Abstract
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.
Highlights
Edited by: Massimo Gadina, National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS), United States
Humans have an innate desire to observe and subsequently dissect an event into component pieces in an effort to better characterize the event. We examine these pieces individually and in combinations using this information to determine the outcome of future similar events and the likelihood of their recurrence
Multivariate statistical methods are integral to the analysis of these biologic networks and are vital tools in the effort to discover biomarkers that are predictive of disease activity, severity, and response to therapeutic interventions
Summary
Predictive and prognostic models are statistical tools that predict a clinical outcome determined by at least 2 points of patient data and ideally more with novel statistical models which take into account change over time [2, 3]. Van Dijkhuizen’s group, 35 years later, reports employing a Bayesian model of disease activity utilizing four continuous outcome variables to stratify patients by disease activity and allow for more sign/symptom-specific treatment based on these variables. These measures assess various domains of the JIIMs including global disease activity, muscle strength, physical function, and quality of life (Table 1) [14]. Van Dijkhuizen’s et al utilized data from the UK Juvenile dermatomyositis cohort and biomarker study (JDCBS) in which data were analyzed using a Bayesian model to develop a model of disease activity [21]. They identified signs and symptoms that associated with four outcome parameters. Visual Analog Scale (VAS) or Likert scale Visual Analog Scale (VAS) or Likert scale 10 items scored dichotomously, 3 polychotomously; DAS skin (range 0–9) and muscle (range 0–11) scores Combined tool: VAS for each organ (MYOACT) and polychotomous response (MITAX)
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