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Predicting Which Children with Juvenile Idiopathic Arthritis Will Have a Severe Disease Course: Results from the
Jaime Guzman1,2, Andrew Henrey3,4, Thomas Loughin3,4
1From the British Columbia Children's Hospital and the University of British Columbia, Vancouver; Simon Fraser University, Burnaby, British Columbia; the Alberta Children's Hospital and University of Calgary, Calgary, Alberta; London Health Sciences Centre and Western University, London; the Children's Hospital of Eastern Ontario and University of Ottawa, Ottawa, Ontario, Canada; the Shands Children's Hospital and University of Florida, Gainesville, Florida, USA. jguzman@cw.bc.ca.
Insights
Researchers identified four distinct juvenile idiopathic arthritis (JIA) disease courses in children. Early diagnosis information can predict the probability of a severe JIA disease course.
Area of Science:
- Pediatric Rheumatology
- Clinical Epidemiology
- Disease Trajectory Research
Background:
- Juvenile idiopathic arthritis (JIA) presents heterogeneous clinical courses.
- Predicting disease severity is crucial for timely intervention in pediatric rheumatology.
- Understanding disease trajectories aids in personalized treatment strategies for JIA patients.
Purpose of the Study:
- To identify distinct disease courses in a juvenile idiopathic arthritis (JIA) inception cohort over five years.
- To develop a predictive model for estimating the probability of a severe JIA disease course at the time of diagnosis.
Main Methods:
- Longitudinal study of children with JIA, with assessments over 60 months.
- Multivariable cluster analysis used to define disease courses based on key variables.
- Logistic regression model developed to predict severe disease course using baseline data.
Main Results:
- Four distinct JIA disease courses were identified: Mild, Moderate, Severe Controlled, and Severe Persisting.
- A predictive model incorporating JIA category, active joint count, and joint involvement pattern accurately estimated severe disease risk (c-index = 0.87).
- High-risk children (highest decile) had a 91% probability of experiencing a severe disease course, versus 5% in the lowest decile.
Conclusions:
- Children with JIA follow one of four distinct disease trajectories.
- Predictive models using initial diagnostic information can reliably estimate the likelihood of a severe JIA disease course.
Objective:
We studied an inception cohort of children with juvenile idiopathic arthritis (JIA) to (1) identify distinct disease courses based on changes over 5 years in 5 variables prioritized by patients, parents, and clinicians; and (2) estimate the probability of a severe disease course for each child at diagnosis.
Methods:
Assessments of quality of life, pain, medication requirements, patient-reported side effects, and active joint counts were scheduled at 0, 6, 12, 18, 24, 36, 48, and 60 months. Patients who attended at least 6 assessments were included. Multivariable cluster analysis, r2, and silhouette statistics were used to identify distinct disease courses. One hundred candidate prediction models were developed in random samples of 75% of the cohort; their reliability and accuracy were tested in the 25% not used in their development.
Results:
Four distinct courses were identified in 609 subjects. They differed in prioritized variables, disability scores, and probabilities of attaining inactive disease and remission. We named them Mild (43.8% of children), Moderate (35.6%), Severe Controlled (9%), and Severe Persisting (11.5%). A logistic regression model using JIA category, active joint count, and pattern of joint involvement at enrollment best predicted a severe disease course (Controlled + Persisting, c-index = 0.87); 91% of children in the highest decile of risk actually experienced a severe disease course, compared to 5% of those in the lowest decile.
Conclusion:
Children in this JIA cohort followed 1 of 4 disease courses and the probability of a severe disease course could be estimated with information available at diagnosis.
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