Macaire Claire, Hanne-Poujade Sandrine, De Azevedo Emeline, Denoix Jean-Marie, Coudry Virginie, Jacquet Sandrine, Bertoni Lélia, Tallaj Amélie, Audigié Fabrice, Hatrisse Chloé, Hébert Camille, Martin Pauline, Marin Frédéric, Chateau Henry
Labcom LIM-ENVA, LIM France, 24300 Nontron, France.
CIRALE, USC 957 BPLC, Ecole Nationale Vétérinaire d'Alfort, 94700 Maisons-Alfort, France.
Animals (Basel). 2023 Oct 25;13(21):3319. doi: 10.3390/ani13213319.
The assessment of lameness in horses can be aided by objective gait analysis tools. Despite their key role of evaluating a horse at trot on a circle, asymmetry thresholds have not been determined for differentiating between sound and lame gait during this exercise. These thresholds are essential to distinguish physiological asymmetry linked to the circle from pathological asymmetry linked to lameness. This study aims to determine the Asymmetry Indices (AIs) with the highest power to discriminate between a group of sound horses and a group of horses with consistent unilateral lameness across both circle directions, as categorized by visual lameness assessment conducted by specialist veterinarians. Then, thresholds were defined for the best performing AIs, based on the optimal sensitivity and specificity. AIs were calculated as the relative comparison between left and right minima, maxima, time between maxima and upward amplitudes of the vertical displacement of the head and the withers. Except the AI of maxima difference, the head AI showed the highest sensitivity (≥69%) and the highest specificity (≥81%) for inside forelimb lameness detection and the withers AI showed the highest sensitivity (≥72%) and the highest specificity (≥77%) for outside forelimb lameness detection on circles.
客观步态分析工具有助于评估马匹的跛行情况。尽管在评估马匹在圆周上小跑时起着关键作用,但尚未确定用于区分该运动中健全步态和跛行步态的不对称阈值。这些阈值对于区分与圆周相关的生理不对称和与跛行相关的病理不对称至关重要。本研究旨在确定具有最高辨别力的不对称指数(AI),以区分一组健全马匹和一组经专业兽医进行视觉跛行评估分类为在两个圆周方向上均存在持续性单侧跛行的马匹。然后,根据最佳敏感性和特异性,为表现最佳的AI定义阈值。AI计算为头部和肩胛部垂直位移的左右最小值、最大值、最大值之间的时间以及向上幅度的相对比较。除最大差值AI外,头部AI在检测内侧前肢跛行方面表现出最高敏感性(≥69%)和最高特异性(≥81%),肩胛部AI在检测圆周外侧前肢跛行方面表现出最高敏感性(≥72%)和最高特异性(≥77%)。