New Osteoarthritis Algorithm Based on Cartilage Degeneration


The disease progression prediction tool could facilitate clinical decision-making and delay total knee replacement surgery.

A novel cartilage degeneration algorithm can predict osteoarthritis progression in individual patients and could facilitate clinical decision-making in treatment, according to a recent study.

University of Eastern Finland researchers tested the ability of a cartilage degeneration algorithm that they created earlier to predict osteoarthritis progression in individual patients and to grade the severity of their disease using the Kellgren-Lawrence classification.

The investigators applied the algorithm to 21 patients divided into 3 groups-those who did not have osteoarthritis, those who had mild osteoarthritis, and those with severe osteoarthritis-based on their Kellgren-Lawrence grades defined experimentally after a 4-year follow-up. The group without osteoarthritis represented normal weight subjects; the osteoarthritis groups represented overweight subjects. All of the patients were osteoarthritis–free at the start of the follow-up.

The algorithm was applied at the onset of the follow-up, and the findings were compared against the 4-year follow-up data. Based on the prognosis from the simulation and the experimentally defined Kellgren-Lawrence grades 4 years later, the researchers found that the algorithm was able to categorize patients into their correct groups.

The degeneration algorithm is based on stresses the knee joint experiences during walking, which were simulated on a computer. The algorithm assumes that stresses that exceed a certain threshold during walking will cause local degeneration in the knee’s articular cartilage.

The algorithm was able to predict osteoarthritis progression similarly with the experimental follow-up data and separate subjects with radiographic osteoarthritis (Kellgren-Lawrence grade 2 and 3) from healthy subjects.

Maximum degeneration and degenerated volumes within cartilage were significantly higher in osteoarthritis compared with healthy subjects; the grade 3 group showed the highest degeneration values.

The algorithm was able to show higher degeneration levels for the grade 3 group than the grade 2 group, even though the groups had the same body mass index, the researchers noted.

“These results highlight the potential of our degeneration algorithm to predict the subject-specific overloading-related progression of the knee OA,” they said. “KL grading method estimates the degree of OA mainly based on the amount of osteophytes and joint space narrowing and cannot reveal any changes in cartilage. Our degeneration algorithm predicts directly cartilage degeneration, not bony changes or cartilage loss.”

Because the degeneration algorithm was able to predict cartilage degeneration for obese subjects and separate groups with different levels of osteoarthritis, it provides a novel tool to predict quantitatively the subject-specific levels of cartilage degenerations and estimate progression of osteoarthritis, the authors stated. “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.”

The algorithm could be applied as a clinical tool to improve personalized osteoarthritis treatment planning by “optimizing” the intervention, the researchers concluded. “This could decelerate or prevent the progression of OA and delay the need for total knee replacement surgery."

The findings were published in Scientific Reports.


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." Sci Rep. 2017 Aug 23;7:9177. doi: 10.1038/s41598-017-09013-7.

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