Validation and Applications of Discrete Element Analysis in the Hip Joint

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Abstract

Osteoarthritis is a progressive degenerative joint disease which causes pain, inflammation, and eventual loss of joint function. This debilitating disease affects approximately 3% of U.S. adults over 30 years old, with direct medical costs of over $100 billion each year. Post-traumatic osteoarthritis is a sub-set of osteoarthritis initiated by injuries such as a fracture of the joint surface. When a surgeon reconstructs a fractured joint, there are often residual incongruities on the surface, which can lead to elevated contact stresses. Increased cartilage contact stress has been shown to be a major risk factor for developing post-traumatic osteoarthritis. Computational modeling offers a method of detecting elevated contact stresses and thereby assessing the associated risk of a patient developing post-traumatic osteoarthritis. Discrete element analysis (DEA) is a computational method capable of fast and reliable contact stress predictions that has been used successfully to predict knee and ankle osteoarthritis. The purpose of this study was to validate the accuracy of DEA models of both intact and fractured hips by directly comparing experimentally measured intra-articular contact stresses in human cadaveric hips to corresponding DEA predictions. Overall correlation was greater than 90% for both intact and fractured hips. The validated DEA algorithm was then applied to a series of 3 patients with a hip fracture and another series of 19 patients with surgical hip realignment. As anticipated, changes in contact stress correlated well with pain and function (p < 0.05). This validated DEA model appears to be a clinically useful tool for identifying patients who are at higher risk for developing osteoarthritis as a result of elevated joint contact stresses.

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Cited By (1)

Year	Entry
2017	Thomas HD. A Computational Investigation of Patient Factors Contributing to Contact Stress Abnormalities in the Dysplastic Hip Joint [Master's thesis]. University of Iowa; December 2017.