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Background: New Injury Severity Score (NISS) and Glasgow Coma Scale, Age and Pressure (GAP) scoring systems have cutoffs to define severe injury and identify high-risk patients. This is important in trauma quality monitoring and improvement. The overall aim was to explore if GAP scoring system can be a complement or an alternative to the traditional NISS scoring system., Methods: Adults exposed to trauma between 2017 and 2021 were included in the study, using data from The Swedish Trauma Registry. The performance of NISS and GAP scores in predicting mortality, and ICU admissions were assessed using the area under the receiver operator characteristics (AUROC) in all patients and in subgroups (blunt, penetrating trauma and older (≥65 years) trauma patients). Patients were classified as severely injured by NISS >15 as Severely Injured NISS (SIN) or with a high-risk for mortality, by GAP 3-18 as High Risk GAP (HRG). Undertriage was calculated based on the cutoffs HRG and SIN., Results: Overall, 37,017 patients were included. The AUROC (95 % CI) for mortality using NISS was 0.84 (0.83-0.85) and for GAP 0.92 (0.91-0.93) (p-value <0.001), the AUROC (95 % CI) for ICU-admissions was 0.82 (0.82-0.83) using NISS and for GAP 0.70 (0.70-0.71) p-value <0.001, in the overall cohort. In older patients the AUROC (95 % CI) for mortality was 0.76 (0.75-0.78) using NISS and 0.79 (0.78-0.81) using GAP, p-value <0.001. Overall, 8,572 (23.2 %) and 2,908 (7.9 %) were classified as SIN and HRG, respectively, with mortality rates of 13.7 % and 34.3 %. In the HRG group low-energy falls dominated and in the SIN group most patients were exposed to MVCs. In the SIN and HRG groups the rate of Emergency Trauma Interventions according to Utstein guidelines (ETIU) and ICU admission was 14.0 vs 9.5 % and 47.0 vs 62.5 % respectively., Conclusion: Our findings suggest that the GAP score and its cutoff 3-18 can be used to define severe trauma as complement to NISS >15 and can be a valuable tool in trauma quality monitoring and improvement. However, both scoring systems were less accurate in predicting mortality for the older trauma patients and should be explored further., Competing Interests: Declaration of competing interest None., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Background: Mortality as an endpoint has been the focus of trauma research whereas few studies investigate long-term outcomes in terms of morbidity. An adequate analysis of post-injury morbidity includes several dimensions, for this reason sick leave has been used as a proxy for morbidity in the current study. The aim of this retrospective matched observational cohort study was to investigate sick leave before and after trauma and factors associated with prolonged sick leave., Methods: Patients from a level one trauma centre 2005-2010 were matched in a 1:5 ratio with uninjured controls. By linkage to national registries, sick leave rates were compared. The association between potential risk factors and full-time sick leave at twelve months post injury, the primary end-point, was examined in trauma patients by logistic regression., Results: Four thousand seven hundred twelve patients and 25,013 controls aged 20-63 were included. Trauma patients had more sick leave both before and after trauma. Age, psychiatric disease, low level of education, serious injury, spinal injury, reduced consciousness at admission, discharge destination other than home, and hospital length of stay >7 days were all associated with the primary end-point. The strongest risk factor was sick leave before trauma; this was also noted in the most seriously injured patients., Discussion: In this retrospective matched observational cohort study we found a significant long-term morbidity, measured as sick leave, among trauma patients. Compared to controls the difference was maximal early after trauma and sustained throughout the follow up period. In the logistic regression, factors associated with the traumatic injury as well as host factors increased the probability of not returning to work. Full sick leavemonth twelve post injury was strongly associated with pre-injury sick leave but also with age, psychiatric comorbidity, level of education, injury severity, spinal injury, low GCS at admission, length of stay at hospital and discharge to other destination than home., Conclusions: Trauma patients suffer from significant long-term morbidity. The sustained post-trauma morbidity is largely influenced by factors not related to injury per se. These insights enable identification of patients at risk for prolonged sick leave after trauma.

Background: Assessment of trauma-system performance is important for improving the care of injured patients. The aim of the study was to compare risk-adjusted survival in two Scandinavian Level-I trauma centres., Methods: This was an observational, retrospective study of prospectively-collected trauma registry data for patients >14 years from Karolinska University Hospital - Solna (KUH), Sweden, and Oslo University Hospital - Ullevål (OUH), Norway, from 2009-2011. Probability of survival (Ps) was calculated according to the Trauma and Injury Severity Score (TRISS) method. Risk-adjusted survival per patient was calculated by assigning every patient a value corresponding to gained or lost fractional life: Each survivor contributed a reward of 1-Ps and each death a penalty of -Ps. The sum of penalties and rewards, corresponding to the difference between expected and actual mortality, was compared between the centres. We present the data as excess survivors per 100 trauma patients., Results: There were 4485 admissions at KUH and 3591 at OUH. The proportion of severely injured patients was higher at OUH compared with KUH (Injury Severity Score [ISS] >15: 33.9 % vs. 21.1 %, p <0.001). OUH had a larger proportion of patients >65 years (16.0 % vs. 13.4 %, p <0.001) and greater comorbidity (ASA-PS ≥3: 14.6 % vs. 6.9 %, p <0.001) compared with KUH. The frequency of helicopter transport and presence of prehospital physicians was higher at OUH compared with KUH (27.6 % vs. 15.5 % and 30.5 % vs. 3.7 %, both p <0.001). Secondary admissions were 5.2-fold more common at OUH compared with KUH (p <0.001). There were no differences in 30-day mortality for severely injured patients (ISS >15). Risk-adjusted survival rate was higher at OUH than at KUH for primary (0.59 vs. 0.51) but lower for secondary (1.41 vs. 2.85) admissions (both p <0.001)., Conclusion: Adjustments for age as a continuous variable and comorbidity should be made when comparing risk-adjusted survival between hospitals, but this is not possible with the TRISS model. A survival prediction model that takes this into account may be a better choice for Scandinavian trauma populations. The current study could not rule out the influence of the system differences between the centres on risk-adjusted survival.

Background: Securing high-quality mortality statistics requires systematic evaluation of all trauma deaths. We examined the proportion of trauma patients dying within 30 days from causes not related to the injury and the impact of exclusion of patients dead on arrival on 30-day trauma mortality. We also defined the demographics, injury characteristics, cause of death and time to death in patients admitted to our trauma center who died within 30 days, between 2007-2011., Methods: Demographics, injury characteristics, status alive/dead on arrival, cause of death and time to death of all patients were reviewed. Deaths were analyzed based on injury mechanism (penetrating, blunt trauma and low energy blunt trauma) and cause of death (traumatic brain injury (TBI), hemorrhage, organ dysfunction and other/unknown)., Results: Of the 7422 admissions, 343 deaths were identified of which 36 (10.5%) involved causes not related to the injury. The overall age was 71 years, Injury Severity Score (ISS) 29 and time to death 24 hours (all medians). Fifty-four patients (17.6%) were dead on arrival. Exclusion of patients dead on arrival reduced the overall mortality rate (P < 0.05) and median ISS (P < 0.05) and increased median age (P < 0.01) and time to death (P < 0.001). Injury mechanism was penetrating trauma in 7.5%, blunt trauma in 56.0%, and low energy blunt trauma in 36.5%. TBI accounted for 58.6%; hemorrhage 16.3%, organ dysfunction 15.0%, and other/unknown for 10.1% of the deaths. Patients who died after low energy blunt trauma were older, had lower ISS and longer time to death compared to those who died after penetrating and blunt trauma (all P < 0.01)., Conclusions: Clinical review of all trauma deaths was essential to interpret mortality. Thirty-day trauma mortality included 10.5% deaths not directly related to the injury and the exclusion of patients dead on arrival significantly affected the unadjusted mortality rate, ISS, median age and time to death.