Nils Oddvar Skaga

Purpose: This study explores the protective capabilities of bicycle helmets on serious head injury among bicyclists hospitalized in a Norwegian level 1 trauma centre. Method: Information on helmet use, demographic variables, Abbreviated Injury Scale (AIS) and surgical procedure codes was retrieved from the Oslo University Hospital Trauma Registry for patients with bicycle-related injuries from 2005 through 2016. Outcomes were serious head injury defined as maximum AIS severity score ≥ 3 in the AIS region Head, any cranial neurosurgical procedure, and 30-day mortality. Results: A total of 1256 hospitalized bicyclists were included. The median age was 41 years (quartiles 26–53), 73% were male, 5.3% had severe pre-injury comorbidities, and 54% wore a helmet at the time of injury. Serious head injury occurred in 30%, 9% underwent a cranial neurosurgical procedure, and 30-day mortality was 2%. Compared to non-helmeted bicyclists, helmeted bicyclists were older (43 years, quartiles 27–54, vs. 38 years, quartiles 23–53, p = 0.05), less often crashed during night-time (21% vs. 38%, p < 0.001), less frequently had serious head injury (22% vs. 38%, OR 0.29, 95% CI 0.22–0.39), and less often underwent cranial neurosurgery (6% vs. 14%, OR 0.36, 95% CI 0.24–0.54). No statistically significant difference in 30-day mortality between the two groups was found (1.5% vs. 2.9%, OR 0.50, 95% CI 0.22–1.11). Conclusion: Helmet use was associated with fewer and less severe head injuries and fewer neurosurgical procedures. This adds evidence to the protective capabilities of bicycle helmets.

Purpose: To compare the types of facial fractures and their treatment in bicyclists admitted to a level 1 trauma centre with major and minor-moderate head injury. Methods: Retrospective analysis of data from bicycle-related injuries in the period 2005–2016 extracted from the Oslo University Hospital trauma registry. Results: A total of 967 bicyclists with head injuries classified according to the Abbreviated Injury Scale (AIS) were included. The group suffering minor-moderate head injury (AIS Head 1–2) included 518 bicyclists, while 449 bicyclists had major head injury (AIS Head 3–6). The mean patient age was 40.2 years (range 3–91 years) and 701 patients (72%) were men. A total of 521 facial fractures were registered in 262 patients (on average 2 facial fractures per bicyclist). Bicyclists with major head injury exhibited increased odds for facial fractures compared to bicyclists with minor-moderate head injury (sex and age adjusted odds ratio (OR) 2.75, 95% confidence interval (CI) 2.03–3.72, p < 0.001. More specifically, there was increased odds for all midface fractures, but no difference for mandible fractures. There was also increased odds for orbital reconstruction in cyclist with major head injury compared to bicyclist with minor-moderate head injury (adjusted OR 3.34, 95% CI 1.30–8.60, p = 0.012). Conclusion: Bicyclists with more severe head injuries had increased odds for midface fractures and surgical correction of orbital fractures. During trauma triage, the head and the face should be considered as one unit.

Introduction: Anatomic injury, physiological derangement, age, injury mechanism and pre‐injury comorbidity are well‐founded predictors of trauma outcome. Statistical prediction models may have poorer discrimination, calibration and accuracy when applied in new locations. We aimed to compare the TRISS, TARN and NORMIT survival prediction models in a Norwegian trauma population.Methods: Consecutive patients admitted to Oslo University Hospital Ullevål within 24 h after injury, with Injury Severity Score ≥ 10, proximal penetrating injuries, or received by trauma team, were studied. Original NORMIT coefficients were updated in a derivation dataset (NORMIT 2; n = 5923; 2005–2009). TRISS, TARN and NORMIT prediction models were evaluated in the validation dataset (n = 6348; 2010–2013) using two different AIS editions for injury coding. Exclusion due to missing data was 0.26%. Outcome was 30‐day mortality. Validation included AUROC, scaled Brier statistics, and calibration plots.Results: The NORMIT models had significantly better discrimination, calibration, and overall fit than the TRISS 09, TARN 09 and TARN 12 models. The updated NORMIT 2 had higher numerical values of AUROC and scaled Brier than the original NORMIT, but with overlapping 95%CI. Overlapping 95%CI for AUROCs and Discrimination slopes indicated that the TARN and TRISS models performed similarly. Calibration plots showed tight and consistent predictions over all Ps strata for NORMIT 2 run on AIS'98 coded data, and only little deterioration when AIS'08 data was substituted.Conclusions: In a Norwegian trauma population, the updated Norwegian survival prediction model in trauma (NORMIT 2) performed better than well‐established British and US alternatives. External validation of these three models in other Nordic populations is warranted.

Penetrating cardiac injuries in Europe have been poorly studied. We present a 10-year outcome for patients with penetrating heart injuries at Oslo University Hospital. Data from 01.01.2001 until 31.12.2010 was collected from the Oslo University Hospital Trauma Registry and from the patients’ records. Thirty-one patients were admitted with a penetrating cardiac injury. Fourteen patients survived (45 %). Four out of 8 patients (50 %) with gunshot wounds survived compared to 10 out of 23 (44 %) with stab wounds. Median (quartiles) for the following values were: Injury Severity Score 25 (21–35), Revised Trauma Score 0 (0–6,9), Probability of Survival 0,015 (0,004–0,956), Glasgow Coma Scale 3 (3–13). Thirteen patients had signs of life on admission and survived. Eighteen patients were admitted without signs of life and received emergency department thoracotomy. Eight of these had no signs of life at the scene of injury and did not survive. Out of the remaining 10 patients, one survived. The outcome of patients with penetrating cardiac injury reaching the emergency department with signs of life was excellent. Hemodynamic instability indicates immediate surgery. Stable patients with penetrating thoracic trauma and possible cardiac injury detected by imaging should be considered for conservative treatment.

Damage control surgery and damage control resuscitation have reduced mortality in patients with severe abdominal injuries. The shift towards non-operative management in haemodynamically stable patients suffering blunt abdominal trauma has further contributed to the improved results. However, in many countries, low volume of trauma cases and limited exposure to trauma laparotomies constitute a threat to trauma competence. The aim of this study was to evaluate the institutional patient volume and performance for patients with abdominal injuries over an eight-year period. Data from 955 consecutive trauma patients admitted in Oslo University Hospital Ulleval with abdominal injuries during the eight-year period 2002-2009 were retrospectively explored. A separate analysis was performed on all trauma patients undergoing laparotomy during the same period, whether abdominal injuries were identified or not. Variable life-adjusted display (VLAD) was used in order to describe risk-adjusted survival trends throughout the period and the patients admitted before (Period 1) and after (Period 2) the institution of a formal Trauma Service (2005) were compared. There was a steady increase in admitted patients with abdominal injuries, while the number of patients undergoing laparotomy was constant exposing the surgical trauma team leaders to an average of 8 trauma laparotomies per year. No increase in missed injuries or failures of non-operative management was detected. Unadjusted mortality rates decreased from period 1 to period 2 for all patients with abdominal injuries as well as for the patients undergoing laparotomy. However, this apparent decrease was not confirmed as significant in TRISS-based analysis of risk-adjusted mortality. VLAD demonstrated a steady performance throughout the study period. Even in a high volume trauma center the exposure to abdominal injuries and trauma laparotomies is limited. Due to increasing NOM, an increasing number of patients with abdominal injuries was not accompanied by an increase in number of laparotomies. However, we have demonstrated a stable performance throughout the study period as visualized by VLAD without an increase in missed injuries or failures of NOM.

Introduction: Anatomic injury, physiological derangement, age, and injury mechanism are well‐founded predictors of trauma outcome. We aimed to develop and validate the first Scandinavian survival prediction model for trauma.Methods: Eligible were patients admitted to Oslo University Hospital Ullevål within 24 h after injury with Injury Severity Score ≥ 10, proximal penetrating injuries or received by a trauma team. The derivation dataset comprised 5363 patients (August 2000 to July 2006); the validation dataset comprised 2517 patients (August 2006 to July 2008). Exclusion because of missing data was < 1%. Outcome was 30‐day mortality. Logistic regression analysis incorporated fractional polynomial modelling and interaction effects. Model validation included a calibration plot, Hosmer–Lemeshow test and receiver operating characteristic (ROC) curves.Results: The new survival prediction model included the anatomic New Injury Severity Score (NISS), Triage Revised Trauma Score (T‐RTS, comprising Glascow Coma Scale score, respiratory rate, and systolic blood pressure), age, pre‐injury co‐morbidity scored according to the American Society of Anesthesiologists Physical Status Classification System (ASA‐PS), and an interaction term. Fractional polynomial analysis supported treating NISS and T‐RTS as linear functions and age as cubic. Model discrimination between survivors and non‐survivors was excellent. Area (95% confidence interval) under the ROC curve was 0.966 (0.959–0.972) in the derivation and 0.946 (0.930–0.962) in the validation dataset. Overall, low mortality and skewed survival probability distribution invalidated model calibration using the Hosmer–Lemeshow test.Conclusions: The Norwegian survival prediction model in trauma (NORMIT) is a promising alternative to existing prediction models. External validation of the model in other trauma populations is warranted.

The trauma room at Oslo University Hospital- Ulleval is fully equipped for major damage control procedures, in order to minimize delay to surgery. Since 2006, patients in need of immediate laparotomy have increasingly been transferred to a dedicated trauma operating room (OR). We wanted to determine the decrease in number of procedures performed in the emergency department (ED), the effect on time from admission to laparotomy, the effect on non-therapeutic laparotomies, and finally to determine whether such a change could be undertaken without an increase in mortality.

Background: The BIG score (Admission base deficit (B), International normalized ratio (I), andGlasgow Coma Scale (G)) has been shown to predict mortality on admission inpediatric trauma patients. The objective of this study was to assess itsperformance in predicting mortality in an adult trauma population, and to compareit with the existing Trauma and Injury Severity Score (TRISS) and probability ofsurvival (PS09) score. Materials and methods: A retrospective analysis using data collected between 2005 and 2010 from seventrauma centers and registries in Europe and the United States of America wasperformed. We compared the BIG score with TRISS and PS09 scores in a population ofblunt and penetrating trauma patients. We then assessed the discrimination abilityof all scores via receiver operating characteristic (ROC) curves and compared theexpected mortality rate (precision) of all scores with the observed mortalityrate. Results: In total, 12,206 datasets were retrieved to validate the BIG score. The mean ISSwas 15 ± 11, and the mean 30-day mortality rate was 4.8%. With an AUROC of0.892 (95% confidence interval (CI): 0.879 to 0.906), the BIG score performed wellin an adult population. TRISS had an area under ROC (AUROC) of 0.922 (0.913 to0.932) and the PS09 score of 0.825 (0.915 to 0.934). On a penetrating-traumapopulation, the BIG score had an AUROC result of 0.920 (0.898 to 0.942) comparedwith the PS09 score (AUROC of 0.921; 0.902 to 0.939) and TRISS (0.929; 0.912 to0.947). Conclusions: The BIG score is a good predictor of mortality in the adult trauma population. Itperformed well compared with TRISS and the PS09 score, although it hassignificantly less discriminative ability. In a penetrating-trauma population, theBIG score performed better than in a population with blunt trauma. The BIG scorehas the advantage of being available shortly after admission and may be used topredict clinical prognosis or as a research tool to risk stratify trauma patientsinto clinical trials.