs-7517406","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7517406","identity":"rs-7517406","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}