Risk factors for musculoskeletal injuries in 2-year-old Thoroughbred racehorses

Abstract

Musculoskeletal injuries (MSI) in racehorses have been identified as the most common cause of lost training days and weeks spent resting at pasture. Several training-, track- and horse-related risk factors have been identified. However, there is no clear understanding of the inter-relationship between these factors. The aim of this longitudinal cohort study was to investigate risk factors for MSI in 2-year-old Thoroughbred racehorses. Australian Thoroughbred trainers were convenience sampled and enrolled in a 27-month longitudinal cohort study. The study population consisted of all 2-year-old Thoroughbred racehorses trained by a participating trainer. Horses were followed from the time of enrolment until the completion of the study or until they were lost to follow-up. Trainers were visited at approximately 14-day intervals to collect training and injury data. Training days were categorised as fast days if the maximum speed during training exceeded 800 m/min. For each horse, the first training period that included one or more fast days was analysed. A multiple logistic regression model was used to determine those horse- and training-related variables that were associated with MSI. Data from 274 horses trained by 14 trainers at five racetracks were analysed. Forty percent of the horses sustained a MSI during their first fast preparation. The average distance trained at speeds ≥800 m/min and the percentages of fast days from the first fast day until the end of the preparation were the only variables associated with MSI. After accounting for these variables there were still significant differences between trainers. This suggests that there are other training-related variables such as the rate of increase in distance trained at speeds ≥800 m/min that may be risk factors for MSI. In addition, other factors such as the level of veterinary involvement and the training centre/racetrack may be risk factors for MSI. More analysis using multi-level modelling is required to determine the relationship between trainer- and track-related risk factors.