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

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

SUBMITTER: Duquesne J 

PROVIDER: S-EPMC10321123 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature