Trauma and Injury Severity Score (TRISS): is it time for variable re-categorisations and re-characterisations?
BACKGROUND
Despite its limitations, the Trauma and Injury Severity Score (TRISS) continues to be the most commonly used tool for benchmarking trauma outcome. Since its inception, considerable energy has been devoted to improving TRISS. However, there has been no investigation into the classification or characterisation of the TRISS variables. Using a major nationally representative database, this study aims to explore the adequacy of the existing TRISS model by investigating variable re-categorisations and alternative characterisations in a logistic model used to predict survival in adults after traumatic injury.


METHODS
Data were obtained from the National Trauma Data Bank National Sample Project (NSP). Each variable in the TRISS model was related to discharge status and various categorisations considered using weighted logistic regression. Categorisations were treated nominally,using a series of indicator variables. For each variable and classification level, the best category combination was ascertained using the Bayesian Information Criterion (BIC). All best 5-category classified TRISS variables were combined, as were all best 10-category classified TRISS variables, and their predictive performance assessed against two conventionally defined TRISS models on the unweighted NSP sample using area under the Receiver Operating Characteristic curve (AUC) and BIC statistics.


RESULTS
Overall, the weighted sample included 1,124,001 adults with injury events and known discharge status, of whom 1,061,709 (94.5%) were alive at discharge. When separately related to discharge status, each re-classified TRISS variable yielded a superior BIC statistic to its original specification. When investigating predictive performance, complete information was available for 167,239 (79.9%) adults with blunt and 20,643 (82.3%) adults with penetrating injury mechanisms. AUC and BIC estimates for the re-classified TRISS models were superior to the conventionally defined TRISS models. While having better predictive precision, the complexity associated with the best 10-category model resulted in the best 5-category model being preferred for penetrating mechanism injuries and being negligibly inferior for blunt mechanism injuries.


CONCLUSIONS
Substantial improvements in the predictive power of TRISS were demonstrated by reclassifying the component variables and treating the variable categories nominally. However, before anew TRISS model with updated coefficients can be published, variable interactions and the effect of missing data needs thorough statistical evaluation.
Schluter
Philip J
PJ
AUT University, School of Public Health and Psychosocial Studies, Auckland, New Zealand. philip.schluter@aut.ac.nz
D000293:Adolescent; D000328:Adult; D000368:Aged; D019985:Benchmarking; D005260:Female; D006801:Humans; D015601:Injury Severity Score; D008297:Male; D008875:Middle Aged; D009520:New Zealand / Q000453:epidemiology; D012042:Registries / Q000145:classification* / Q000706:statistics & numerical data; D015599:Trauma Severity Indices; D014947:Wounds and Injuries / Q000145:classification*; D055815:Young Adult