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A clinical prediction model for estimating the risk of developing uveitis in patients with juvenile idiopathic

Joeri W van Straalen1, Gabriella Giancane2,3, Yasmine Amazrhar1

  • 1Department of Pediatric Immunology and Rheumatology, Wilhelmina Children's Hospital, Utrecht, Netherlands.

Rheumatology (Oxford, England)
|December 4, 2020
PubMed

Insights

A new model predicts uveitis in children with Juvenile Idiopathic Arthritis (JIA). This tool helps identify children at higher risk for JIA-associated uveitis, aiding early intervention.

Area of Science:

  • Pediatric Rheumatology
  • Ophthalmology
  • Clinical Prediction Modeling

Background:

  • Juvenile Idiopathic Arthritis (JIA) is a chronic autoimmune disease affecting children.
  • Uveitis is a common and serious complication of JIA, potentially leading to vision loss.
  • Accurate prediction of JIA-associated uveitis (JIA-U) is crucial for timely management.

Purpose of the Study:

  • To develop and validate a clinical prediction model for JIA-associated uveitis.
  • To identify key risk factors and protective factors for developing JIA-U in pediatric patients.
  • To create a tool for informing patients and parents about the probability of JIA-U.

Main Methods:

  • Utilized data from the international observational Pharmachild registry.
  • Employed multivariable logistic regression to determine risk factors and predictors for JIA-U.
  • Selected the optimal prediction model using the Akaike information criterion and adjusted for optimism via bootstrap resampling.

Main Results:

  • JIA-U affected 19.9% of 5529 JIA patients.
  • Independent risk factors included ANA positivity (OR: 1.88) and HLA-B27 positivity (OR: 1.48).
  • Older age at JIA onset was a protective factor (OR: 0.84). The final model incorporating age, JIA category, and ANA positivity achieved an optimism-adjusted AUC of 0.75.

Conclusions:

  • A user-friendly prediction model for JIA-U has been developed.
  • This model can assist clinicians in assessing individual patient risk.
  • The tool aims to enhance patient and parental understanding of uveitis probability in JIA.
Abstract

Keywords:
JIAconfoundersprediction modelrisk factorsuveitis

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