Correlating injury severity scores and major trauma volume using a state-wide in-patient administrative dataset linked to trauma registry data-A retrospective analysis from New South Wales Australia.

Introduction: Trauma registries are used to analyse and report activity and benchmark quality of care at designated facilities within a trauma system. These capabilities may be enhanced with the incorporation of administrative and electronic medical record datasets, but are currently limited by the use of different injury coding systems between trauma and administrative datasets.

Objectives: Use an Abbreviated Injury Scale to International Classification of Disease (AIS-ICD) mapping tool to correlate estimated injury severity scores and major trauma volume based on administrative data collections with trauma registry data.

Methods: Adult trauma cases were identified from the New South Wales Trauma Registry between 2012 and 2016 and linked probabilistically using age, facility and date of facility arrival to the Admitted Patient Data Collection (APDC). Estimated Injury Severity Scores (ISS) were derived using the AIS-ICD mapping tool applied to diagnoses contained in the APDC.

Results: A total of eligible 13,439 cases were analysed. The overall correlation between trauma registry ISS and ISS estimated from APDC using the AIS-ICD mapping tool was low to moderate (Spearman Rho 0.41 95%CI 0.40, 0.43). Based on an estimated ISS cut-off value of 8, there was high correlation between estimated trauma volume and the number of major trauma cases at each facility (Spearman Rho 0.98, 95%CI 0.95, 0.99). Trauma Revised Injury Severity Score (TRISS) was associated with only slightly higher mortality prediction performance compared to estimated ISS (AUROC 0.76 95%CI 0.75, 0.78 versus AUROC 0.74 95%CI 0.73, 0.76).

Conclusion: A low to moderate correlation exists between individual patient ISS scores based on AIS to ICD mapping of in-patient data collection, but a high correlation for overall major trauma volume using the AIS-ICD mapping at facility level with comparable TRISS mortality prediction.