Trajectories of femorotibial cartilage thickness among persons with or at risk of knee osteoarthritis: development of a prediction model to identify progressors

Objective: There is significant variability in the trajectory of structural progression across people with knee osteoarthritis (OA). We aimed to identify distinct trajectories of femorotibial cartilage thickness over 2 years and develop a prediction model to identify individuals experiencing progressive cartilage loss. Methods: We analysed data from the Osteoarthritis Initiative (OAI) (n = 1,014). Latent class growth analysis (LCGA) was used to identify trajectories of medial femorotibial cartilage thickness assessed on magnetic resonance imaging (MRI) at baseline, 1 and 2 years. Baseline characteristics were compared between trajectory-based subgroups and a prediction model was developed including those with frequent knee symptoms at baseline (n = 686). To examine clinical relevance of the trajectories, we assessed their association with concurrent changes in knee pain and incidence of total knee replacement (TKR) over 4 years. Results: The optimal model identified three distinct trajectories: (1) stable (87.7% of the population, mean change −0.08 mm, SD 0.19); (2) moderate cartilage loss (10.0%, −0.75 mm, SD 0.16) and (3) substantial cartilage loss (2.2%, −1.38 mm, SD 0.23). Higher Western Ontario & McMaster Universities Osteoarthritis Index (WOMAC) pain scores, family history of TKR, obesity, radiographic medial joint space narrowing (JSN) ≥1 and pain duration ≤1 year were predictive of belonging to either the moderate or substantial cartilage loss trajectory [area under the curve (AUC) 0.79, 95% confidence interval (CI) 0.74, 0.84]. The two progression trajectories combined were associated with pain progression (OR 1.99, 95% CI 1.34, 2.97) and incidence of TKR (OR 4.34, 1.62, 11.62). Conclusions: A minority of individuals follow a progressive cartilage loss trajectory which was strongly associated with poorer clinical outcomes. If externally validated, the prediction model may help to select individuals who may benefit from cartilage-targeted therapies.