Predictive Modeling of Hamstring Strain Injuries in Elite Australian Footballers

AbstractPurposeThree of the most commonly identified hamstring strain injury (HSI) risk factors are age, previous HSI and low levels of eccentric hamstring strength. However, no study has investigated the ability of these risk factors to predict the incidence of HSI in elite Australian footballers. Accordingly, the purpose of this prospective cohort study was to investigate the predictive ability of HSI risk factors using machine learning techniques.MethodsEccentric hamstring strength, demographic and injury history data were collected at the start of pre-season for 186 and 176 elite Australian footballers in 2013 and 2015 respectively. Any prospectively occurring HSIs were reported to the research team. Using various machine learning techniques, predictive models were built for 2013 and 2015 within-year HSI prediction and between-year HSI prediction (2013 to 2015). The calculated probabilities of HSI were compared to the injury outcomes and area under the curve (AUC) was determined and used to assess the predictive performance of each model.ResultsThe minimum, maximum and median AUC values for the 2013 models were 0.26, 0.91 and 0.58 respectively. For the 2015 models, the minimum, maximum and median AUC values were, correspondingly, 0.24, 0.92 and 0.57. For the between-year predictive models the minimum, maximum and median AUC values were 0.37, 0.73 and 0.52 respectively.ConclusionWhile some iterations of the models achieved near perfect prediction, the large ranges in AUC highlight the fragility of the data. The 2013 models performed slightly better than the 2015 models. The predictive performance of between-year HSI models was poor however. In conclusion, risk factor data cannot be used to identify athletes at an increased risk of HSI with any consistency. Purpose Three of the most commonly identified hamstring strain injury (HSI) risk factors are age, previous HSI and low levels of eccentric hamstring strength. However, no study has investigated the ability of these risk factors to predict the incidence of HSI in elite Australian footballers. Accordingly, the purpose of this prospective cohort study was to investigate the predictive ability of HSI risk factors using machine learning techniques. Methods Eccentric hamstring strength, demographic and injury history data were collected at the start of pre-season for 186 and 176 elite Australian footballers in 2013 and 2015 respectively. Any prospectively occurring HSIs were reported to the research team. Using various machine learning techniques, predictive models were built for 2013 and 2015 within-year HSI prediction and between-year HSI prediction (2013 to 2015). The calculated probabilities of HSI were compared to the injury outcomes and area under the curve (AUC) was determined and used to assess the predictive performance of each model. Results The minimum, maximum and median AUC values for the 2013 models were 0.26, 0.91 and 0.58 respectively. For the 2015 models, the minimum, maximum and median AUC values were, correspondingly, 0.24, 0.92 and 0.57. For the between-year predictive models the minimum, maximum and median AUC values were 0.37, 0.73 and 0.52 respectively. Conclusion While some iterations of the models achieved near perfect prediction, the large ranges in AUC highlight the fragility of the data. The 2013 models performed slightly better than the 2015 models. The predictive performance of between-year HSI models was poor however. In conclusion, risk factor data cannot be used to identify athletes at an increased risk of HSI with any consistency. Corresponding author: Joshua D. Ruddy, joshua.ruddy@myacu.edu.au, 17 Young Street, Fitzroy, VIC, Australia, 3065 The authors declare that the results of the study are presented clearly, honestly, and without fabrication, falsification, or inappropriate data manipulation. The results of this study do not constitute endorsement by ACSM. AS and DO are listed as co-inventors on a patent filed for a field testing device of eccentric hamstring strength (PCT/AU2012/001041.2012) as well as being minority shareholders in Vald Performance Pty Ltd, the company responsible for comercializng the device. The remaining authors declare no competing interests. No funding was received. Accepted for Publication: 14 December 2017 © 2017 American College of Sports Medicine

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