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  1. J. Duquesne1,
  2. V. Bouget1,
  3. P. H. Cournède2,
  4. B. Fautrel3,
  5. S. Hassler4,
  6. F. Guillemin5,
  7. M. Pallardy6,7,
  8. P. Broet8,
  9. S. Bitoun9,
  10. X. Mariette9
  1. 1Scienta Lab, Research department, Paris, France
  2. 2CentraleSupélec, Lab of mathematics and computer science (MICS), Gif-sur-Yvette, France
  3. 3Sorbonne Université – Assistance Publique Hôpitaux de Paris, Groupe Hospitalier Pitié Salpêtrière, Service de Rhumatologie, Paris, France
  4. 4Assistance Publique Hôpitaux de Paris, Groupe Hospitalier Pitié Salpêtrière, Biotherapy (CIC-BTi), Paris, France
  5. 5Université de Lorraine, APEMAC, Nancy, France
  6. 6INSERM, Inflammation microbiome immunsurveillance, Chatenay Malabry, France
  7. 7ABIRISK, Innovative Medecines Initiative, Paris, France
  8. 8INSERM, University Paris-Saclay, Hôpitaux Universitaires Paris-Sud, CESP, Villejuif, France
  9. 9Université Paris Saclay, Assistance Publique-Hôpitaux de Paris, Hôpital Bicêtre, Service de Rhumatologie, Le Kremlin-Bicêtre, France


Background Thirteen drugs with different mechanisms of action may be considered to treat patients with methotrexate (MTX) inadequate response in rheumatoid arthritis (RA). TNF inhibitors (TNFi) are frequently the first choice in this situation. Unfortunately, 30% to 40% of RA patients do not respond to TNFi, resulting in a delay for beginning the appropriate targeted DMARD (tDMARD). Predicting the patient response to TNFi before prescribing the treatment is therefore a major goal and could help physicians to prescribe a tDMARD suited to the patient.

Objectives We aimed to build machine learning models based on simple clinical and biological data to predict patient response to TNFi.

Methods We used data from the ESPOIR early arthritis cohort (1) to train the models, and the ABIRISK cohort (2) to validate the results. We included patients that fulfilled the EULAR/ACR 2010 criteria and that were treated with a TNFi. The models take as inputs patient’s characteristics at treatment initiation and predicts the therapeutic response, defined as the EULAR response 12 months (+/- 6 months) after treatment initiation. We compared the performances of four models (Linear Regression, Random Forest, XGBoost, and Catboost) on the training set by cross-validated them using the Area Under the ROC Curve (AUCROC). The best model was then evaluated on the validation dataset (ABIRISK cohort). We conducted the methodology both on all TNFi together and on etanercept and monoclonal anti-TNF antibodies separated. We analyzed how clinical and biological variables impacted response to provide explainability of the prediction.

Results We included 164 patients from the ESPOIR cohort and 118 patients from the ABIRISK cohort. Better results were obtained when etanercept and monoclonal anti-TNF antibodies were analyzed separately.

These models predict a probability for a patient to respond to TNFi. This probability is compared to a decision threshold to obtain the binary outcome. Two decision thresholds were tested. The first prioritizes a high confidence when identifying responders (Strategy 1) while the second prioritizes a high confidence when identifying non-responders (Strategy 2). The model’s results are presented in Table 1.

Table 1.

Sensitivity, Specificity, PPV and NPV are computed on the ABIRISK validation cohort for each strategy

Using SHAP, we were able to analyze how each variable impacted the predictions. In particular, a DAS28 around 5 had the highest positive impact on response. Higher and lower values of DAS28 had either less impact or even negative impact on the patient response to TNFi treatment (Figure 1). This allows to identify non-linear relations between variables and patient response.

Conclusion The machine learning models developed in this study can predict RA patients’ response to TNFi using exclusively data available in clinical routine. These models also allow to analyze how these variables are used to predict response. Along with similar models for other tDMARDs, such algorithms could lead to a personalized therapeutic strategy.

References [1]Combe B, Benessiano J, Berenbaum F, Cantagrel A, Daurès J-P, Dougados M, et al. The ESPOIR cohort: A ten-year follow-up of early arthritis in France.

[2]Anon. ABIRISK Anti-Biopharmaceutical Immunization: Prediction and Analysis of Clinical Relevance to Minimize the RISK. 2019.

Acknowledgements For the first 5 years of the ESPOIR cohort, an unrestricted grant from Merck Sharp and Dohme (MSD) was allocated. Two additional grants from INSERM were obtained to support part of the biological database. The French Society of Rheumatology, Pfizer, AbbVie, Lilly, and more recently Fresenius and Biogen also supported the ESPOIR cohort study. We also wish to thank Nathalie Rincheval (Montpellier) who did expert monitoring and data management and all the investigators who recruited and followed the patients (F.Berenbaum, Paris-Saint Antoine, MC.Boissier, Paris-Bobigny, A.Cantagrel, Toulouse, B.Combe, Montpellier, M.Dougados, Paris-Cochin, P.Fardellone et P.Boumier Amiens, B.Fautrel, Paris-La Pitié, RM. Flipo, Lille, Ph. Goupille, Tours, F. Liote, Paris-Lariboisière, O.Vittecoq, Rouen, X.Mariette, Paris Bicetre, P.Dieude, Paris Bichat, A.Saraux, Brest, T.Schaeverbeke, Bordeaux, J.Sibilia, Strasbourg) as well as S.Martin (Paris Bichat) who did all the central dosages of CRP, IgA and IgM rheumatoid.

SB was supported by FHU CARE.

Disclosure of Interests None declared.

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