OBJECTIVES:The combination of broad conditional applicability and ease of data collection make some general risk scores an attractive tool for clinical decision making under acute care conditions. To date, general risk scores have demonstrated moderate levels of accuracy for key outcomes, but there are no definitive general scores integrated universally into prehospital care. The objective of our study was to demonstrate a relationship between the Revised Trauma Score (RTS) and prehospital lifesaving interventions (LSI) and downstream hospital mortality among a large, diverse, multi-year cohort of critical care transport patients. We hypothesized that the RTS is associated with mortality and prehospital LSI generally across all conditions, including non-trauma. METHODS:We conducted a retrospective observational study using a pre-established cohort of sequentially enrolled patients from a regional air medical service between the years 2012 and 2021. Pediatric patients, non-transports, and those transported to hospitals outside the regional health system were excluded from the study. Both trauma and non-trauma patients were included in this study. We performed logistic regressions to evaluate the association between RTS and the outcomes of LSI and hospital mortality, while controlling for age, sex, and medical category. Graphs were constructed to plot RTS against prehospital LSI and survival percentage. RESULTS:Our final patient cohort was 62,424 patients. 58.4% of all patients required a prehospital LSI. Non-trauma cases made up 69.7% of the patient population. The Revised Trauma Score was inversely proportional with both prehospital LSI and mortality. The logistic regression model yielded an odds ratio (OR) of 0.55 (95% CI 0.54 - 0.56) for the association between RTS and death. Additionally, when the components of RTS were associated with mortality, they each showed a statistically significant OR. The Revised Trauma Score was also associated with prehospital LSI (OR 0.10; 95% CI 0.03 - 0.33). CONCLUSIONS:In a large helicopter EMS cohort of both trauma and non-trauma patients, the RTS was inversely associated with prehospital LSI and hospital mortality. The generalized utility of RTS demonstrated in our study warrants further investigation of this measure as a broader triage tool.

INTRODUCTION:International data describes a changing pattern to trauma over the last decade, with an increasingly comorbid population presenting challenges to trauma management and resources. In Ireland, resource provision and management of trauma is being transformed to deliver a trauma network, in line with international best practice. Our hospital plays a crucial role within this network and is designated a Trauma Unit with Specialist Services (TUSS) to distinguish it from standard trauma units. METHODS:This study aims to describe the characteristics of patients and injuries and assess trends in mortality rates. It is a retrospective observational study of adult ICU trauma admissions from August 2010 to July 2021. Primary outcome was all-cause mortality at 30-days, 90-days, and 1 year. Secondary outcomes included length of stay, disposition, and complications. Patients were categorised by age, injury severity score (ISS), and mechanism of injury. RESULTS:In all, 709 patients were identified for final analysis. Annual admissions doubled since 2010/11, with a trough of 41 admissions, increasing to peak at 95 admissions in 2017/18. Blunt trauma accounted for 97.6% of cases. Falls <2 m (45.4%) and RTAs (29.2%) were the main mechanisms of injury. Polytrauma comprised 41.9% of admissions. Traumatic brain injury accounted for 30.2% of cases; 18.8% of these patients were transferred to a neurosurgical centre. The majority of patients, 58.1%, were severely injured (ISS ≥ 16). Patients ≥ 65 years of age accounted for 45.7% of admissions, with falls <2 m their primary mechanism of injury. The primary outcome of all-cause mortality reduced with an absolute risk reduction (ARR) of 8.0% (95% CI: -8.37%, 24.36%), 12.9% (95% CI: -4.19%, 29.94%) and 8.2% (95% CI: -9.64%, 26.09%) for 30-day, 90-day and 1-year respectively. Regression analysis demonstrated a significant reduction in mortality for 30-days and 90-days post presentation to hospital (P-values of 0.018, 0.033 and 0.152 for 30-day, 90-day and 1-year respectively). CONCLUSION:The burden of major trauma in our hospital is considerable and increasing over time. Substantial changes in demographics, injury mechanism and mortality were seen, with outcomes improving over time. This is consistent with international data where trauma systems have been adopted.

OBJECTIVES:Timely and accurate prediction of multiple organ dysfunction syndrome (MODS) is essential for the rescue and treatment of trauma patients However, existing methods are invasive, easily affected by artifacts and can be difficult to perform in a pre-hospital setting. We aim to develop prediction models for patients with MODS using only non-invasive parameters. METHOD:In this study, records from 2319 patients were extracted from the Multiparameter Intelligent Monitoring in Intensive Care Ⅲ database (MIMIC Ⅲ), based on the sequential organ failure assessment (SOFA) score. Seven commonly used machine learning (ML) methods were selected and applied to develop a real-time prediction method for MODS based on full parameters (laboratory parameter. drug and non-invasive parameters, 57 parameters in total) and non-invasive parameters only (17 parameters) and compared with four traditional scoring systems. RESULTS:The prediction results using LightGBM (LGBM) and Adaboost based on the full parameter modeling were 0.959 for area under receiver operating characteristic curve (AUC), outperforming four traditional scoring systems. The removal of 40 parameters and retaining of 17 non-invasive parameters decreased the AUC value of LGBM by 0.015, which still outperformed all traditional scoring systems. CONCLUSIONS:A real-time and accurate MODS prediction method was developed in this paper based on non-invasive parameters by comparing the performance of four ML methods, which proved to be superior to the traditional scoring systems. This method can help medical staff to diagnose MODS as soon as possible and can improve the survival rate of patients in a pre-hospital setting.