Machine learning predicts response to TNF inhibitors in rheumatoid arthritis: results on the ESPOIR and ABIRISK cohorts. Issue 2 (23rd August 2022)

• Record Type: Journal Article
• Authors: Bouget, Vincent; Duquesne, Julien; Hassler, Signe; Cournède, Paul-Henry; Fautrel, Bruno; Guillemin, Francis; Pallardy, Marc; Broët, Philippe; Mariette, Xavier; Bitoun, Samuel
• Is Part Of: RMD open. Volume 8:Issue 2(2022)
• Publication Date: 2022-08-23
• Subjects: Rheumatoid Arthritis -- Outcome Assessment, Health Care -- Tumor Necrosis Factor Inhibitors; Musculoskeletal system -- Diseases -- Periodicals; Rheumatism -- Periodicals; 616.7005
• DOI: 10.1136/rmdopen-2022-002442
• Languages: English
• ISSNs: 2056-5933

Abstract

Abstract : Objectives: Around 30% of patients with rheumatoid arthritis (RA) do not respond to tumour necrosis factor inhibitors (TNFi). We aimed to predict patient response to TNFi using machine learning on simple clinical and biological data. Methods: We used data from the RA ESPOIR cohort to train our models. The endpoints were the EULAR response and the change in Disease Activity Score (DAS28). We compared the performances of multiple models (linear regression, random forest, XGBoost and CatBoost) on the training set and cross-validated them using the area under the receiver operating characteristic curve (AUROC) or the mean squared error. The best model was then evaluated on a replication cohort (ABIRISK). Results: We included 161 patients from ESPOIR and 118 patients from ABIRISK. The key selected features were DAS28, lymphocytes, ALT (aspartate aminotransferase), neutrophils, age, weight, and smoking status. When predicting EULAR response, CatBoost achieved the best performances of the four tested models. It reached an AUROC of 0.72 (0.68–0.73) on the train set (ESPOIR). Better results were obtained on the train set when etanercept and monoclonal antibodies were analysed separately. On the test set (ABIRISK), these models respectively achieved on AUROC of 0.70 (0.57–0.82) and 0.71 (0.55–0.86). Two decision thresholds were tested. The first prioritised a high confidence in identifying responders and yielded a confidence up to 90% for predicting response. The second prioritised a high confidence in identifying inadequate responders and yielded a confidence up to 70% for predicting non-response. The change in DAS28 was predicted with an average error of 1.1 DAS28 points. Conclusion: The machine learning models developed allowed predicting patient response to TNFi exclusively using data available in clinical routine.