Simple computational models may help predict post-traumatic osteoarthritis

The researchers have developed a method to predict post-traumatic osteoarthritis in patients with ligament ruptures using a simplified computational model.

The study published in the journal -- Clinical Biomechanics -- also verified the model predictions against measured structural and compositional changes in the knee joint between follow-up times.

Knee joint injuries such as ligament rupture are common among athletes. As the intact joint ligaments offer a precondition for joint stability, ligament injuries are often surgically reconstructed. However, in many cases, these injuries or surgeries can lead to post-traumatic osteoarthritis.

The articular cartilage, which serves to provide frictionless contact between bones, wears out completely, causing severe joint pain, lack of mobility and even social isolation. Currently, preventing the onset and development of osteoarthritis is still the best clinical course of action.

In this proof-of-concept study, computational models were generated from patient clinical magnetic resonance images and measured motion. Articular cartilage was assumed to degenerate due to excessive tissue stresses, leading to collagen fibril degeneration, or excessive deformations, causing proteoglycan loss. These predictions were then compared against changes in MRI-specific parameters linked to each degeneration mechanism.

"Our results suggest that a relatively simple finite element model, in terms of geometry, motion, and materials, can identify areas susceptible to osteoarthritis, in line with measured changes in the knee joint from MRI. Such methods would be particularly useful in assessing the effect of surgical interventions or in evaluating non-surgical management options for avoiding or delaying osteoarthritis onset and/or progression," said researcher Paul Bolcos, a PhD student at the University of Eastern Finland.

The findings are significant and could provide pathways for patient-specific clinical evaluation of osteoarthritis risks and reveal optimal and individual rehabilitation protocols.

"We are currently working on adding more patients in order to help tune the degeneration parameters and ensure the sensitivity of the mechanical to MRI parameters. Later, this method could be combined with a fully automated approach for generating these computational models developed in our group, narrowing the gap between research and clinical application," added Bolcos.