Have changes in computerised tomography guidance positively impacted detection of cervical spine injury in children? A review of the Trauma Audit and Research Network data

Background Clinically significant damage to the cervical spine in children is uncommon, but missing this can be life-changing for patients. The balance between rarity and severity leads to inconsistent scanning, with both resource and radiation implications. In 2014, the United Kingdom’s National Institute for Health and Care Excellence updated their computerised tomography neck imaging guidance in children. The aim of this study was to assess if the change in guidance had resulted in a change in diagnosis or imaging rates. Methods A retrospective review of the national Trauma Audit and Research Network’s data for computerised tomography spine imaging in children in 2012–2013 was compared to the same data sample collected in 2015–2016. Results The percentage of children presenting with neck trauma who were imaged reduced from 15.5 to 14.1% with an increase in confirmed cervical spine injury from 1.6 to 2.3% between the two time periods. The specificity of computerised tomography scanning increased from 10 to 16.4%. There was variation in scan rates, with major trauma centres scanning a greater percentage of children of all ages and with all injury scores, than trauma units. Discussion This study suggests national guidance can impact clinical care in a relatively short timeframe. Variation in how guidance is applied, with major trauma centres scanning proportionately more children with a lower yield, could be because scanning is more readily available, or because trauma protocols encourage more scans. Twenty per cent of injuries were not found on the initial computerised tomography, in keeping with previously reported data, because the injuries were ligamentous or cord contusion. This suggests a role for early magnetic resonance imaging in children with suspected spinal injury

Age and the distribution of major injury across a national trauma system

Background Trauma places a significant burden on healthcare services, and its management impacts greatly on the injured patient. The demographic of major trauma is changing as the population ages, increasingly unveiling gaps in processes of managing older patients. Key to improving patient care is the ability to characterise current patient distribution. Objectives There is no contemporary evidence available to characterise how age impacts on trauma patient distribution at a national level. Through an analysis of the Trauma Audit Research Network (TARN) database, we describe the nature of Major Trauma in England since the configuration of regional trauma networks, with focus on injury distribution, ultimate treating institution and any transfer in-between. Methods The TARN database was analysed for all patients presenting from April 2012 to the end of October 2017 in NHS England. Results About 307,307 patients were included, of which 63.8% presented directly to a non-specialist hospital (trauma unit (TU)). Fall from standing height in older patients, presenting and largely remaining in TUs, dominates the English trauma caseload. Contrary to perception, major trauma patients currently are being cared for in both specialist (major trauma centres (MTCs)) and non-specialist (TU) hospitals. Paediatric trauma accounts for <5% of trauma cases and is focussed on paediatric MTCs. Conclusions Within adult major trauma patients in England, mechanism of injury is dominated by low level falls, particularly in older people. These patients are predominately cared for in TUs. This work illustrates the reality of current care pathways for major trauma patients in England in the recently configured regional trauma networks

Cross-validation of two prognostic trauma scores in severely injured patients

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

Older People Are Not All The Same: Lessons From A Major Trauma Database

Objectives & Background While there is extensive research on the differences between older and younger patients with serious injuries, little is known about variations within the older age group. However, increased frailty over the age of 85 suggests that these ‘oldest old’ patients are likely to be significantly different to younger seniors. Methods The aim of this study was do determine whether the demographic, premorbid and injury characteristics of older patients (aged ≥65) varied with age. A cross-sectional study of of patients from the Trauma Audit and Research Network (TARN) admitted between June 2013 and May 2015 was undertaken, comparing those aged 65 to 74; 75–84 and ≥85 years old. Demographic, premorbid and injury characteristics were compared using Chi-squared analysis, while multiple logistic regression was used to calculate risk adjusted mortality, utilising the PS14 TARN predictive model. Results 51,491 patients on the TARN database were eligible for inclusion. Of these, 18,664 (36.3%) were≥85 years; 19,157 (37.2%) 75–84 years and 13670 (26.5%) 65–74 years. Patients ≥85 years were significantly more likely to be female (68.8% vs 46.6% aged 65–74 years, p<0.001) and suffer low level falls (89.0% vs 63.0% aged 65–74 years, p<0.001). These patients were also more likely to have multiple comorbidities, with a median Charlson comorbidity score of 4 (IQR 0–5) compared to a median CCI of 0 (IQR 0–4) in patients aged 65–74 years. Despite having the lowest median injury severity scores, patients aged ≥85 years had significantly higher crude mortality rates (12.9% vs 5.9% in patients aged 65–74 years). Risk adjusted mortality was also highest in patients ≥85 years, with an adjusted odds ratio of 4.55 (95% CI 3.87–5.35) compared to patients aged 65–74 years. Conclusion There are significant variations in the demographic, comorbid and injury characteristics between different age groups of older trauma patients, which are associated with marked differences in crude and risk adjusted mortality. The most senior (over 85) were the most likely to sustain major trauma and the least likely to survive

Helicopter and ground emergency medical services transportation to hospital after major trauma in England: a comparative cohort study

Background: The utilization of helicopter emergency medical services (HEMS) in modern trauma systems has been a source of debate for many years. This study set to establish the true impact of HEMS in England on survival for patients with major trauma. Methods: A comparative cohort design using prospectively recorded data from the UK Trauma Audit and Research Network registry. 279 107 patients were identified between January 2012 and March 2017. The primary outcome measure was risk adjusted in-hospital mortality within propensity score matched cohorts using logistic regression analysis. Subset analyses were performed for subjects with prehospital Glasgow Coma Scale 29 and systolic blood pressure <90. Results: The analysis was based on 61 733 adult patients directly admitted to major trauma centers: 54 185 ground emergency medical services (GEMS) and 7548 HEMS. HEMS patients were more likely male, younger, more severely injured, more likely to be victims of road traffic collisions and intubated at scene. Crude mortality was higher for HEMS patients. Logistic regression demonstrated a 15% reduction in the risk adjusted odds of death (OR=0.846; 95% CI 0.684 to 1.046) in favor of HEMS. When analyzed for patients previously noted to benefit most from HEMS, the odds of death were reduced further but remained statistically consistent with no effect. Sensitivity analysis on 5685 patients attended by a doctor on scene but transported by GEMS demonstrated a protective effect on mortality versus the standard GEMS response (OR 0.77; 95% CI 0.62 to 0.95). Discussion: This prospective, level 3 cohort analysis demonstrates a non-significant survival advantage for patients transported by HEMS versus GEMS. Despite the large size of the cohort, the intrinsic mismatch in patient demographics limits the ability to statistically assess HEMS true benefit. It does, however, demonstrate an improved survival for patients attended by doctors on scene in addition to the GEMS response. Improvements in prehospital data and increased trauma unit reporting are required to accurately assess HEMS clinical and cost-effectiveness

Predicting need for hospital admission in patients with traumatic brain injury or skull fractures identified on CT imaging : a machine learning approach

Background: Patients with mild traumatic brain injury on CT scan are routinely admitted for inpatient observation. Only a small proportion of patients require clinical intervention. We recently developed a decision rule using traditional statistical techniques that found neurologically intact patients with isolated simple skull fractures or single bleeds <5 mm with no preinjury antiplatelet or anticoagulant use may be safely discharged from the emergency department. The decision rule achieved a sensitivity of 99.5% (95% CI 98.1% to 99.9%) and specificity of 7.4% (95% CI 6.0% to 9.1%) to clinical deterioration. We aimed to transparently report a machine learning approach to assess if predictive accuracy could be improved. Methods: We used data from the same retrospective cohort of 1699 initial Glasgow Coma Scale (GCS) 13–15 patients with injuries identified by CT who presented to three English Major Trauma Centres between 2010 and 2017 as in our original study. We assessed the ability of machine learning to predict the same composite outcome measure of deterioration (indicating need for hospital admission). Predictive models were built using gradient boosted decision trees which consisted of an ensemble of decision trees to optimise model performance. Results: The final algorithm reported a mean positive predictive value of 29%, mean negative predictive value of 94%, mean area under the curve (C-statistic) of 0.75, mean sensitivity of 99% and mean specificity of 7%. As with logistic regression, GCS, severity and number of brain injuries were found to be important predictors of deterioration. Conclusion: We found no clear advantages over the traditional prediction methods, although the models were, effectively, developed using a smaller data set, due to the need to divide it into training, calibration and validation sets. Future research should focus on developing models that provide clear advantages over existing classical techniques in predicting outcomes in this population

Letter in Response to 'Classification of Traumatic Brain Injury Severity Using Informed Data Reduction in a Series of Binary Classifier Algorithms'

This paper is a comment to the paper "Classification of Traumatic Brain Injury Severity Using Informed Data Reduction in a Series of Binary Classifier Algorithms" by Bloom et al. The authors inform the editors that they have conducted a similar study on behalf of, and funded BrainScope Company, Inc. and that the method used to acquire the QEEG data suffer from a technical fault. Same methods seem to have been used by Bloom et. al. It is noted that the authors do not refer to this technical fault even though it arose months before the publication of the papers

Implementing the National Institute for Health and Clinical Excellence Head Injury 2014 Guidelines in a major children’s hospital emergency department

Objectives and background Head injury is a common paediatric emergency department presentation. The National Institute for Health and Clinical Excellence updated its guidance in January 2014 regarding imaging required for adults and children following a head injury (CG176). This study looked at the rates of computed tomography (CT) head scans performed and adherence rates to CG176. Patients and methods A single-centre audit was carried out, examining imaging practice in children with head injuries. CG176 was implemented formally in August 2014 to the new trainee doctors. The primary outcome was adherence to CG176. As the data were binary, 95% confidence intervals were used for comparison. Results In all, 1797 patients were identified as having a head injury. Implementation at the Sheffield’s Children NHS Foundation Trust resulted in a statistically significant increase in guideline adherence from 79.2% [95% confidence interval (CI): 76.4–81.9%] to 85.1% (95% CI: 82.9–87.4%). The greatest impact in adherence was found in CT head scans, from 95.8% (95% CI: 94.5–97.2%) to 97.7% (95% CI: 96.7–98.6%). Conclusion The implementation at the Sheffield’s Children NHS Foundation Trust was successful in satisfying the aim of CG176 by increasing adherence and decreasing CT head scans. This success could be explained by the formal implementation to the new cohort of doctors and better physician agreement with the guidelines. The increase in adherence is contrary to the previous studies

What are the functional consequences after TBI? The SHEFBIT cohort experience

Objectives To investigate functional outcome after TBI and identify variables that predict outcome in a multiordinal regression model. Background The results of global outcome studies after Traumatic Brain Injury(TBI) differ widely due to differences in outcome measure, attrition to follow-up and selection bias. Outcome information would inform patients/families, guide service development and target high-risk individuals Subjects/Setting prospective cohort of 1322 admissions with TBI, assessed by face to face interviews at 1 yr. Measures Extended Glasgow Outcome Scale (GOSE) by structured questionnaire. Results At 1 year, outcome was determined in 1207(91.3%). Mean age was 46.9(SD17.3); Almost half(49.2%) had mild injury. At one year, 42.9% achieved Good Recovery but GOSE declined in 11.4% of the cohort compared to 10 weeks including 60(4.9%) deaths. In an ordinal logistic regression, increasing TBI severity, etiology (assault), more prominent CT abnormality, past psychiatric history and alcohol intoxication were independent predictors of worse GOSE. A pseudo-R2 of 0.38 suggested that many unmeasured factors also contribute to TBI outcome. Future work needs to identify other variables that may influence outcome. Conclusions In a large TBI cohort, there is still considerable functional disability at 1 year. It may be possible to target high-risk groups for rehabilitatio