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Predicting Which Children with Juvenile Idiopathic Arthritis Will Not Attain Early Remission with Conventional

Jaime Guzman1,2,3, Andrew Henrey4,5,6, Thomas Loughin4,5,6

  • 1From the British Columbia Children's Hospital and the University of British Columbia, Vancouver; Simon Fraser University, Burnaby, British Columbia; London Health Sciences Centre and Western University, London; Children's Hospital of Eastern Ontario and University of Ottawa, Ottawa, Ontario; IWK Health Centre and Dalhousie University, Halifax, Nova Scotia; Winnipeg Children's Hospital and University of Manitoba, Winnipeg, Manitoba; Hospital for Sick Children and University of Toronto, Toronto, Ontario; McGill University Health Centre and McGill University, Montreal, Quebec; Janeway Children's Health and Rehabilitation Centre and Memorial University, Saint John's, Newfoundland and Labrador; Royal University Hospital and University of Saskatchewan, Saskatoon, Saskatchewan; Centre Hospitalier Universitaire Sainte-Justine and Université de Montréal, Montreal; Centre Hospitalier Universitaire de Sherbrooke and Université de Sherbrooke, Sherbrooke, Quebec; Alberta Children's Hospital and University of Calgary, Alberta, Canada; Shands Children's Hospital and University of Florida, Gainesville, Florida, USA. jguzman@cw.bc.ca.

The Journal of Rheumatology
|January 17, 2019
PubMed

Insights

Predicting early remission in juvenile idiopathic arthritis (JIA) is crucial. A new model identifies more children with low remission chances, potentially guiding early biologic or triple disease-modifying antirheumatic drug (DMARD) treatment.

Area of Science:

  • Pediatric Rheumatology
  • Clinical Prediction Modeling

Background:

  • Juvenile idiopathic arthritis (JIA) management requires predicting treatment response.
  • Identifying children unlikely to achieve remission with conventional therapy is key for optimizing treatment strategies.

Purpose of the Study:

  • To estimate the probability of early remission in children with JIA using conventional treatment.
  • To identify candidates for upfront biologic or triple disease-modifying antirheumatic drug (DMARD) therapy based on low remission probability.

Main Methods:

  • Utilized data from 1074 children in the Research in Arthritis in Canadian Children emphasizing Outcomes (ReACCh-Out) cohort.
  • Developed and tested Cox logistic regression models on random splits of the cohort to predict clinically inactive disease within one year.
  • Evaluated model performance using c-index and compared predictions with observed remission rates.

Main Results:

  • The best model, incorporating 18 clinical variables, achieved a c-index of 0.69, outperforming JIA category alone (0.59).
  • The model identified 14% of subjects with a low chance of remission (<0.25 probability), of whom 77% did not achieve remission.
  • Children in the lowest probability decile had a 20% chance of remission, while those in the highest had a 69% chance.

Conclusions:

  • The developed model, while not meeting the c-index > 0.70 threshold, significantly improved identification of JIA patients with a low probability of early remission compared to JIA category alone.
  • This model can serve as a benchmark for future research incorporating laboratory or imaging biomarkers to enhance JIA treatment decisions.
Abstract

Keywords:
COHORT STUDIESJUVENILE IDIOPATHIC ARTHRITISPREDICTIONPROGNOSISRISK STRATIFICATION

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