Abstract
BackgroundValidated clinical prediction models to identify children with poor prognosis at the time of juvenile idiopathic arthritis (JIA) diagnosis would be very helpful for tailoring treatments, and avoiding under- or over-treatment. Our objective was to externally validate Nordic clinical prediction models in Canadian patients with JIA.MethodsWe used data from 513 subjects at the 3-year follow-up from the Research in Arthritis in Canadian Children emphasizing Outcomes (ReACCh-Out) cohort. The predicted outcomes were non-achievement of remission, severe disease course, and functional disability. The Nordic models were evaluated exactly as published and after fine-tuning the logistic regression coefficients using multiple data splits of the Canadian cohort. Missing data was handled with multiple imputation, and prediction ability was assessed with C-indices. C-index values > 0.7 were deemed to reflect helpful prediction.ResultsOverall, 81% of evaluable patients did not achieve remission off medications, 15% experienced a severe disease course, and 38% reported disability (CHAQ score > 0). The Nordic model for predicting non-achievement of remission had a C-index of 0.68 (95% CI 0.62–0.74), and 0.74 (0.67–0.80) after fine-tuning. For prediction of severe disease course, it had a C-index of 0.69 (0.61–0.78), and 0.79 (0.68–0.91) after fine-tuning. The fine-tuned Nordic model identified 85% of the cohort as low risk for a severe disease course (< 20% chance) and 7% as high risk (> 60% chance). The Nordic model to predict functional disability had a C-index of 0.57 (0.50–0.63), and 0.51 (0.39–0.63) after fine-tuning.ConclusionsFine-tuned Nordic models, combining active joint count, physician global assessment of disease activity, morning stiffness, and ankle involvement, predicted well non-achievement of remission and severe disease course in Canadian patients with JIA. The Nordic model for predicting disability could not predict functional disability in Canadian patients.
Highlights
Validated clinical prediction models to identify children with poor prognosis at the time of juvenile idiopathic arthritis (JIA) diagnosis would be very helpful for tailoring treatments, and avoiding under- or overtreatment
Similar to what was reported in the Nordic cohort [8], we looked at the performance of prediction models that excluded the laboratory variables from the prediction model
The cohorts are similar to each other and to other inception cohorts of JIA reported in western populations
Summary
Validated clinical prediction models to identify children with poor prognosis at the time of juvenile idiopathic arthritis (JIA) diagnosis would be very helpful for tailoring treatments, and avoiding under- or overtreatment. Validated clinical prediction models to identify children with poor prognosis at diagnosis would be very helpful for tailoring aggressive treatments, such as synthetic and/or biologic DMARDS prescribed shortly after diagnosis, to patients with poor prognosis and prevent under- or over-treatment. Good practices for development of clinical prediction models and consensus statements for reporting these studies are available [4, 5]. Their discrimination accuracy is often assessed with the C-index, equivalent to the area under the Receiver Operating Characteristic curve (AUC), where 1.0 reflects perfect prediction and 0.5 reflects chance alone. Prediction models with Cindex values > 0.7 are considered helpful and those with values > 0.8 are considered excellent [6]
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