Background Trauma care in England was re-organised in 2012 with ambulance bypass of local hospitals to newly designated Major Trauma Centres (MTCs). There is still controversy about the optimal way to organise health series for patients suffering severe injury. Methods A longitudinal series of annual cross-sectional studies of care process and outcomes from April 2008 to March 2017. Data was collected through the national clinical audit of major trauma care. The primary analysis was carried out on the 110,863 patients admitted to 35 hospitals that were ‘consistent submitters’ throughout the study period. The main outcome was longitudinal analysis of risk adjusted survival. Findings Major Trauma networks were associated with significant changes in (1) patient flow (with increased numbers treated in Major Trauma Centres), (2) treatment systems (more consultant led care and more rapid imaging), (3) patient factors (an increase in older trauma), and (4) clinical care (new massive transfusion policies and use of tranexamic acid). There were 10,247 (9.2%) deaths in the 110,863 patients with an ISS of 9 or more. There were no changes in unadjusted mortality. The analysis of trends in risk adjusted survival for study hospitals shows a 19% (95% CI 3%–36%) increase in the case mix adjusted odds of survival from severe injury over the 9-year study period. Interrupted time series analysis showed a significant positive change in the slope after the intervention time point of April 2012 (+ 0.08% excess survivors per quarter, p = 0.023), in other words an increase of 0.08 more survivors per 100 patients every quarter. Interpretation A whole system national change was associated with significant improvements in both the care process and outcomes of patients after severe injury. Funding This analysis was carried out independently and did not receive funding. The data collection for the national clinical audit was funded by subscriptions from participating hospitals.

Introduction Trauma scoring systems are important tools for outcome prediction and severity adjustment that informs trauma quality assessment and research. Discrimination and precision of such systems is tested in validation studies. The German TraumaRegister DGU ® (TR-DGU) and the Trauma Audit and Research Network (TARN) from the UK agreed on a cross-validation study to validate their prediction scores (RISC II and PS14, respectively). Methods Severe trauma patients with an Injury Severity Score (ISS) ≥ 9 documented in 2015 and 2016 were selected in both registries (primary admissions only). The predictive scores from each registry were applied to the selected data sets. Observed and predicted mortality were compared to assess precision; area under the receiver operating characteristic curve was used for discrimination. Hosmer-Lemeshow statistic was calculated for calibration. A subgroup analysis including patients treated in intensive care unit (ICU) was also carried out. Results From TR-DGU, 40,638 patients were included (mortality 11.7%). The RISC II predicted mortality was 11.2%, while PS14 predicted 16.9% mortality. From TARN, 64,622 patients were included (mortality 9.7%). PS14 predicted 10.6% mortality, while RISC II predicted 17.7%. Despite the identical cutoff of ISS ≥ 9, patient groups from both registries showed considerable difference in need for intensive care (88% versus 18%). Subgroup analysis of patients treated on ICU showed nearly identical values for observed and predicted mortality using RISC II. Discussion Each score performed well within its respective registry, but when applied to the other registry a decrease in performance was observed. Part of this loss of performance could be explained by different development data sets: the RISC II is mainly based on patients treated in an ICU, while the PS14 includes cases mainly cared for outside ICU with more moderate injury severity. This is according to the respective inclusion criteria of the two registries. Conclusion External validations of prediction models between registries are needed, but may show that prediction models are not fully transferable to other health-care settings.

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