The following is a summary of the “Test–retest precision and longitudinal cartilage thickness loss in the IMI-APPROACH cohort,” published in the February 2023 issue of Osteoarthritis and Cartilage by Wirth, et al.
The purpose of this study, which was conducted on the IMI-APPROACH cohort (an exploratory, 5-center, 2-year prospective follow-up cohort), was to examine the test-retest precision and to report the longitudinal change in cartilage thickness, the percentage of knees with progression, and the predictive value of the machine-learning estimated structural progression score (s-score) for cartilage thickness loss. From 1.5T or 3T MRI, 270 of the 297 IMI-APPROACH individuals had quantitative cartilage morphology at baseline and at least 1 follow-up visit (78% females, age: 66.4 ±7.1 years, body mass index (BMI): 28.1± 5.3 kg/m<sup style=”vertical-align: sup;”>2</sup>, 55% with radiographic knee OA).
A total of 34 people had their test-retest reliability (root-mean-square coefficient of variance) measured. Eleven subjects with longitudinal test-retest scans were used to calculate smallest detectable change (SDC) criteria, which were then used to define progressor knees. To compare the likelihood of advancement in femoralotibial cartilage thickness (threshold: -211 μm) between the quartiles with the highest and lowest s-scores, a binary logistic regression analysis was conducted. Overall, the femorotibial joint had a test-retest precision of 69 μm.
As time went on, the femorotibial joint saw a mean cartilage thickness decrease of 174 μm (95% CI: [-207, -141] m, 32.7%). There was no correlation between the s-score and MRI-determined 24-month progression rates (OR = 1.30, 95% CI = [0.52, 3.28]). Despite the fact that the estimated s-score from machine learning was not found to be predictive of cartilage thickness loss, IMI-APPROACH was able to enroll patients with significant cartilage loss. The IMI-APPROACH dataset will be used in future studies to assess the role of clinical, imaging, biomechanical, and biochemical biomarkers in cartilage thickness loss and to fine-tune the s-score via machine learning.