Knee tissue lesions and prediction of incident knee osteoarthritis over 7 years in a cohort of persons at higher risk

Abstract

Objective: 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.

Design: 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).

Results: 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.

Conclusions: 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.

Keywords: Epidemiology; Knee osteoarthritis; Magnetic resonance imaging; Risk factors.

Figure 1. Derivation of Analysis Sample

Among 1114 eligible persons, 77 missed the 48-month evaluation [withdrew (28), difficulty scheduling (32), died (14), health problems (2), caregiving responsibilities (1)], 176 attended without MRI, and 12 had inadequate images. We assessed knee MR images (1 knee/person) in the remaining 849 individuals, of whom 8 were excluded for missing covariable data. Although this study utilized the 12-month MRI data, MRIs at 12- and 48-month follow-up were required. While MRI acquisition was included in the core funding of the OAI, MRI readings were not. We successfully applied for funding for an ancillary study that had additional goals which required the 48-month reading. Reading data are therefore not available in those missing the 48-month MRI. In any case, including these participants would only have added 8 additional cases of incident KL ≥ 2.

Figure 2. Receiver Operating Characteristic Curves for Models to Predict Radiographic Knee OA Defined as KL≥2

The figure shows receiver operating characteristic (ROC) curves for two prediction models (to predict KL≥2). In each figure, the dotted line represents the model including age, gender, overweight, obesity, hand OA, knee injury, knee surgery, LIFT, SQUAT, and WOMAC Pain. The solid line in Figure 2A represents the model including age, gender, overweight, obesity, hand OA, knee injury, knee surgery, LIFT, SQUAT, WOMAC Pain + number of subregions with cartilage damage. The solid line in Figure 2B represents the model including age, gender, overweight, obesity, hand OA, knee injury, knee surgery, LIFT, SQUAT, WOMAC Pain + sum of lesion types. (n=841 persons, 1 knee/person) (AUC=area under the curve; CI=confidence interval)