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Wimbledon Clinics
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September 02, 2019

Predicting post-traumatic osteoarthritis

predicting_ptoa

Researchers have developed a simple computational model that could help predict post-traumatic osteoarthritis (PTOA).

PTOA, also known as secondary arthritis, sometimes develops in a joint after injury or surgery. In fact, joint injury is one of the biggest risk factors for osteoarthritis, with 50% of people with significant knee injuries developing PTOA within 10 years, according to the Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences at Oxford University.

However, while computational modelling can be used to predict locations susceptible to osteoarthritis, they are too complicated for clinical use and lack verification of predictions.

A new study published in Clinical Biomechanics tackles this problem and could lead to patient-specific clinical evaluation of osteoarthritis risks.

Researchers from the University of Eastern Finland, in collaboration with the University of California in San Francisco, Cleveland Clinic, the University of Queensland, the University of Oulu and Kuopio University Hospital, have developed a method to predict PTOA in patients with ligament ruptures using a simplified computational model. The researchers also verified the model predictions against measured structural and compositional changes in the knee joint between follow-up times.

In the 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.

https://www.uef.fi/-/yksinkertaiset-tietokonemallit-voivat-ennustaa-vamman-jalkeisen-nivelrikon-kehittymista

https://www.clinbiomech.com/article/S0268-0033(18)31029-5/fulltext

https://www.ndorms.ox.ac.uk/news/preventing-osteoarthritis-after-injury-first-international-pre-guidelines

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