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

Olazagasti, Jeannette M.; Crowson, Cynthia S.; Hein, Molly S.; Padilla, Consuelo Lopez de; Aggarwal, Rohit; Oddis, Chester V.; Reed, Ann M.

doi:10.4236/ojra.2015.53012

Cited by 1 publication

(1 citation statement)

References 19 publications

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“…Reed et al found that biomarker signatures involving type-1 interferon regulated and other proinflammatory chemokines and cytokines in conjunction with autoantibodies predicted response to rituximab in patients with refractory myositis (45). Furthermore, Olazagasti et al found that adding gene expression, cytokine and chemokine data to clinical and standard laboratory assessments improved prediction of response to rituximab in patients with JIIM (62).…”

Section: Autoantibodiesmentioning

confidence: 99%

A Path to Prediction of Outcomes in Juvenile Idiopathic Inflammatory Myopathy

2019

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Humans have an innate desire to observe and subsequently dissect an event into component pieces in an effort to better characterize the event. We then examine these pieces individually and in combinations using this information to determine the outcome of future similar events and the likelihood of their recurrence. Practically, this attempt to foretell an occurrence and predict its outcomes is evident in multiple disciplines ranging from meteorology to sociologic studies. In this manuscript we share the historical and present-day tools to predict course and outcome in juvenile idiopathic inflammatory myopathy including clinical features, testing, and biomarkers. Further we discuss considerations for building more complex predictive models of outcome especially in diseases such as juvenile idiopathic inflammatory myopathy where patients numbers are low. Many of the barriers to developing risk prediction models for juvenile idiopathic inflammatory myopathy outcomes have improved with many remaining challenges being addressed.

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Section: Autoantibodiesmentioning

confidence: 99%