title: Speed and Cardiac Recovery Variables Predict the Probability of Elimination in Equine Endurance Events.
abstract: Nearly 50% of the horses participating in endurance events are eliminated at a veterinary examination (a vet gate). Detecting unfit horses before a health problem occurs and treatment is required is a challenge for veterinarians but is essential for improving equine welfare. We hypothesized that it would be possible to detect unfit horses earlier in the event by measuring heart rate recovery variables. Hence, the objective of the present study was to compute logistic regressions of heart rate, cardiac recovery time and average speed data recorded at the previous vet gate (n-1) and thus predict the probability of elimination during successive phases (n and following) in endurance events. Speed and heart rate data were extracted from an electronic database of endurance events (80-160 km in length) organized in four countries. Overall, 39% of the horses that started an event were eliminated--mostly due to lameness (64%) or metabolic disorders (15%). For each vet gate, logistic regressions of explanatory variables (average speed, cardiac recovery time and heart rate measured at the previous vet gate) and categorical variables (age and/or event distance) were computed to estimate the probability of elimination. The predictive logistic regressions for vet gates 2 to 5 correctly classified between 62% and 86% of the eliminated horses. The robustness of these results was confirmed by high areas under the receiving operating characteristic curves (0.68-0.84). Overall, a horse has a 70% chance of being eliminated at the next gate if its cardiac recovery time is longer than 11 min at vet gate 1 or 2, or longer than 13 min at vet gates 3 or 4. Heart rate recovery and average speed variables measured at the previous vet gate(s) enabled us to predict elimination at the following vet gate. These variables should be checked at each veterinary examination, in order to detect unfit horses as early as possible. Our predictive method may help to improve equine welfare and ethical considerations in endurance events.
authors: UBIAE, Université d'Evry Val d'Essonne, Evry, France.
authors: Mohamed
authors: M
authors: Younes
authors: INRA, GABI, UMR1313, Jouy-en-Josas, France; Université Paris-Est, Ecole Nationale Vétérinaire d'Alfort, Maison Alfort, France.
authors: Céline
authors: C
authors: Robert
authors: EA4532 CIAMS, Université Paris Sud, Orsay, France; Département STAPS, Université d'Evry Val d'Essonne, Evry, France.
authors: François
authors: F
authors: Cottin
authors: UBIAE, Université d'Evry Val d'Essonne, Evry, France; INRA, GABI, UMR1313, Jouy-en-Josas, France.
authors: Eric
authors: E
authors: Barrey
mesh_terms: D000818:Animals; D005684:Gait / Q000502:physiology; D006321:Heart / Q000502:physiology*; D006339:Heart Rate / Q000502:physiology; D006736:Horses / Q000502:physiology*; D007794:Lameness, Animal / Q000503:physiopathology; D016015:Logistic Models; D008659:Metabolic Diseases / Q000503:physiopathology; D010805:Physical Conditioning, Animal / Q000502:physiology*; D010807:Physical Endurance / Q000502:physiology*; D011336:Probability; D018634:Veterinarians