Omar Bouamra1, Richard Jacques2, Antoinette Edwards1, David W Yates1, Thomas Lawrence1, Tom Jenks1, Maralyn Woodford1, Fiona Lecky3. 1. Trauma Audit Research Network, Institute of Population Health, University of Manchester, Salford, UK. 2. Health Services Research Section, School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK. 3. Trauma Audit Research Network, Institute of Population Health, University of Manchester, Salford, UK Health Services Research Section, School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK.
Abstract
BACKGROUND: Prediction models for trauma outcome routinely control for age but there is uncertainty about the need to control for comorbidity and whether the two interact. This paper describes recent revisions to the Trauma Audit and Research Network (TARN) risk adjustment model designed to take account of age and comorbidities. In addition linkage between TARN and the Office of National Statistics (ONS) database allows patient's outcome to be accurately identified up to 30 days after injury. Outcome at discharge within 30 days was previously used. METHODS: Prospectively collected data between 2010 and 2013 from the TARN database were analysed. The data for modelling consisted of 129 786 hospital trauma admissions. Three models were compared using the area under the receiver operating curve (AuROC) for assessing the ability of the models to predict outcome, the Akaike information criteria to measure the quality between models and test for goodness-of-fit and calibration. Model 1 is the current TARN model, Model 2 is Model 1 augmented by a modified Charlson comorbidity index and Model 3 is Model 2 with ONS data on 30 day outcome. RESULTS: The values of the AuROC curve for Model 1 were 0.896 (95% CI 0.893 to 0.899), for Model 2 were 0.904 (0.900 to 0.907) and for Model 3 0.897 (0.896 to 0.902). No significant interaction was found between age and comorbidity in Model 2 or in Model 3. CONCLUSIONS: The new model includes comorbidity and this has improved outcome prediction. There was no interaction between age and comorbidity, suggesting that both independently increase vulnerability to mortality after injury. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/
BACKGROUND: Prediction models for trauma outcome routinely control for age but there is uncertainty about the need to control for comorbidity and whether the two interact. This paper describes recent revisions to the Trauma Audit and Research Network (TARN) risk adjustment model designed to take account of age and comorbidities. In addition linkage between TARN and the Office of National Statistics (ONS) database allows patient's outcome to be accurately identified up to 30 days after injury. Outcome at discharge within 30 days was previously used. METHODS: Prospectively collected data between 2010 and 2013 from the TARN database were analysed. The data for modelling consisted of 129 786 hospital trauma admissions. Three models were compared using the area under the receiver operating curve (AuROC) for assessing the ability of the models to predict outcome, the Akaike information criteria to measure the quality between models and test for goodness-of-fit and calibration. Model 1 is the current TARN model, Model 2 is Model 1 augmented by a modified Charlson comorbidity index and Model 3 is Model 2 with ONS data on 30 day outcome. RESULTS: The values of the AuROC curve for Model 1 were 0.896 (95% CI 0.893 to 0.899), for Model 2 were 0.904 (0.900 to 0.907) and for Model 3 0.897 (0.896 to 0.902). No significant interaction was found between age and comorbidity in Model 2 or in Model 3. CONCLUSIONS: The new model includes comorbidity and this has improved outcome prediction. There was no interaction between age and comorbidity, suggesting that both independently increase vulnerability to mortality after injury. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/
Authors: Leonie de Munter; Nancy C W Ter Bogt; Suzanne Polinder; Charlie A Sewalt; Ewout W Steyerberg; Mariska A C de Jongh Journal: PLoS One Date: 2018-12-18 Impact factor: 3.240
Authors: Philip McHale; Daniel Hungerford; David Taylor-Robinson; Thomas Lawrence; Timothy Astles; Ben Morton Journal: PLoS One Date: 2018-12-31 Impact factor: 3.240
Authors: Charlie A Sewalt; Eveline J A Wiegers; Fiona E Lecky; Dennis den Hartog; Stephanie C E Schuit; Esmee Venema; Hester F Lingsma Journal: Scand J Trauma Resusc Emerg Med Date: 2020-03-06 Impact factor: 2.953