Exploring the Utility of Several Evaluation Methods in Distinguishing Cannon Bones from Fracture-Afflicted and Skeletally Intact Racehorses
thesisposted on 06.12.2019, 16:34 by Jonathan Elliot GaideJonathan Elliot Gaide
Stress fractures are common in the limb bones of human and equine athletes alike. Repetitive skeletal loading can lead to remodeling and the accumulation of microdamage in bone, which only becomes grossly evident during catastrophic fracture of the bone due to the accumulated microdamage. Though various metrics attempting to quantify bone health exist, none have distinguished themselves as early predictors of the susceptibility of bone to fracture. In this exploratory study, we examine the ability of several evaluation methods to distinguish between third metacarpal (MC3) bones from racehorses that have experienced a limb-bone fracture and from those that have not. Third metacarpal bones were harvested from deceased Thoroughbred racehorses and categorized into four groups: MC3 bones from horses whose cause of death was not related to skeletal fracture (Control group, n = 20), MC3 bones form horses that were euthanized after fracturing proximal sesamoid bones (Sesamoid group, n = 20), MC3 bones from horses that were euthanized after fracturing a non-MC3 long bone (Long Bone group, n = 19), and MC3 bones from horses that were euthanized after fracturing an MC3 (MC3 group, n = 5). Each MC3 bone underwent testing using a variety of tools and methods at the proximal, midshaft, and distal levels of the lateral, dorsal, and medial surfaces. All tools and methods (OsteoProbe reference point indentation, BioDent reference point indentation, x-ray, micro-CT, and pQCT) exhibited some capability in differentiating between control and fracture groups. The long-term objective of this project is to create a model that will utilize data from a set of evaluations and output the susceptibility of the horse to fracture a bone, a long bone, or the MC3, specifically. Although the sample size in this study is not sufficient to create a reliably predictive logistic regression model, promising results from preliminary models provide incentive to further explore the possibility of creating one. While clinical practicality will be a vital consideration for a model in the future, establishing this basis for the capability of each evaluation at hand is a necessary first step in predicting and preventing fracture in bone.