Younes Mohamed, Robert Céline, Cottin François, Barrey Eric
UBIAE, Université d'Evry Val d'Essonne, Evry, France.
INRA, GABI, UMR1313, Jouy-en-Josas, France; Université Paris-Est, Ecole Nationale Vétérinaire d'Alfort, Maison Alfort, France.
PLoS One. 2015 Aug 31;10(8):e0137013. doi: 10.1371/journal.pone.0137013. eCollection 2015.
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.
参加耐力赛的马匹中,近50%在兽医检查(兽医关卡)时被淘汰。在健康问题出现并需要治疗之前检测出不适合参赛的马匹,对兽医来说是一项挑战,但对于改善马匹福利至关重要。我们假设通过测量心率恢复变量有可能在赛事中更早地检测出不适合参赛的马匹。因此,本研究的目的是计算在前一个兽医关卡(n-1)记录的心率、心脏恢复时间和平均速度数据的逻辑回归,从而预测耐力赛后续阶段(n及之后)被淘汰的概率。速度和心率数据从四个国家组织的耐力赛(长度为80 - 160公里)的电子数据库中提取。总体而言,开始参赛的马匹中有39%被淘汰,主要原因是跛行(64%)或代谢紊乱(15%)。对于每个兽医关卡,计算解释变量(在前一个兽医关卡测量的平均速度、心脏恢复时间和心率)和分类变量(年龄和/或赛事距离)的逻辑回归,以估计被淘汰的概率。兽医关卡2至5的预测逻辑回归正确分类了62%至86%的被淘汰马匹。这些结果的稳健性通过接受操作特征曲线下的高面积(0.68 - 0.84)得到证实。总体而言,如果一匹马在兽医关卡1或2时心脏恢复时间超过11分钟,或在兽医关卡3或4时超过13分钟,那么它在下一个关卡被淘汰的几率为70%。在前一个兽医关卡测量的心率恢复和平均速度变量使我们能够预测在下一个兽医关卡是否会被淘汰。应在每次兽医检查时检查这些变量,以便尽早检测出不适合参赛的马匹。我们的预测方法可能有助于改善耐力赛中的马匹福利和伦理考量。