Osteoarthritis of the knee is the bane of many worker’s compensation claims. Frequently, an injured worker demonstrates evidence of arthritis but claims an acute event aggravated the condition to the point that it was symptomatic. What can be frustrating for the employer or insurance carrier is the fact that in most cases the arthritis itself was not caused by the employment. Thus, the employer or insurance carrier laments the fact that they are being held responsible for 100% of a condition that would have almost certainly become symptomatic regardless of the acute work-related event. Unfortunately, to date there has been no reliable method to measure whether this is accurate or, if so, when the arthritis would have become symptomatic. However, a recent study suggest we may have technology to do just that.
A group of Finnish and Swedish researchers released “A Novel Method to Simulate the Progression of Collagen Degeneration of Cartilage in the Knee” in which they developed a “degeneration algorithm … combined with computational modeling” that accurately predicted the rate at which knee cartilage would deteriorate based on the weight of patients. The study was conducted on 429 patients under 65 years of age who initially had no radiographic evidence of osteoarthritis (cartilage thinning) in their knees. The subjects were divided into a study group, consisting of patients with a BMI of 35 or higher, considered to be at high risk of developing weight-related osteoarthritis of the knee, and a control group, consisting of patients with a BMI lower than 25, considered to be of low risk for developing weight-related osteoarthritis of the knee. As noted, neither group demonstrated radiographic evidence of osteoarthritis at the beginning of the study nor had any member of either group sustained a knee injury that either prevented them from walking for more than 2 days or required surgical intervention. The 2 groups were then followed for 4 years.
The researchers developed an algorithm to predict the rate at which cartilage loss associated with osteoarthritis would occur based on BMI and other physiological characteristics. In their words, “[t]he algorithm was based on cartilage overloading so that cumulatively accumulated excessive stresses (above failure limit) caused alterations in tissue properties with time.” The researchers then developed a computational program to simulate the expected cartilage loss over four years based on the baseline status of knee cartilage from MRI readings. The accuracy of the algorithm and computational modeling was measured against x-rays taken at the beginning of the study and after 4 years. The results demonstrated the ability of the algorithm and modeling to accurately predict which subjects would experience loss of cartilage associate with osteoarthritis and how much loss each subject would experience. According the study, “[t]he simulated onset and development of osteoarthritis agreed with experimental baseline and 4-year follow-up data.” This lead researchers to conclude that, “[t]he present work provides…an important and groundbreaking step toward developing a rapid and subject-specific diagnostic tool for the simulations of the onset and development of knee osteoarthritis and cartilage degeneration related to excessive chronic overloading due to overweight [sic].”
The implications of this individualized ability to predict the onset of knee arthritis could be significant for worker’s compensation. If the method proves to be accurate and reliable in subsequent studies, it could be used in the same manner as occupational hearing tests to measure a baseline condition and determine if the subsequent condition is related to the normal progression of the disease versus an occupational aggravation. Likewise, the method would offer the potential to calculate, based on an employee’s physical condition on the date of injury, the likelihood that the alleged employment-related event aggravated the underlying arthritis and if so how much the employment-related event is responsible for. This would finally allow for an accurate accounting of what portion of a preexisting degenerative condition is related to an industrial event and what portion is due to the natural progression of the condition. While this would admittedly be at best a distant possibility, it is nevertheless a possibility. Hence, the study and its future applications are worth following.
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