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Bilateral Change in Vertical Hoof Force Distribution in Horses with Unilateral Forelimb Lameness before and after Successful Diagnostic Anaesthesia.

Johanna R HoffmannFlorian GeburekJenny HagenKathrin BüttnerAntonio M CruzMichael Röcken
Published in: Animals : an open access journal from MDPI (2022)
Kinetic examinations of horses with induced lameness as well as the effect of perineural anaesthesia in sound horses have shown promise, but clinical studies regarding the effect of diagnostic anaesthesia during the different stance phases are rare. Fourteen horses with unilateral forelimb lameness were examined with the Hoof™ System during trot to assess vertical force distribution (in kg) affecting both front hooves before and after diagnostic anaesthesia during landing, midstance, and breakover. For statistical analysis, a covariance analysis with repeated measurements regarding the limb (lame/sound) as well as anaesthesia (before/after) and the covariable body weight was performed. The p- values for the pairwise comparisons were adjusted using the Bonferroni-Holm correction ( p < 0.05). For all phases of the stance, a significant interaction between the factors limb and anaesthesia was shown. Before diagnostic anaesthesia, vertical force was significantly reduced on the lame limb compared to the sound limb during landing (-25%, p < 0.001), midstance (-20%, p < 0.001) and breakover (-27%, p < 0.001). After anaesthesia, the difference between both forelimbs was not significant anymore for all phases. The vertical force on the lame limb increased significantly after positive anaesthesia during the whole stance phase, with breakover being most affected (+27%, p = 0.001). Pressure measurements with the Hoof™ System can be used to evaluate the effect of diagnostic anaesthesia in a clinical setting with pain-related vertical force asymmetries being neutralised after diagnostic anaesthesia. Breakover is the main event influenced by lameness.
Keyphrases
  • body weight
  • single molecule
  • chronic pain
  • endothelial cells
  • machine learning
  • neuropathic pain
  • stress induced