1750 Prediction of 6 month Trauma PROMS using in-hospital data. Issue 12 (22nd November 2022) - Coats, Timothy; Bouamra, Omar; Edwards, Antoniette; Lecky, Fiona; Mirkes, Evgeny; Sergeant, Jamie; Ivan, Tyukin
Abstract : Aims, Objectives and Background: Trauma audit has used lived/died as an outcome for 30 years, but Patient Reported Outcome Measures (PROMS) have also been collected by the Trauma Audit and Research Network (TARN) for the past 5 years across major trauma centres. These are measured at 6 months after injury and include two measures of health-related quality of life, EQ5D-5 and GOSE, employment status and three patient experience questions. It is not known if 6-month PROMS can be predicted after major trauma. Method and Design: The TARN PROMS data was extracted and randomly divided into a model development (training) and a model testing (test) dataset. There is no standard way of using this type of complex data, so three different modelling approaches were used: (1) conventional logistic regression, (2) artificial intelligence (AI) selection of 'nearest neighbours', and (3) AI decision trees. The performance of each model was evaluated using the test dataset. Results and Conclusion: There were 5791 patients in the training set and 1447 patients in the test set. All three of the methods achieved an ROC AUC between 0.69 and 0.77 – implying that this might be the limit of prediction based on this type of data. When tested against the binary Glasgow Outcome Score the results are shown in the table 1 . The AI method of 'k Nearest Neighbours' achieved a balance between sensitivity (72%) and specificity (71%). Conclusion: patient reported outcomes at 6 months after injury can be predicted from in-hospital data. This potentially gives a new method for clinical audit and comparison of trauma outcomes using a measure that is more relevant to survivors of major trauma than the current 'lived/died'.