Abstract
BackgroundMeasures 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.MethodA 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.ResultsThere 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.ConclusionsThe 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.
Highlights
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 multiplicative-injury ICISS with age group, gender, all comorbidities, injury mechanism, and nature of injury was the best predictive model for ICU admission
Previous research has indicated that treatment at a major trauma service can provide a survival advantage [12, 13], but it is unclear whether the accuracy of ICISS to indicate post-discharge survival could be improved by including type of trauma service as a covariate
Summary
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 ICISS has previously been shown to provide good estimates of survival following injury [1, 5] It has a practical advantage [6] as it is relatively derived by calculating Survival Risk Ratios (SRRs) for injury diagnosis classifications using hospital administrative data and an indicator of mortality. Both in-hospital [5, 7] and post-discharge mortality. Previous research has indicated that treatment at a major trauma service can provide a survival advantage [12, 13], but it is unclear whether the accuracy of ICISS to indicate post-discharge survival could be improved by including type of trauma service as a covariate
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