An Updated Subsequent Injury Categorisation Model (SIC-2.0): Data-Driven Categorisation of Subsequent Injuries in Sport
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ORIGINAL RESEARCH ARTICLE
An Updated Subsequent Injury Categorisation Model (SIC-2.0): Data-Driven Categorisation of Subsequent Injuries in Sport Liam A. Toohey1,2,3 • Michael K. Drew1,2 • Lauren V. Fortington2 Caroline F. Finch2,3 • Jill L. Cook2,3
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Ó Springer International Publishing AG, part of Springer Nature 2018
Abstract Background Accounting for subsequent injuries is critical for sports injury epidemiology. The subsequent injury categorisation (SIC-1.0) model was developed to create a framework for accurate categorisation of subsequent injuries but its operationalisation has been challenging. Objectives The objective of this study was to update the subsequent injury categorisation (SIC-1.0 to SIC-2.0) model to improve its utility and application to sports injury datasets, and to test its applicability to a sports injury dataset. Methods The SIC-1.0 model was expanded to include two levels of categorisation describing how previous injuries relate to subsequent events. A data-driven classification level was established containing eight discrete injury categories identifiable without clinical input. A sequential classification level that sub-categorised the data-driven categories according to their level of clinical relatedness has 16 distinct subsequent injury types. Manual and automated SIC-2.0 model categorisation were applied to a prospective injury dataset collected for elite rugby sevens players over a 2-year period. Absolute agreement between the two coding methods was assessed. & Liam A. Toohey [email protected] 1
Department of Physical Therapies, c/o AIS Physical Therapies, Australian Institute of Sport, Leverrier Street, Bruce, ACT 2617, Australia
2
Australian Centre for Research into Injury in Sport and its Prevention (ACRISP), Federation University Australia, Ballarat, VIC, Australia
3
School of Allied Health (Physiotherapy), Sport and Exercise Medicine Department, La Trobe University, Bundoora, VIC 3086, Australia
Results An automated script for automatic data-driven categorisation and a flowchart for manual coding were developed for the SIC-2.0 model. The SIC-2.0 model was applied to 246 injuries sustained by 55 players (median four injuries, range 1–12), 46 (83.6%) of whom experienced more than one injury. The majority of subsequent injuries (78.7%) were sustained to a different site and were of a different nature. Absolute agreement between the manual coding and automated statistical script category allocation was 100%. Conclusions The updated SIC-2.0 model provides a simple flowchart and automated electronic script to allow both an accurate and efficient method of categorising subsequent injury data in sport.
Key Points The updated subsequent injury categorisation (SIC2.0) model offers clinicians and researchers an expanded framework to categorise subsequent injuries within the sporting context. The development of a flowchart to guide users through the application of the SIC-2.0 model enhances the model’s useability. The creation of an automated script (available from the
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