Evaluation of a stochastic Markov-chain model for the development of forelimb injuries in Thoroughbred racehorses

Abstract

To evaluate a Markov-chain model for the development of forelimb injuries in Thoroughbreds and to use the model to determine effects of reducing sprint distance on incidence of metacarpal condylar fracture (CDY) and severe suspensory apparatus injury (SSAI). Weekly exercise and injury data for 122 Thoroughbreds during racing or training. Weekly data were used to construct a Markov-chain model with 5 states (uninjured [UNINJ], palpable suspensory apparatus injury [PSAII, SSAI, CDY, and lost to follow-up [LOST]). Transition probabilities between UNINJ and PSAI were estimated as a function of weekly sprint distance by use of linear regression analysis. The model was used to predict distributions of annual CDY and SSAI incidences in southern California racehorses and was validated by using CDY incidence reported by racetrack practitioners. The model was modified by reducing the number of sprint distances that were > 6 furlongs (> 1.20 km) by 20%, and CDY and SSAI incidences were compared with those generated by the baseline model. The model accurately fit development of injuries in the sample population but overestimated development of injuries in the southern California racehorse population. Development of and recovery from PSAI were correlated with distance run at high speeds. Reducing by 20% the number of sprints run at distances > 6 furlongs significantly reduced modeled annual CDY and SSAI incidence by 9%. Reducing the number of sprints at distances > 6 furlongs, particularly among horses with PSAI, reduces risk of CDY and SSAI.