Abstract

BackgroundTrauma centres and systems have been associated with improved morbidity and mortality after injury. However, variability in outcomes across centres within a given system have been demonstrated. Performance improvement initiatives, that utilize external benchmarking as the backbone, have demonstrated system-wide improvements in outcomes. This data driven approach has been lacking in Australia to date. Recent improvement in local data quality may provide the opportunity to engage in data driven performance improvement. Our objective was to generate risk-adjusted outcomes for the purpose of external benchmarking of trauma services in New South Wales (NSW) based on existing data standards. MethodsRetrospective cohort study of the NSW Trauma Registry. We included adults (>16 years), with an Injury Severity Score >12, that received definitive care at either Major Trauma Services (MTS) or Regional Trauma Services (RTS) between 2012-2016. Hierarchical logistic regression models were then used to generate risk-adjusted outcomes. Our outcome measure was in-hospital death. Demographics, vital signs, transfer status, survival risk ratios, and injury characteristics were included as fixed-effects. Median odds ratios (MOR) and centre-specific odds ratios with 95% confidence intervals were generated. Centre-level variables were explored as sources of variability in outcomes. Results14,452 patients received definitive care at one of seven MTS (n = 12,547) or ten RTS (n = 1905). Unadjusted mortality was lower at MTS (9.4%) compared to RTS (11.2%). After adjusting for case-mix, the MOR was 1.33, suggesting that the odds of death was 1.33-fold greater if a patient was admitted to a randomly selected centre with worse as opposed to better risk-adjusted mortality. Definitive care at an MTS was associated with a 41% lower likelihood of death compared to definitive care at an RTS (OR 0.59 95%CI 0.35-0.97). Similar findings were present in the elderly and isolated severe brain injury subgroups. ConclusionsThe NSW trauma system exhibited variability in risk-adjusted outcomes that did not appear to be explained by case-mix. A better understanding of the drivers of the described variation in outcomes is crucial to design targeted locally-relevant quality improvement interventions.

Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call