Identification and internal validation of models for predicting survival and ICU admission following a traumatic injury

Background: Measures to improve the accuracy of determining survival and intensive care unit (ICU) admission using the International Classification of Injury Severity Score (ICISS) are not often conducted on a population-wide basis. The aim is to determine if the predictive ability of survival and ICU admission using ICISS can be improved depending on the method used to derive ICISS and incremental inclusion of covariates. Method: A retrospective analysis of linked injury hospitalisation and mortality data during 1 January 2010 to 30 June 2014 in New South Wales, Australia was conducted. Both multiplicative-injury and single-worst-injury ICISS were calculated. Logistic regression examined 90-day mortality and ICU admission with a range of predictor variables. The models were assessed in terms of their ability to discriminate survivors and non-survivors, model fit, and variation explained. Results: There were 735,961 index injury admissions, 13,744 (1.9%) deaths within 90-days and 23,054 (3.1%) ICU admissions. The best predictive model for 90-day mortality was single-worst-injury ICISS including age group, gender, all comorbidities, trauma centre type, injury mechanism, and nature of injury as covariates. The multiplicative-injury ICISS with age group, gender, all comorbidities, injury mechanism, and nature of injury was the best predictive model for ICU admission. Conclusions: The inclusion of comorbid conditions, injury mechanism and nature of injury, improved discrimination for both 90-day mortality and ICU admission. Moves to routinely use ICD-based injury severity measures, such as ICISS, should be considered for hospitalisation data replacing more resource-intensive injury severity classification measures.