Role of magnetic resonance imaging in classifying individuals who will develop accelerated radiographic knee osteoarthritis

Lori Lyn Price, Matthew S. Harkey, Robert J. Ward, James Mackay, Ming Zhang, Jincheng Pang, Julie E. Davis, Timothy E. McAlindon, Grace H. Lo, Mamta Amin, Charles B. Eaton, Bing Lu, Jeffrey Duryea, Mary F. Barbe, Jeffrey B. Driban

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We assessed whether adding magnetic resonance (MR)‐based features to a base model of clinically accessible participant characteristics (i.e., serological, radiographic, demographic, symptoms, and physical function) improved classification of adults who developed accelerated radiographic knee osteoarthritis (AKOA) or not over the subsequent 4 years. We conducted a case–control study using radiographs from baseline and the first four annual visits of the osteoarthritis initiative to define groups. Eligible individuals had no radiographic KOA in either knee at baseline (Kellgren–Lawrence [KL] grade <2). We classified two groups matched on sex (i) AKOA: at least one knee developed advanced‐stage KOA (KL = 3 or 4) within 48 months and (ii) did not develop AKOA within 48 months. The MR‐based features were assessments of bone, effusion/synovitis, tendons, ligaments, cartilage, and menisci. All characteristics and MR‐based features were from the baseline visit. Classification and regression tree analyses were performed to determine classification rules and identify statistically important variables. The CART models with and without MR features each explained approximately 40% of the variability. Adding MR‐based features to the model yielded modest improvements in specificity (0.90 vs. 0.82) but lower sensitivity (0.62 vs. 0.70) than the base model. There was consistent evidence that serum glucose, effusion‐synovitis volume, and cruciate ligament degeneration are statistically important variables in classifying individuals who will develop AKOA. We found common MR‐based measures failed to dramatically improve classification. These findings also show a complex interplay among participant characteristics and a need to identify novel characteristics to improve classification. © 2019 Orthopaedic Research Society.
Original languageEnglish
Pages (from-to)2420-2428
Number of pages9
JournalJournal of Orthopaedic Research
Issue number11
Early online date12 Jul 2019
Publication statusPublished - Nov 2019

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