The aim of this study was to assess the occurrence of post-concussion symptoms and post-concussion syndrome (PCS) in a large cohort of patients after complicated and uncomplicated mild traumatic brain injury (mTBI) at three and six months post-injury. Patients were included through the prospective cohort study: Collaborative European NeuroTrauma Effectiveness Research (CENTER-TBI). Patients enrolled with mTBI (Glasgow Coma Scale 13–15) were further differentiated into complicated and uncomplicated mTBI based on the presence or absence of computed tomography abnormalities, respectively. The Rivermead Post-Concussion Symptoms Questionnaire (RPQ) assessed post-concussion symptoms and PCS according to the mapped ICD-10 classification method. The occurrence of post-concussion symptoms and syndrome at both time points was calculated. Chi square tests were used to test for differences between and within groups. Logistic regression was performed to analyse the association between complicated versus uncomplicated mTBI and the prevalence of PCS. Patients after complicated mTBI reported slightly more post-concussion symptoms compared to those after uncomplicated mTBI. A higher percentage of patients after complicated mTBI were classified as having PCS at three (complicated: 46% vs. uncomplicated: 35%) and six months (complicated: 43% vs. uncomplicated 34%). After adjusting for baseline covariates, the effect of complicated versus uncomplicated mTBI at three months appeared minimal: odds ratio 1.25 (95% confidence interval: 0.95–1.66). Although patients after complicated mTBI report slightly more post-concussion symptoms and show higher PCS rates compared to those after uncomplicated mTBI at three and six months, complicated mTBI was only found a weak indicator for these problems.

A prominent outcome measure within burn care is health related quality of life (HRQL). Until now, no model for long-term recovery of HRQL exists for adult burn patients which requires large samples with repeated measurements. Re-use and the combination of existing data is a way to achieve larger data samples that enable the estimation of long-term recovery models. The aim of this secondary data analysis was to assess the recovery of HRQL after a burn injury over time. Data from ten European studies on generic HRQL assessed in adult burn patients (either with the EQ-5D or SF-36) from five different countries were merged into one dataset. SF-36 outcomes were transformed into EQ-5D outcomes. A 24-month recovery of HRQL (EQ-5D utility) was modeled using a linear mixed-effects model and adjusted for important patient and burn characteristics. Subgroups of patients with mild and intermediate burns (≤20% total body surface area (TBSA) burned) and with major burns (>20% TBSA burned) were compared. The combined database included 1687 patients with a mean age of 43 (SD 15) years and a median %TBSA burned of 9% (IQR 4–18). There was large improvement in HRQL up to six months after burns, and HRQL remained relatively stable afterwards (studied up to 24 months post burn). However, the estimated EQ-5D utility scores remained below the norm scores of the general population. In this large sample, females, patients with a long hospital stay and patients with major burns had a delayed and worse recovery. The proportion of patients that reported problems for the EQ-5D dimensions ranged from 100% (pain/discomfort at baseline in patients with major burns) to 10% (self-care ≥3 months after injury in patients with mild and intermediate burns). After 24 months, both subgroups of burn patients did not reach the level of the general population in the dimensions pain/discomfort and anxiety/depression, and patients with major burns in the dimension usual activities. A main limitation of the study includes that the variables in the model were limited to age, gender, %TBSA, LOS and time since burn as these were the only variables available in all datasets. The 24-month recovery model can be used in clinical practice to inform patients on expected HRQL outcomes and provide clinicians insights into the expected recovery of HRQL. In this way, a delayed recovery can be recognized in an early stage and timely interventions can be started in order to improve patient outcomes. However, external validation of the developed model is needed before implementation into clinical practice. Furthermore, our study showed the benefit of secondary data usage within the field of burns.

Prehospital care for traumatic brain injury (TBI) is important to prevent secondary brain injury. We aim to compare prehospital care systems within Europe and investigate the association of system characteristics with the stability of patients at hospital arrival. We studied TBI patients who were transported to CENTER-TBI centers, a pan-European, prospective TBI cohort study, by emergency medical services between 2014 and 2017. The association of demographic factors, injury severity, situational factors, and interventions associated with on-scene time was assessed using linear regression. We used mixed effects models to investigate the case mix adjusted variation between countries in prehospital times and interventions. The case mix adjusted impact of on-scene time and interventions on hypoxia (oxygen saturation <90%) and hypotension (systolic blood pressure <100mmHg) at hospital arrival was analysed with logistic regression. Among 3878 patients, the greatest driver of longer on-scene time was intubation (+8.3 min, 95% CI: 5.6 – 11.1). Secondary referral was associated with shorter on-scene time (-5.0 min 95% CI: -6.2- -3.8). Between countries, there was a large variation in response (range: 12-25 min), on-scene (range: 16-36 min) and travel time (range: 15-32 min) and in prehospital interventions. These variations were not explained by patient factors such as conscious level or severity of injury (expected OR between countries: 1.8 for intubation, 1.8 for IV fluids, 2.0 for helicopter). On-scene time was not associated with the regional EMS policy (p = 0.58). Hypotension and/or hypoxia were seen in 180 (6%) and 97 (3%) patients in the overall cohort and in 13% and 7% of patients with severe TBI (GCS ≤8). The largest association with secondary insults at hospital arrival was with major extracranial injury: the OR was 3.6 (95% CI: 2.6– 5.0) for hypotension and 4.4 (95% CI: 2.9 – 6.7) for hypoxia. To conclude, hypoxia and hypotension continue to occur in patients who suffer a TBI, and remain relatively common in severe TBI. Substantial variation in prehospital care exists for patients after TBI in Europe, which is only partially explained by patient factors.

Background Patients with major trauma might benefit from treatment in a trauma centre, but early identification of major trauma (Injury Severity Score (ISS) over 15) remains difficult. The aim of this study was to undertake an external validation of existing prognostic models for injured patients to assess their ability to predict mortality and major trauma in the prehospital setting. Methods Prognostic models were identified through a systematic literature search up to October 2017. Injured patients transported by Emergency Medical Services to an English hospital from the Trauma Audit and Research Network between 2013 and 2016 were included. Outcome measures were major trauma (ISS over 15) and in‐hospital mortality. The performance of the models was assessed in terms of discrimination (concordance index, C‐statistic) and net benefit to assess the clinical usefulness. Results A total of 154 476 patients were included to validate six previously proposed prediction models. Discriminative ability ranged from a C‐statistic value of 0·602 (95 per cent c.i. 0·596 to 0·608) for the Mechanism, Glasgow Coma Scale, Age and Arterial Pressure model to 0·793 (0·789 to 0·797) for the modified Rapid Emergency Medicine Score (mREMS) in predicting in‐hospital mortality (11 882 patients). Major trauma was identified in 52 818 patients, with discrimination from a C‐statistic value of 0·589 (0·586 to 0·592) for mREMS to 0·735 (0·733 to 0·737) for the Kampala Trauma Score in predicting major trauma. None of the prediction models met acceptable undertriage and overtriage rates. Conclusion Currently available prehospital trauma models perform reasonably in predicting in‐hospital mortality, but are inadequate in identifying patients with major trauma. Future research should focus on which patients would benefit from treatment in a major trauma centre.

BACKGROUND: With implementation of trauma systems, a level of trauma care classification was introduced. Use of such a system has been linked to significant improvements in survival and other outcomes. OBJECTIVES: The aim of this study was assessing the association between level of trauma care and fatal and nonfatal outcome measures for general and major trauma (MT) populations. METHODS: A systematic literature search was conducted using six electronic databases up to December 18, 2019. Studies comparing mortality or nonfatal outcomes between different levels of trauma care in general and MT populations (preferably Injury Severity Score of >15) were included. Two independent reviewers performed selection of relevant studies, data extraction, and a quality assessment of included articles. With a random-effects meta-analysis, adjusted and unadjusted pooled effect sizes were calculated for level I versus non–level I trauma centers. RESULTS: Twenty-two studies were included. Quality of the included studies was good; however, adjustment for comorbidity (32%) and interhospital transfer (38%) was performed less frequently. Nine (60%) of the 15 studies analyzing in-hospital mortality in general trauma populations reported a survival benefit for level I trauma centers. Level I trauma centers were not associated with higher mortality than non–level I trauma centers (adjusted odd ratio, 0.97; 95% confidence interval, 0.61–1.52). Of the 11 studies reporting in-hospital mortality in MT populations, 10 (91%) reported a survival benefit for level I trauma centers. Level I trauma centers were associated with lower mortality than non–level I trauma centers (adjusted odd ratio, 0.77; 95% confidence interval, 0.69–0.87). CONCLUSION: The association between level of trauma care and in-hospital mortality is evident for MT populations; however, this does not hold for general trauma populations. Level I trauma centers produce improved survival in MT populations. This association could not be proven for nonfatal outcomes in general and MT populations because of inconsistencies in the body of evidence. LEVEL OF EVIDENCE: Systematic review and meta-analysis, level III.

In medical research, missing data is common. In acute diseases, such as traumatic brain injury (TBI), even well-conducted prospective studies may suffer from missing data in baseline characteristics and outcomes. Statistical models may simply drop patients with any missing values, potentially leaving a selected subset of the original cohort. Imputation is widely accepted by methodologists as an appropriate way to deal with missing data. We aim to provide practical guidance on handling missing data for prediction modeling. We hereto propose a five-step approach, centered around single and multiple imputation: 1) explore the missing data patterns; 2) choose a method of imputation; 3) perform imputation; 4) assess diagnostics of the imputation; and 5) analyze the imputed data sets. We illustrate these five steps with the estimation and validation of the IMPACT (International Mission on Prognosis and Analysis of Clinical Trials in Traumatic Brain Injury) prognostic model in 1375 patients from the CENTER-TBI database, included in 53 centers across 17 countries, with moderate or severe TBI in the prospective European CENTER-TBI study. Future prediction modeling studies in acute diseases may benefit from following the suggested five steps for optimal statistical analysis and interpretation, after maximal effort has been made to minimize missing data.