SAT0628 VALIDATION OF METHODS FOR PREDICTING LONG-TERM OUTCOME IN JUVENILE IDIOPATHIC ARTHRITIS: RESULTS FOR CANADIAN AND NORDIC PREDICTION MODELS IN THE NORDIC COHORT. (June 2019)

• Record Type: Journal Article
• Authors: Rypdal, Veronika; Guzman, Jaime; Henrey, Andrew; Loughin, Thomas; Glerup, Mia; Fasth, Anders; Arnstad, Ellen Dalen; Berntson, Lillemor; Nielsen, Susan; Zak, Marek; Rygg, Marite; Aalto, Kristiina; Herlin, Troels; Rypdal, Martin; Nordal, Ellen
• Is Part Of: Annals of the rheumatic diseases. Volume 78(2019)Supplement 2
• Page Start: 1410
• Page End: 1411
• Publication Date: 2019-06
• Subjects: Rheumatism -- Periodicals; 616.723005
• DOI: 10.1136/annrheumdis-2019-eular.2577
• Languages: English
• ISSNs: 0003-4967

Abstract

Abstract : Background: Models predicting outcome in juvenile idiopathic arthritis (JIA) have recently been proposed by Guzman et al . 1 and Rypdal et al. 2 Guzman et al . constructed a model for predicting severe disease course derived from the ReACCh-Out study, and Rypdal et al . constructed models for prediction of non-remission, functional disability and joint damage. Objectives: To validate methods for prediction of long-term outcome in JIA by testing the ability of Guzman's model and Rypdal's model to predict severe disease course (the ReACCh-Out outcome) in the Nordic cohort. Methods: The Nordic cohort is a prospective longitudinal multicenter cohort from defined geographical areas of 4 Nordic countries. Children with a baseline and an 8-year study visit were included. Missing data were imputed using low rank matrix factorization 3, and a K-medoids algorithm 4 was used to identify clusters corresponding to severe disease course in the ReACCh-Out study. With this outcome, the prediction model of Guzman et al . was tested with no re-estimation of parameters. A Receiver operating characteristic (ROC) curve and the corresponding area under the curve (AUC) were computed. For the same outcome, prediction models were built using the method of Rypdal et al . on randomly sampled training sets, and tested on disjoint validation sets. Results: In the Nordic cohort 98/440 (22%) patients were identified with a severe disease course. This ratio is similar to the 125/610 (20%) found in the ReACCh-Out study. Characteristics of groups of patients with severe and non-severe disease course are similar in the two cohorts. The model of Guzman et al . had an AUC of 0.85 for prediction of severe disease course and an AUC of 0.66 for predicting remission off medication. In repeated cross-validations, the model of Rypdal et al . had a median AUC of 0.90 (IQR 0.86-0.92) for prediction of severe disease course, and a median AUC of 0.78 (IQR 0.72–0.82) for remission off medication. Conclusion: Tests in the Nordic cohort validate the ability of the model of Guzman et al . to predict severe disease course. Repeated cross-validations of the model of Rypdal et al . indicate that validation results are highly dependent of the chosen outcome, and that prediction of long-term remission status is more challenging than prediction of a severe disease course. References: [1] Guzman, J. et al., Predicting Which Children with Juvenile Idiopathic Arthritis Will Have a Severe Disease Course: Results from the ReACCh-Out Cohort, J Rheumatol, 44, 230-240, 2017. [2] Rypdal, V. et al., Predicting unfavorable long-term outcome in juvenile idiopathic arthritis: results from the Nordic cohort study, Arthritis Res Ther, 20, 91, 2018. [3] Liu, G. et al., Robust subspace segmentation by low-rank representation, Proceedings of the 27th International Conference on International Conference on Machine Learning, 663-670, 2010. [4] Park, H.-S. et al., A simple and fast algorithm for K-medoids clustering, Expert Systems with Applications, 36, 3336-3341, 2009. Disclosure of Interests: Veronika Rypdal: None declared, Jaime Guzman: None declared, Andrew Henrey: None declared, Thomas Loughin: None declared, Mia Glerup: None declared, Anders Fasth: None declared, Ellen Dalen Arnstad: None declared, Lillemor Berntson Consultant for: AbbVie, Speakers bureau: AbbVie, Susan Nielsen: None declared, Marek Zak: None declared, Marite Rygg: None declared, Kristiina Aalto: None declared, Troels Herlin: None declared, Martin Rypdal: None declared, Ellen Nordal: None declared