1. Machine learning identifies a profile of inadequate responder to methotrexate in rheumatoid arthritis
- Author
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Julien Duquesne, Vincent Bouget, Paul Henry Cournède, Bruno Fautrel, Francis Guillemin, Pascal H P de Jong, Judith W Heutz, Marloes Verstappen, Annette H M van der Helm-van Mil, Xavier Mariette, Samuel Bitoun, Scienta Lab [Gif-sur-Yvette, France], Mathématiques et Informatique pour la Complexité et les Systèmes (MICS), CentraleSupélec-Université Paris-Saclay, CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Pharmacoépidémiologie et évaluation des soins [iPLesp] (PEPITES), Institut Pierre Louis d'Epidémiologie et de Santé Publique (iPLESP), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU), Adaptation, mesure et évaluation en santé. Approches interdisciplinaires (APEMAC), Université de Lorraine (UL), Erasmus University Medical Center [Rotterdam] (Erasmus MC), Service de Rhumatologie [CHU Bicêtre], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital Bicêtre, Immunologie des maladies virales, auto-immunes, hématologiques et bactériennes (IMVA-HB), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris-Saclay, Leiden University Medical Center (LUMC), and Universiteit Leiden
- Subjects
machine learning ,Rheumatology ,[SDV.MHEP.RSOA]Life Sciences [q-bio]/Human health and pathology/Rhumatology and musculoskeletal system ,[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] ,[SDV.SP.PHARMA]Life Sciences [q-bio]/Pharmaceutical sciences/Pharmacology ,treatment response ,biomarker ,Pharmacology (medical) ,[SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie ,MTX ,RA - Abstract
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/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.
- Published
- 2023