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BackgroundAlthough mortality rates following major trauma are continuing to decline, a growing number of patients are experiencing long-term disability. The aim of this study was to identify factors associated with health status in the first year following trauma and develop prediction models based on a defined trauma population.MethodsThe Brabant Injury Outcome Surveillance (BIOS) study was a multicentre prospective observational cohort study. Adult patients with traumatic injury were included from August 2015 to November 2016 if admitted to one of the hospitals of the Noord-Brabant region in the Netherlands. Outcome measures were EuroQol Five Dimensions 5D-3L (EQ-5D™ utility and visual analogue scale (VAS)) and Health Utilities Index (HUI) 2 and 3 scores 1 week and 1, 3, 6 and 12 months after injury. Prediction models were developed using linear mixed models, with patient characteristics, preinjury health status, injury severity and frailty as possible predictors. Predictors that were significant (P < 0·050) for one of the outcome measures were included in all models. Performance was assessed using explained variance (R2).ResultsIn total, 4883 patients participated in the BIOS study (50·0 per cent of the total), of whom 3366 completed the preinjury questionnaires. Preinjury health status and frailty were the strongest predictors of health status during follow-up. Age, sex, educational level, severe head or face injury, severe torso injury, injury severity, Functional Capacity Index score, co-morbidity and duration of hospital stay were also relevant in the multivariable models predicting health status. R2 ranged from 35 per cent for EQ-VAS to 48 per cent for HUI 3.ConclusionThe most important predictors of health status in the first year after trauma in this population appeared to be preinjury health status and frailty.

Background Because the death rate among the total trauma population is low, other performance indicators in addition to the classical dependent variable mortality are required to assess the overall quality of trauma care. The aim of this study was to develop and validate a prediction model for the occurrence of complications that can be used to adjust a measure of quality of trauma care for case mix. Methods Complications recorded in a trauma registry between 1997 and 2008 were analysed. Formulas for different types of complication (institution- or diagnosis-related) derived from logistic regression models were used to calculate the probability of absence of complications (PAC). Discriminative power was tested by calculating the area under the receiver operating characteristic curve (AUC) in test and validation samples. Calibration was tested using Hosmer and Lemeshow methodology. Results Some 5944 surgical trauma admissions were included in the analysis. A significant association between both institution- and diagnosis-related complications and Injury Severity Score was found. Diagnosis-related complications were also associated with Glasgow Coma Score and age. The AUCs of the PACs for institution- and diagnosis-related complications were 0·64 and 0·75 respectively in the test sample, and 0·66 and 0·76 in the validation sample. The AUCs increased when the outcomes of the models were divided into subcategories of complications. Hosmer and Lemeshow tests were not significant for all models, except that for institutional complications. Conclusion To predict complications, a distinction should be made between institution- and diagnosis-related complications. The development of more detailed diagnosis-related prediction models is preferable because of better performance. The formulas predicting the PAC can be used to compare expected and observed complications.

Background There is growing demand for a simple accurate scoring model to evaluate the quality of trauma care. This study compared different trauma survival prediction models with regard to their performance in different trauma populations. Methods The probability of survival for 10 777 trauma patients admitted to hospital was calculated using the formulas of the following models: the Major Trauma Outcome Study (MTOS), the Trauma Audit and Research Network (TARN) and the Base Excess Injury Severity Scale (BISS). Updated coefficients were calculated by logistic regression analysis based on a Dutch data set. Different models were compared for several subsets of patients, according to age and injury type and severity, using the area under the receiver operating characteristic (ROC) curve (AUC). Calibration for the updated models was presented graphically. Results Most of the models had an AUC exceeding 0·8. For the total population, the TARN Ps07 model with updated coefficients had the highest AUC (0·924); for the subset of patients in whom all parameters were available, the BISS model including the Glasgow Coma Scale had the highest AUC (0·909). All of the models had high discriminative power for patients aged less than 55 years. However, in older or intubated patients and in those with severe head injuries the discriminative power of the models dropped. The TARN model showed the best accuracy. Conclusion The investigated models predict mortality fairly accurately in a Dutch trauma population. However, the accuracy of the models depends greatly on the patients included. Severe head injuries and greater age are likely to lead to a decrease in the accuracy of survival prediction.