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Updated: Jul 20, 2026
Controlled Cortical Impact Model for Traumatic Brain Injury
Published on: August 5, 2014
A new approach to outcome prediction in trauma: A comparison with the TRISS model
Omar Bouamra1, Alan Wrotchford, Sally Hollis
1University of Manchester, The Trauma Audit & Research Network, Clinical Sciences Building, Hope Hospital, Salford, United Kingdom. Omar.Bouamra@manchester.ac.uk
Background:
The Trauma Audit & Research Network (TARN) has been using the TRISS methodology since 1989. Its database contains 200,000 hospital admissions from 110 hospitals over the country. To improve outcome prediction, a revision of the current model became necessary. Our model tried to overcome some of the concerns of the trauma community, namely missing data, functional form of the predictors, inclusion criteria and patient's death within 30 days.
Methods:
The data for modeling consisted of 100,399 anonymized hospital trauma admissions during the period 1996 to 2001. Cross validation was performed on this data set, and a multiple logistic regression model was derived using the prediction set and then its prediction ability was tested on the validation set. Fractional polynomials modeling showed that the linear functional form of the Injury Severity Score (ISS) in the model was not satisfactory. Using the Glasgow Coma Score (GCS) instead of the revised trauma score (RTS) has dramatically reduced the number of missing cases. Sex and its interaction with age have also been included in the model. The model was tested on different subsets of cases, traditionally excluded, such as children, those with penetrating injuries, and ventilated and transferred patients. The new model included all those subsets using age, a transformation of ISS, GCS, sex, and sex by age interaction as predictors.
Results:
The model has shown a good discriminant ability tested by the Area under the Receiver Operating Characteristic (AROC) curve. The values of the AROC for the new model were 0.947 (95% confidence interval [CI]: 0.943-0.951) on the prediction set and 0.952 (95% CI: 0.946-0.957) on the validation set compared, respectively, with 0.937 (95% CI: 0.932-0.943) and 0.941 (95% CI: 0.936-0.952) for TRISS.
Conclusion:
The new model has enabled us to include most of the cases that were excluded under the TRISSs inclusion criteria, less missing data are incurred and the predictive performance was significantly better than that of the TRISS model as shown by the AROC curves.
