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Comparison of two prognostic models in trauma outcome

Citation

Cook, A and Osler, T and Glance, L and Lecky, F and Bouamra, O and Weddle, J and Gross, B and Ward, J and Moore III, FO and Rogers, F and Hosmer, D, Comparison of two prognostic models in trauma outcome, British Journal of Surgery, 105, (5) pp. 513-519. ISSN 0007-1323 (2018) [Refereed Article]

Copyright Statement

Copyright 2018 BJS Society Ltd.

DOI: doi:10.1002/bjs.10764

Abstract

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.

Item Details

Item Type:Refereed Article
Keywords:stattistical models
Research Division:Mathematical Sciences
Research Group:Statistics
Research Field:Biostatistics
Objective Division:Health
Objective Group:Public health (excl. specific population health)
Objective Field:Behaviour and health
UTAS Author:Hosmer, D (Professor David Hosmer)
ID Code:138950
Year Published:2018
Web of Science® Times Cited:6
Deposited By:Menzies Institute for Medical Research
Deposited On:2020-05-14
Last Modified:2020-06-17
Downloads:0

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