No abstract

No abstract

ObjectivesAdequate risk adjustment for factors beyond the control of the healthcare system contributes to the process of transparent and equitable benchmarking of trauma outcomes. Current risk adjustment models are not optimal in terms of the number and nature of predictor variables included in the model and the treatment of missing data. We propose a statistically robust and parsimonious risk adjustment model for the purpose of benchmarking.SettingThis study analysed data from the multicentre Australia New Zealand Trauma Registry from 1 July 2016 to 30 June 2018 consisting of 31 trauma centres.Outcome measuresThe primary endpoints were inpatient mortality and length of hospital stay. Firth logistic regression and robust linear regression models were used to study the endpoints, respectively. Restricted cubic splines were used to model non-linear relationships with age. Model validation was performed on a subset of the dataset.ResultsOf the 9509 patients in the model development cohort, 72% were male and approximately half (51%) aged over 50 years . For mortality, cubic splines in age, injury cause, arrival Glasgow Coma Scale motor score, highest and second-highest Abbreviated Injury Scale scores and shock index were significant predictors. The model performed well in the validation sample with an area under the curve of 0.93. For length of stay, the identified predictor variables were similar. Compared with low falls, motor vehicle occupants stayed on average 2.6 days longer (95% CI: 2.0 to 3.1), p<0.001. Sensitivity analyses did not demonstrate any marked differences in the performance of the models.ConclusionOur risk adjustment model of six variables is efficient and can be reliably collected from registries to enhance the process of benchmarking.

No abstract

No abstract