Test-retest precision and longitudinal cartilage thickness loss in the IMI-APPROACH cohort
W Wirth, S Maschek, A C A Marijnissen, A Lalande, F J Blanco, F Berenbaum, L A van de Stadt, M Kloppenburg, I K Haugen, C H Ladel, J Bacardit, A Wisser, F Eckstein, F W Roemer, F P J G Lafeber, H H Weinans, M Jansen, W Wirth, S Maschek, A C A Marijnissen, A Lalande, F J Blanco, F Berenbaum, L A van de Stadt, M Kloppenburg, I K Haugen, C H Ladel, J Bacardit, A Wisser, F Eckstein, F W Roemer, F P J G Lafeber, H H Weinans, M Jansen
Abstract
Objective: To investigate 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 in the IMI-APPROACH cohort - an exploratory, 5-center, 2-year prospective follow-up cohort.
Design: Quantitative cartilage morphology at baseline and at least one follow-up visit was available for 270 of the 297 IMI-APPROACH participants (78% females, age: 66.4 ± 7.1 years, body mass index (BMI): 28.1 ± 5.3 kg/m2, 55% with radiographic knee osteoarthritis (OA)) from 1.5T or 3T MRI. Test-retest precision (root mean square coefficient of variation) was assessed from 34 participants. To define progressor knees, smallest detectable change (SDC) thresholds were computed from 11 participants with longitudinal test-retest scans. Binary logistic regression was used to evaluate the odds of progression in femorotibial cartilage thickness (threshold: -211 μm) for the quartile with the highest vs the quartile with the lowest s-scores.
Results: The test-retest precision was 69 μm for the entire femorotibial joint. Over 24 months, mean cartilage thickness loss in the entire femorotibial joint reached -174 μm (95% CI: [-207, -141] μm, 32.7% with progression). The s-score was not associated with 24-month progression rates by MRI (OR: 1.30, 95% CI: [0.52, 3.28]).
Conclusion: IMI-APPROACH successfully enrolled participants with substantial cartilage thickness loss, although the machine-learning-estimated s-score was not observed to be predictive of cartilage thickness loss. IMI-APPROACH data will be used in subsequent analyses to evaluate the impact of clinical, imaging, biomechanical and biochemical biomarkers on cartilage thickness loss and to refine the machine-learning-based s-score.
Gov identification: NCT03883568.
Keywords: Cartilage loss; Knee; MRI; Osteoarthritis; Progression; Test–retest precision.
Copyright © 2022 The Author(s). Published by Elsevier Ltd.. All rights reserved.
Source: PubMed