Algorithm Could Aid Decision Making By Predicting Osteoarthritis Progression

A novel cartilage degeneration algorithm could be used to predict individual-specific osteoarthritis (OA) progression and assist with clinical decision-making, according to a recent study.

Despite the considerable costs associated with OA, no clinical tools for the prediction of individual-specific progression currently exist. In their study, the researchers tested the ability of a novel cartilage degeneration algorithm, based upon tissue overloading, to separate individuals into different Kellgren-Lawrence (KL) grade groups based upon predicted level of cartilage degeneration.
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The algorithm was applied to 21 patients with radiographically intact knee joints at baseline who were divided into 3 groups: those without OA, those with mild OA, and those with severe OA, based upon 4-year follow-up KL grades.

After comparing simulated cartilage degenerations with cartilage degenerations obtained from radiographic KL grading and MRI scoring after 4 years of follow-up, the researchers found that they were able to successfully separate individuals with varying levels of radiographical OA from healthy individuals, and to differentiate between KL3 and KL2 groups.

“Our degeneration algorithm was able to predict cartilage degeneration for obese subjects and separate groups with different levels of OA. Thus, it provides a novel tool to predict quantitatively the subject-specific levels of cartilage degenerations and estimate progression of OA,” the researchers concluded.

“By using this approach, effects of different interventions, such as weight loss, rehabilitation, and surgery, on OA progression could be simulated in a personalized medicine manner.”

—Michael Potts

Reference:

Liukkonen MK, Mononen ME, Klets O, et al. Simulation of subject-specific progression of knee osteoarthritis and comparison to experimental follow-up data: data from the osteoarthritis initiative [published online August 23, 2017].