Among high risk individuals, whether knee lesions in tissues involved in osteoarthritis (OA) can improve prediction of knee OA is unclear. We hypothesized that models predicting (1) incident osteophytes and (2) incident osteophytes and joint space narrowing can be improved by including symptoms or function, and further improved by lesion status.
In Osteoarthritis Initiative (OAI) participants with normal knee X-rays, we assessed cartilage damage, bone marrow lesions (BMLs), and menisci. Cox proportional hazards models were used to develop risk prediction models for risk of each outcome. Nested models (increasingly larger baseline covariable sets) were compared using likelihood ratio tests and Schwarz Bayesian Information Criterion (SBC). Model discrimination used receiver operating characteristic (ROC) curves and area under the curve (AUC).
In 841 participants [age 59.6, body mass index (BMI) 26.7, 55.9% women] over up to 7 years follow-up, each larger set improved prediction (+hand OA, injury, surgery, activities; +symptoms/function). Prediction was further improved by including cartilage damage both compartments, BMLs both compartments, meniscal tear, meniscal extrusion, sum of lesion types, number of subregions with cartilage damage, number of subregions with BMLs, and (concurrently) subregion number with cartilage damage, subregion number with BMLs, and meniscal tear. AUCs were ≥0.80 for both outcomes for number of subregions with cartilage damage and the combined model.
Among persons at higher risk for knee OA with normal X-rays, MRI tissue lesions improved prediction of mild as well as moderate disease. These findings support that disease onset is likely occurring during the "high-risk" period and encourage a reorientation of approach.