Clinical prediction models for methotrexate treatment outcomes in patients with rheumatoid arthritis: A systematic review and meta-analysis

Background: In the management of rheumatoid arthritis (RA), there is a clinical need to identify which patients are at high-risk of not responding to methotrexate (MTX), or experiencing adverse events (AEs), to enable earlier alternative treatments. Many clinical prediction models (CPMs) have previously been developed, but a summary of such models and their methodological quality is lacking. This systematic review aimed to (i) identify and summarize previously published CPMs of MTX outcomes in biologic-naive RA patients, and (ii) critically appraise their methodological properties. Methods: Medline and Embase were searched to identify studies developing or validating CPMs of MTX outcomes in RA patients. The risk of bias (ROB) was assessed using PROBAST (prediction model risk of bias assessment tool). A fixed effects meta-analysis summarised discrimination for models with multiple external validations. Results: The systematic review identified 20 CPMs across 13 studies, and 4 validation studies. Three outcome types were used: a state of disease activity (n = 14, 70%); EULAR response criteria (n = 4, 20%); or discon-tinuation due to AEs (n = 2, 10%). Only one model accounted for potential competing risks, and nine (45%) were internally validated. Eight (40%) models used multiple imputation for missing data, others were often limited to complete case analysis. There was overall high ROB. The meta-analysis summarised c-statistics for two models with multiple external validations was 0.77 (95% CI: 0.69, 0.84) and 0.68 (0.64, 0.71). Conclusion: This review highlights several methodological shortcomings that should be addressed in future model development to increase potential for implementation into practice.