4.7 Article

Machine learning identifies a profile of inadequate responder to methotrexate in rheumatoid arthritis

Journal

RHEUMATOLOGY
Volume 62, Issue 7, Pages 2402-2409

Publisher

OXFORD UNIV PRESS
DOI: 10.1093/rheumatology/keac645

Keywords

RA; MTX; treatment response; machine learning; biomarker

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The study aims to use routine clinical and biological data to build machine learning models for predicting EULAR inadequate response to MTX and identifying simple predictive biomarkers. The results show that the algorithm can accurately predict whether patients have an inadequate response to MTX and can guide new therapeutic strategies.
Objectives Around 30% of patients with RA have an inadequate response to 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). The 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/mm(3) 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.

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