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Comparison of two prognostic models in trauma outcome

journal contribution
posted on 2023-05-20, 14:16 authored by Cook, A, Osler, T, Glance, L, Lecky, F, Bouamra, O, Weddle, J, Gross, B, Ward, J, Moore III, FO, Rogers, F, David Hosmer

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.


Publication title

British Journal of Surgery










Menzies Institute for Medical Research


John Wiley & Sons Ltd

Place of publication

The Atrium, Southern Gate, Chichester, England, W Sussex, Po19 8Sq

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Copyright 2018 BJS Society Ltd.

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Behaviour and health

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