Cartilage degeneration algorithm predicts progression of osteoarthritis
A novel cartilage degeneration algorithm can predict the progression of osteoarthritis in individual patients, according to new research from the University of Eastern Finland. The new algorithm could greatly facilitate clinical decision-making in the treatment of osteoarthritis.
Finite element predictions of bone displacement in the direction of primary joint loading between a normal pelvis (left) and a pelvis with acetabular dysplasia (right) during simulated walking. The pelvic bone of the patient with hip dysplasia deformed much more than that of the normal hip joint, indicating that hip dysplasia may predispose the joint to early degeneration. Jeffrey Weiss, University of Utah in Computational Biomechanics: Making Strides Toward Patient Care
University of Eastern Finland August 25, 2017
Osteoarthritis (OA) is a joint disease that deteriorates the articular cartilage. The most important risk factors are ageing and overweight, and osteoarthritis is common especially in joints that are subject to heavy loading. For societies, osteoarthritis constitutes a significant financial burden: it cannot be cured by current treatments, and the disease often leads to joint replacement surgery, which is highly expensive. Current imaging methods, such as MRI or X-ray, only provide information on the thickness or composition of the cartilage, but they fail to provide data on the risk of osteoarthritis or tools to predict its progression.
A research group from the University of Eastern Finland tested the ability of a cartilage degeneration algorithm, created earlier by the same group, to predict the progression of osteoarthritis in individual patients and to grade the severity of their disease by using the Kellgren-Lawrence classification. The findings were published in Scientific Reports.
To reduce damaging loads on the inner side of his own early-stage osteoarthritic knee, B.J. Fregly measured the moment arm of the ground reaction force vector relative to his knee center before training (above left); then predicted the optimal moment arm (above center); and then trained himself to walk in a way that mimicked the optimum (above right). Note that the distance between the left knee center and the ground reaction force vector (in green) is reduced (relative to pre-training) by the optimization. This reduction is comparable to what the patient (Fregly) achieved post-training. The moment arm reduction for both the optimization and post-training resulted in roughly a 40 percent reduction in the first peak of the knee adduction moment curve, which is likely to be clinically significant. B.J. Fregly, University of Florida in Computational Biomechanics: Making Strides Toward Patient Care
The algorithm was applied to 21 patients who were divided into three groups: patients without OA, patients with mild OA, and patients with severe OA. The patients were divided into the groups based on their Kellgren-Lawrence grades defined experimentally after a four-year follow-up. At the start of the follow-up, all of the patients were OA-free.
The algorithm was applied at the onset of the follow-up, and the findings were compared against the four-year follow-up data. Based on the prognosis from the simulation and the experimentally defined Kellgren-Lawrence grades four years later, the researchers found that the algorithm was able to categorise patients into their correct groups.
The degeneration algorithm is based on stresses experienced by the knee joint during walking, and these were simulated on a computer. The algorithm assumes that stresses exceeding a certain threshold during walking will cause local degeneration in the articular cartilage of the knee.
This degeneration algorithm shows great potential in predicting patient-specific progression of osteoarthritis in the knee. The algorithm could be used to clinically simulate the effects of various interventions, including osteotomy, meniscectomy and weight loss, on the progression of osteoarthritis.
The new algorithm could facilitate clinical decision-making in the treatment of osteoarthritis. The objective is to slow down and possibly even stop the progression of the disease. Alleviated symptoms or their complete absence can greatly affect the functional capacity of patients.
| For further information, please contact: Early Stage Researcher Mimmi Liukkonen, tel. +358 50 467 2045. Senior Researcher Mika Mononen, tel. +358 40 355 3112 |
Source University of Eastern Finland via Science Daily
| References |
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Computational Modeling of Patellofemoral Joint Motion of Human Knee Jeffry Hartanto. 2016, Dept. of Computer Science, National University of Singapore. PDF
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Also see
Computational modelling can predict onset and progression of knee osteoarthritis in overweight people University of Eastern Finland
Computational Biomechanics: Making Strides Toward Patient Care Biomedical Computation Review, Stanford
Shoes, walking mechanics cause osteoarthritis in knees The Baxter Bulletin
