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Aims

To assess risk factors for elimination due to lameness and metabolic reasons from Fédération Equestre Internationale (FEI) endurance rides of 80–160 km distance.

Methods

Venue, horse and rider-related variables (n = 33, including data on speed, signalment, previous experience) were collected from the FEI website. Weather and terrain data were collected at the venue. Univariable and multivariable logistic regression and generalised estimated equation (GEE) statistics were performed to assess risk factors for eliminations due to lameness and metabolic reasons, respectively.

Results

Multivariable logistic regression on 1435 horse starts revealed that the venue (n = 11) (P = 0.013), the horse's previous experience at greater distance than the current ride (OR = 0.82, P = 0.008) and time (>90 days) elapsed since the last FEI ride (OR = 0.78, P = 0.044) were significantly associated with elimination for lameness; all 3 predictors remained significant in the GEE model. In the multivariable model for elimination for metabolic reasons, the venue (P = 0.011), increasing number of entries (OR = 1.008, P = 0.001) and deep sand or soil on the track (OR = 1.98, P = 0.001) significantly increased the risk of elimination for metabolic reasons.

Conclusions and practical significance

Decreasing the frequency of racing schedule may contribute to decreased risk of elimination for lameness. Competing in deep sand or soil may contribute to exhaustion leading to elimination for metabolic reasons. Venue was associated with both outcomes; a number of reasons other than terrain and going are likely to contribute to this; e.g. unmeasured horse-level factors (training, previous injuries etc.) and the riders' aim (e.g. training, qualification, competition). Elimination due to lameness or metabolic reason is likely to be the end result of a complex process, of which not every aspect was or can be measured. However, further studies with a larger number of horse starts and assessing variables that could not be measured in this study may identify risk factors that can be modified.

Ethical animal research

Not required by this Congress: retrospective analysis of data in public domain. Sources of funding: A. Nagy's PhD scholarship was funded by the University of Bristol. Competing interests: None.