title: Outcome prediction in trauma.
abstract: BACKGROUND
  In the Trauma Audit and Research Network (TARN), currently the largest trauma network in Europe, outcome prediction is performed using the TRISS methodology since 1989. Its database contains 200,000 hospital admissions from 110 hospitals over the country, but a large amount of data is lost for the modelling because of missing data. To improve some of the shortcomings of TRISS a new model was developed.
  
  
  METHODS
  The data for modelling consisted of 100,399 hospital trauma admissions over the period 1996 to 2001. Using the Glasgow Coma Score (GCS) instead of RTS has dramatically reduced the number of missing cases. Gender 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, gender and gender 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% 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.
  
  
  CONCLUSIONS
  The new model has enabled us to include most of the cases that were excluded under the TRISS's 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.
authors.affiliation: The University of Manchester, The Trauma Audit & Research Network, Clinical Sciences Building, Hope Hospital, Salford M6 8HD, UK. Omar.Bouamra@man.ac.uk
authors.forename: Omar
authors.initials: O
authors.lastname: Bouamra
authors.forename: Alan
authors.initials: A
authors.lastname: Wrotchford
authors.forename: Sally
authors.initials: S
authors.lastname: Hollis
authors.forename: Andy
authors.initials: A
authors.lastname: Vail
authors.forename: Maralyn
authors.initials: M
authors.lastname: Woodford
authors.forename: Fiona
authors.initials: F
authors.lastname: Lecky
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D002648:Child; D002675:Child, Preschool; D005060:Europe; D005260:Female; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D010043:Outcome and Process Assessment, Health Care / Q000379:methods* / Q000706:statistics & numerical data; D012107:Research Design; D015599:Trauma Severity Indices; D014947:Wounds and Injuries / Q000401:mortality*