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
LIM France, Labcom LIM-ENVA, 24300 Nontron, France.
Ecole Nationale Vétérinaire d'Alfort, USC INRAE-ENVA 957 BPLC, CIRALE, 94700 Maisons-Alfort, France.
Animals (Basel). 2022 Dec 11;12(24):3498. doi: 10.3390/ani12243498.
Defining whether a gait asymmetry should be considered as lameness is challenging. Gait analysis systems now provide relatively accurate objective data, but their interpretation remains complex. Thresholds for discriminating between horses that are visually assessed as being lame or sound, as well as thresholds for locating the lame limb with precise sensitivity and specificity are essential for accurate interpretation of asymmetry measures. The goal of this study was to establish the thresholds of asymmetry indices having the best sensitivity and specificity to represent the visual single-limb lameness assessment made by expert veterinarians as part of their routine practice. Horses included in this study were evaluated for locomotor disorders at a clinic and equipped with the EQUISYM system using inertial measurement unit (IMU) sensors. Visual evaluation by expert clinicians allocated horses into five groups: 49 sound, 62 left forelimb lame, 67 right forelimb lame, 23 left hindlimb lame, and 23 right hindlimb lame horses. 1/10 grade lame horses were excluded. Sensors placed on the head (_H), the withers (_W), and the pelvis (_P) provided vertical displacement. Relative difference of minimal (AI-min) and maximal (AI-max) altitudes, and of upward (AI-up) and downward (AI-down) amplitudes between right and left stance phases were calculated. Receiver operating characteristic (ROC) curves discriminating the sound horses from each lame limb group revealed the threshold of asymmetry indice associated with the best sensitivity and specificity. AI-up_W had the best ability to discriminate forelimb lame horses from sound horses with thresholds (left: -7%; right: +10%) whose sensitivity was greater than 84% and specificity greater than 88%. AI-up_P and AI-max_P discriminated hindlimb lame horses from sound horses with thresholds (left: -7%; right: +18% and left: -10%; right: +6%) whose sensitivity was greater than 78%, and specificity greater than 82%. Identified thresholds will enable the interpretation of quantitative data from lameness quantification systems. This study is mainly limited by the number of included horses and deserves further investigation with additional data, and similar studies on circles are warranted.
确定步态不对称是否应被视为跛足具有挑战性。步态分析系统现在能提供相对准确的客观数据,但其解读仍然复杂。区分经视觉评估为跛足或健康的马匹的阈值,以及以精确的灵敏度和特异性定位跛肢的阈值,对于准确解读不对称测量结果至关重要。本研究的目的是确定具有最佳灵敏度和特异性的不对称指数阈值,以代表专家兽医在日常实践中进行的视觉单肢跛足评估。本研究中的马匹在一家诊所接受运动障碍评估,并配备了使用惯性测量单元(IMU)传感器的EQUISYM系统。专家临床医生的视觉评估将马匹分为五组:49匹健康马、62匹左前肢跛马、67匹右前肢跛马、23匹左后肢跛马和23匹右后肢跛马。排除了1/10级跛马。放置在头部(_H)、肩胛(_W)和骨盆(_P)上的传感器提供垂直位移。计算了左右站立阶段之间最小(AI-min)和最大(AI-max)高度以及向上(AI-up)和向下(AI-down)幅度的相对差异。区分健康马与各跛肢组马匹的受试者工作特征(ROC)曲线揭示了与最佳灵敏度和特异性相关的不对称指数阈值。AI-up_W具有最佳能力,能以阈值(左:-7%;右:+10%)将前肢跛马与健康马区分开来,其灵敏度大于84%,特异性大于88%。AI-up_P和AI-max_P以阈值(左:-7%;右:+18%和左:-10%;右:+6%)将后肢跛马与健康马区分开来,其灵敏度大于78%,特异性大于82%。确定的阈值将有助于解读来自跛足量化系统的定量数据。本研究主要受纳入马匹数量的限制,值得用更多数据进行进一步研究,并且有必要对环形运动进行类似研究。