Tool for osteoarthritis risk prediction (TOARP) over 8 years using baseline clinical data, X-ray, and MRI: Data from the osteoarthritis initiative.

Abstract

Osteoarthritis (OA), a multifactorial disease causing joint degeneration, often leads to severe disability. The rising rates of disability highlight the need for implementing preventative measures at early stages of the disease, which would especially benefit subjects at high risk for OA development. To develop a risk prediction tool for moderate-severe OA (TOARP) over 8 years based on subject characteristics, knee radiographs, and MRI data at baseline using data from the Osteoarthritis Initiative (OAI). Retrospective. 641 subjects with no/mild radiographic OA (Kellgren-Lawrence [KL] 0-2) and no clinically significant symptoms (Western Ontario and McMaster Universities Arthritis Index [WOMAC] 0-1) were selected from the OAI. MR images were obtained using 3.0T. Compartment-specific cartilage and meniscus morphology and cartilage T2 were assessed. Baseline subject demographics, risk factors, KL score, cartilage WORMS score, presence of meniscus tear, and cartilage T2 were used to predict the development of moderate/severe OA (KL = 3-4 or WOMAC pain ≥5 or total knee replacement [TKR]) over 8 years. Best subsets variable selection followed by cross-validation were used to assess which combinations of variables best predict moderate/severe OA. Model 1 included KL score, previous knee injury in the last 12 months, age, gender, and BMI. Model 2 included all variables in Model 1 plus presence of cartilage defects in the lateral femur and patella, and presence of a meniscal tear. Model 3 included all variables in Models 1 and 2, plus cartilage T2 in the medial tibia and medial femur. Compared to Model 1 (cross-validated AUC = 0.67), Model 3 performed significantly better (AUC = 0.72, P = 0.04), while Model 2 showed a statistical trend (AUC = 0.71, P = 0.08). We established a risk calculator for the development of moderate/severe knee OA over 8 years that includes radiographic and MRI data. The inclusion of MRI-based morphological abnormalities and cartilage T2 significantly improved model performance. 2 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2018;47:1517-1526.