The Trauma and Injury Severity Score (TRISS) revised.
BACKGROUND
The Trauma and Injury Severity Score (TRISS) remains the most commonly used tool for benchmarking trauma fatality outcome. Recently, it was demonstrated that the predictive power of TRISS could be substantially improved by re-classifying the component variables and treating the variable categories nominally. This study aims to develop revised TRISS models using re-classified variables, to assess these models’ predictive performances against existing TRISS models, and to identify and recommend a preferred TRISS model.


METHODS
Revised TRISS models for blunt and penetrating injury mechanism were developed on an adult (aged 15 years) sample from the National Trauma Data Bank National Sample Project (NSP), using 5-category variable classifications and weighted logistic regression. Their predictive performances were then assessed against existing TRISS models on the unweighted NSP, National Trauma Data Bank (NTDB), and New Zealand Database (NZDB) samples using area under the Receiver Operating Characteristic curve (AUC) and Bayesian Information Criterion (BIC) statistics.


RESULTS
The weighted NSP sample included 1,124,001 adults with blunt or penetrating injury mechanism events and known discharge status, of whom 1,061,709 (94.5%) survived to discharge. Complete information for all TRISS variables was available for 896,212 (79.7%). Revised TRISS models that included main-effects and two-factor interaction terms had superior AUC and BIC statistics to main effects models and existing TRISS models for patients with complete data in NSP, NTDB and NZDB samples. Predictive performance decreased as the number of variables with missing values included within revised TRISS models increased, but model performances generally remained superior to existing TRISS models.


CONCLUSIONS
Revised TRISS models had importantly improved predictive capacities over existing TRISS models. Additionally, they were easily computed, utilised only those variables already collected for existing TRISS models, and could be applied and produce meaningful survival probabilities when one or more of the predictor variables contained missing values. The preferred revised TRISS model included main-effects and two-factor interaction terms and allowed for missing values in all predictor variables. A strong case exists for replacing existing TRISS models in trauma scoring systems benchmarking software with this preferred revised TRISS model.
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; D002648:Child; D002675:Child, Preschool; D016208:Databases, Factual; D005260:Female; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D015601:Injury Severity Score; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D009520:New Zealand; D012042:Registries; D015203:Reproducibility of Results; D014193:Trauma Centers / Q000145:classification* / Q000706:statistics & numerical data; D014949:Wounds, Nonpenetrating / Q000145:classification* / Q000401:mortality; D014950:Wounds, Penetrating / Q000145:classification* / Q000401:mortality; D055815:Young Adult