In Rheumatology (Oxford, England)
OBJECTIVES : Around 30% of patients with rheumatoid arthritis (RA) have an inadequate response to methotrexate (MTX). We aimed to use routine clinical and biological data to build machine learning models predicting EULAR inadequate response to MTX and to identify simple predictive biomarkers.
METHODS : Models were trained on RA patients fulfilling the 2010 ACR/EULAR criteria from the ESPOIR and Leiden EAC cohorts to predict the EULAR response at 9 months (+/- 6 months). Several models were compared on the training set using the AUROC. The best model was evaluated on an external validation cohort (tREACH). Model's predictions were explained using Shapley values to extract a biomarker of inadequate response.
RESULTS : We included 493 therapeutic sequences from ESPOIR, 239 from EAC, and 138 from tREACH. The model selected DAS28, Lymphocytes, Creatininemia, Leucocytes, AST, ALT, swollen joint count, and corticosteroid co-treatment as predictors. The model reached an AUROC of 0.72 (95% CI : [0.63; 0.80]) on the external validation set, where 70% of patients were responders to MTX. Patients predicted as inadequate responders had only 38% (95% CI : [20%; 58%]) chance to respond and using the algorithm to decide to initiate MTX would decrease inadequate-response rate from 30% to 23% (95% CI: [17%; 29%]). A biomarker was identified in patients with moderate or high activity (DAS28 > 3.2): patients with a lymphocyte count superior to 2000 cells/mm3 are significantly less likely to respond.
CONCLUSION : Our study highlights the usefulness of machine learning in unveiling subgroups of inadequate responders to MTX to guide new therapeutic strategies. Further work is needed to validate this approach.
Duquesne Julien, Bouget Vincent, Cournède Paul Henry, Fautrel Bruno, Guillemin Francis, de Jong Pascal H P, Heutz Judith W, Verstappen Marloes, van der Helm-van Mil Annette H M, Mariette Xavier, Bitoun Samuel
2022-Nov-23
Biomarker, Machine Learning, Methotrexate, Rheumatoid Arthritis, Treatment Response
