Abstract
Purpose: Since osteoarthritis (OA) is a heterogeneous disease in terms of progression, many trials include patients showing barely or no progression, particularly in early stages of the disease. In the APPROACH study, machine learning models were trained to predict structural and pain progression of individual participants before inclusion. Predicted progression was differentiated into a structural (S) and pain (P) progression score, where the score (range 0-1) reflected the likelihood of a participant being a progressor. Structural progression was defined as a decrease in minimum joint space width (JSW) of at least 0.3mm/year. Only participants with the estimated highest 75% combined (P+S) score after screening were included. The purpose of the current study was to evaluate the associations between the predicted and actual structural progression according to different parameters over two years.
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