Predictive Models of Clinical Improvement in Rituximab-Treated Myositis Patients Using Clinical Features, Autoantibodies, and Biomarkers

Background: Response to rituximab so far is unpredictable in patients with refractory myositis. Predictive models of clinical improvement are developed using clinical, laboratory, and gene expression/cytokine/chemokine variables in rituximab-treated refractory myositis patients. Methods: We analyzed data for 200 myositis patients (76 with adult polymyositis (PM), 76 with adult dermatomyositis (DM), and 48 with juvenile (DM)) in the rituximab in myositis trial. Clinical improvement is defined as the change from baseline to 24 weeks in Physician Global Visual Analog Scale (VAS). We analyze the association of baseline variables with improvements: demographics, myositis subtype, clinical and laboratory parameters, autoantibody status, and interferon (IFN)- regulated chemokines. Multivariable linear regression models are developed by using stepwise variable selection methods. Results: A “base” multivariable model to predict improvement with clinical and laboratory variablesonly is built with modest predictive ability (adjusted R2 = 0.21). This model includes two significant factors at baseline: Physician Global VAS and Muscle Disease Activity VAS. A “final” multivariable model to predict improvement including non-standard laboratory measures is developed and demonstrated better predictive ability (adjusted R2 = 0.32). This model includes Physician Global VAS, IFN chemokine score and IL-2 levels. The “final” model explained 11% more variability than the “base” model. Conclusions: Changes in disease activity over time following treatment with rituximab in refractory myositis can be predicted. These models can be clinically useful to optimize treatment selection in myositis.