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

Hospital-based injury data from level III institution in Cameroon: Retrospective analysis of the present registration system

BackgroundData on the epidemiology of trauma in Cameroon are scarce. Presently, hospital records are still used as a primary source of injury data. It has been shown that trauma registries could play a key role in providing basic data on trauma. Our goal is to review the present emergency ward records for completeness of data and provide an overview of injuries in the city of Limbe and the surrounding area in the Southwest Region of Cameroon prior to the institution of a formal registration system.MethodsA retrospective review of Emergency Ward logs in Limbe Hospital was conducted over one year. Records for all patients over 15 years of age were reviewed for 14 data points considered to be essential to a basic trauma registry. Completeness of records was assessed and a descriptive analysis of patterns and trends of trauma was performed.ResultsInjury-related conditions represent 27% of all registered admissions in the casualty department. Information on age, sex and mechanism of injury was lacking in 22% of cases. Information on vital signs was present in 2% (respiratory rate) to 12% (blood pressure on admission) of records. Patient disposition (admission, transfer, discharge, or death) was available 42% of the time, whilst location of injury was found in 84% of records. Road traffic injury was the most frequently recorded mechanism (36%), with the type of vehicle specified in 54% and the type of collision in only 22% of cases. Intentional injuries were the second most frequent mechanism at 23%.ConclusionThe frequency of trauma found in this context argues for further prevention and treatment efforts. The institution of a formal registration system will improve the completeness of data and lead to increased ability to evaluate the severity and subsequent public health implications of injury in this region