Bayesian decision trees for predicting survival of patients: a study on the US National Trauma Data Bank

Trauma and Injury Severity Score (TRISS) models have been developed for predicting the survival probability of injured patients the majority of which obtain up to three injuries in six body regions. Practitioners have noted that the accuracy of TRISS predictions is unacceptable for patients with a larger number of injuries. Moreover, the TRISS method is incapable of providing accurate estimates of predictive density of survival, that are required for calculating confidence intervals. In this paper we propose Bayesian in ference for estimating the desired predictive density. The inference is based on decision tree models which split data along explanatory variables, that makes these models interpretable. The proposed method has outperformed the TRISS method in terms of accuracy of prediction on the cases recorded in the US National Trauma Data Bank. The developed method has been made available for evaluation purposes as a stand-alone application

Comparison of two prognostic models in trauma outcome

BACKGROUND: The Trauma Audit and Research Network (TARN) in the UK publicly reports hospital performance in the management of trauma. The TARN risk adjustment model uses a fractional polynomial transformation of the Injury Severity Score (ISS) as the measure of anatomical injury severity. The Trauma Mortality Prediction Model (TMPM) is an alternative to ISS; this study compared the anatomical injury components of the TARN model with the TMPM. METHODS: Data from the National Trauma Data Bank for 2011-2015 were analysed. Probability of death was estimated for the TARN fractional polynomial transformation of ISS and compared with the TMPM. The coefficients for each model were estimated using 80 per cent of the data set, selected randomly. The remaining 20 per cent of the data were used for model validation. TMPM and TARN were compared using calibration curves, measures of discrimination (area under receiver operating characteristic curves; AUROC), proximity to the true model (Akaike information criterion; AIC) and goodness of model fit (Hosmer-Lemeshow test). RESULTS: Some 438 058 patient records were analysed. TMPM demonstrated preferable AUROC (0·882 for TMPM versus 0·845 for TARN), AIC (18 204 versus 21 163) and better fit to the data (32·4 versus 153·0) compared with TARN. CONCLUSION: TMPM had greater discrimination, proximity to the true model and goodness-of-fit than the anatomical injury component of TARN. TMPM should be considered for the injury severity measure for the comparative assessment of trauma centres

Injury in China: a systematic review of injury surveillance studies conducted in Chinese hospital emergency departments

<p>Abstract</p> <p>Background</p> <p>Injuries represent a significant and growing public health concern in China. This <it>Review </it>was conducted to document the characteristics of injured patients presenting to the emergency department of Chinese hospitals and to assess of the nature of information collected and reported in published surveillance studies.</p> <p>Methods</p> <p>A systematic search of MEDLINE and China Academic Journals supplemented with a hand search of journals was performed. Studies published in the period 1997 to 2007 were included and research published in Chinese was the focus. Search terms included emergency, injury, medical care.</p> <p>Results</p> <p>Of the 268 studies identified, 13 were injury surveillance studies set in the emergency department. Nine were collaborative studies of which eight were prospective studies. Of the five single centre studies only one was of a prospective design. Transport, falls and industrial injuries were common mechanisms of injury. Study strengths were large patient sample sizes and for the collaborative studies a large number of participating hospitals. There was however limited use of internationally recognised injury classification and severity coding indices.</p> <p>Conclusion</p> <p>Despite the limited number of studies identified, the scope of each highlights the willingness and the capacity to conduct surveillance studies in the emergency department. This <it>Review </it>highlights the need for the adoption of standardized injury coding indices in the collection and reporting of patient health data. While high level injury surveillance systems focus on population-based priority setting, this <it>Review </it>demonstrates the need to establish an internationally comparable trauma registry that would permit monitoring of the trauma system and would by extension facilitate the optimal care of the injured patient through the development of informed quality assurance programs and the implementation of evidence-based health policy.</p

Advanced Trauma Life Support®. ABCDE from a radiological point of view

Accidents are the primary cause of death in patients aged 45 years or younger. In many countries, Advanced Trauma Life Support® (ATLS®) is the foundation on which trauma care is based. We will summarize the principles and the radiological aspects of the ATLS®, and we will discuss discrepancies with day to day practice and the radiological literature. Because the ATLS® is neither thorough nor up-to-date concerning several parts of radiology in trauma, it should not be adopted without serious attention to defining the indications and limitations pertaining to diagnostic imaging