Matrix-based risk models have been proposed as a tool to predict rapid radiographic progression (RRP) in rheumatoid arthritis (RA), but the experience with such models is limited. We tested the performance of 3 risk models for RRP in an observational cohort.
Subjects from an observational RA cohort with hand radiographs and necessary predictor variables to be classified by the risk models were identified (n = 478). RRP was defined as a yearly change in the Sharp/van der Heijde score of ≥5 units. Patients were placed in the appropriate matrix categories, with a corresponding predicted risk of RRP. The mean predicted probability for cases and noncases, integrated discrimination improvement, Hosmer-Lemeshow statistics, and C statistics were calculated.
The median age was 59 years (interquartile range [IQR] 50–66 years), the median disease duration was 12 years (IQR 4–23 years), the median swollen joint count was 6 (IQR 2–13), 84% were women, and 86% had erosions at baseline. Twelve percent of patients (32 of 271) treated with synthetic disease-modifying antirheumatic drugs (DMARDs) at baseline and 10% of patients (21 of 207) treated with biologic DMARDs experienced RRP. Most of the predictor variables had a skewed distribution in the population. All models had a suboptimal performance when applied to this cohort, with C statistics of 0.59 (model A), 0.65 (model B), and 0.57 (model C), and Hosmer-Lemeshow chi-square P values of 0.06 (model A), 0.005 (model B), and 0.05 (model C).
Matrix risk models developed in clinical trials of patients with early RA had limited ability to predict RRP in this observational cohort of RA patients.