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Algorithm devised plotting the progress of osteoarthritis

New research from the University of Eastern Finland has pointed an interesting development in the field of osteoarthritis (OA) treatment, with the introduction of a cartilage degeneration algorithm which could predict the progression of osteoarthritis in individual patients. If accurate, this could be very valuable in doctors’ decision-making in the treatment of osteoarthritis and educate those at risk of developing it.

Osteoarthritis treatment has progressed in recent years with X-ray and MRI scanning – but those procedures can only provide diagnostic information on the thickness or composition of the cartilage. The Finnish researchers are proposing a scientific, non-intrusive breakdown of the risk of osteoarthritis in patients, which will give health professionals the ability to grade the severity of the disease by using the Kellgren-Lawrence classification.

Grading osteoarthritis

The study, recently published in the journal Scientific Reports, involved 21 patients who were divided into three groups: patients without OA, patients with mild OA, and patients with severe OA. The researchers divided these groups based on their Kellgren-Lawrence grades, which were defined experimentally after a four years.

At the start of the study, all of the patients were OA-free, which was then compared with the results at the four year follow up. Based on the prognosis from the simulation and the Kellgren-Lawrence grades four years later, the researchers found that the algorithm was able to categorise patients into their correct groups. How does the algorithm work?

It’s based on stresses experienced by the knee joint during walking, which were simulated on a computer. The algorithm makes the assumption that stresses exceeding a certain threshold during walking will cause local degeneration in the articular cartilage of the knee.

Why is this such an important development?

Because OA industry experts are of the belief that this degeneration algorithm shows great potential in predicting how osteoarthritis will continue to develop in the knee – with the hope being that it could be used to simulate effects of different treatments such as osteotomy, menisectomy and weight loss

The most important risk factors in OA are ageing and weight gain. While the first factor is unavoidable, something can be done about the latter. The hope here is that by being able to spell out a predicted outcome for a potential OA sufferer, the patient will be better informed about their lifestyle choices, more able to confront the danger and take measures to avoid an outcome which will negatively impact the subject’s quality of life and lead to expensive treatment.

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