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

Summary 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.