[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118396-en":3,"doc-seo-118396-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118396,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","Predicting Abatacept Retention Using Machine Learning","Machine learning is increasingly used in clinical practice to improve precision medicine and treatment results. This post hoc analysis pooled patient-level data from real-world ACTION and ASCORE trials in rheumatoid arthritis patients initiating abatacept. Ten machine learning models predicted 12-month retention, defined by treatment duration relative to 365 days combined with remission or major clinical response. SHAP values quantified feature importance and directionality. In 5320 patients, retention at 12 months was 61%.","Alten et al. Arthritis Research & Therapy (2025) 27:20 [https://doi.org/10.1186/s13075-025-03484-0](https://doi.org/10.1186/s13075-025-03484-0)  \nArthritis Research & Therapy  \n RESEARCH Open Access  \nPredicting abatacept retention using machine learning  \nRieke Alten1*, Claire Behar2, Pierre Merckaert3, Ebenezer Afari4, Virginie Vannier-Moreau5, Anael Ohayon5, Sean E. Connolly6, Aurélie Najm7, Pierre-Antoine Juge8, Gengyuan Liu6, Angshu Rai6, Yedid Elbez9 and Karissa Lozenski6  \nAbstract  \nBackground The incorporation of machine learning is becoming more prevalent in the clinical setting. By predicting clinical outcomes, machine learning can provide clinicians with a valuable tool for refining precision medicine approaches and improving treatment outcomes.  \nMethods This was a post hoc analysis of pooled patient-level data from the global, real-world ACTION and ASCOREtrials in patients with rheumatoid arthritis (RA) initiating abatacept. Patient demographic and disease characteristics were input across 10 machine learning models used to predict 12-month treatment retention. Retention was defined as treatment for > 365 days or ≤365 days in patients who achieved remission or major clinical response (based on European Alliance of Associations for Rheumatology response criteria) . The pooled dataset was split into a training/validation cohort for model development and a test cohort for an unbiased evaluation of performance. SHapley Additive exPlanation (SHAP) values determined the level of importance and directionality for key patient features predicting abatacept retention.  \nResults The pooled ACTION and ASCORE dataset included 5320 patients with RA (mean [standard deviation] age 57.7 [12 . 7] years; 79% female) . The 12-month abatacept retention rate was 61%(n = 3236) with a discontinuation rate of 39%(n = 2037) . In the training set (n = 4218), the gradient-boosting classifier model demonstrated the best performance (testing accuracy: 62%) . This model had an area under the receiver operating characteristic curve (95% confidence interval) of 0.620 (0 . 586, 0 . 653) and F1 score of 0.659 (0 . 625, 0 . 689) in the test set of patients (n = 1055) . Using this model, the five most important variables predicting 12-month abatacept retention were low body mass index (BMI), low American College of Rheumatology functional status class, anti-citrullinated protein antibody (ACPA) positivity, low Patient Global Assessment, and younger age.  \nConclusions The gradient-boosting classifier model identified key patient features predictive of abatacept retention from this large, real-world study population. The SHAP values conveyed the directionality and importance of BMI, functional status, ACPA serostatus, Patient Global Assessment, and age for abatacept retention. Findings are consistent with previous observations and help validate the machine learning approach for predictive modelling in RA treatment, and may help inform clinical decision making.  \nTrial registration NCT02109666 (ACTION), NCT02090556 (ASCORE) .  \nAnael Ohayon, Angshu Rai and Karissa Lozenski were affiliated to their institution at the time of analysis.  \n*Correspondence:  \nRieke Alten  \n[Rieke.alten@schlosspark-klinik.de](Rieke.alten@schlosspark-klinik.de)  \nFull list of author information is available at the end of the article  \n© The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons l","cbCaily21DzypftF","https://ap.wps.com/l/cbCaily21DzypftF","pdf",1529366,1,11,"English","en",105,"# Abstract\n## Background\n## Methods\n## Results\n## Conclusions\n## Keywords\n## Background","[{\"question\":\"How was 12-month abatacept retention defined in the analysis?\",\"answer\":\"Retention required abatacept treatment for more than 365 days versus 365 days or fewer, restricted to patients who achieved remission or major clinical response based on EULAR response criteria.\"},{\"question\":\"What datasets and models were used to predict retention?\",\"answer\":\"The study pooled patient-level data from ACTION and ASCORE in rheumatoid arthritis patients initiating abatacept and used 10 machine learning models to predict 12-month retention, including a training/validation cohort and an independent test cohort.\"},{\"question\":\"Which model performed best, and how was performance reported?\",\"answer\":\"A gradient-boosting classifier showed the best performance, with testing accuracy of 62%, ROC AUC (95% CI) of 0.620 (0.586–0.653), and an F1 score of 0.659 (0.625–0.689) in the test set.\"}]","Predicting Abatacept Retention Using Machine Learning | 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was 12-month abatacept retention defined in the analysis?","Question",{"text":75,"@type":76},"Retention required abatacept treatment for more than 365 days versus 365 days or fewer, restricted to patients who achieved remission or major clinical response based on EULAR response criteria.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What datasets and models were used to predict retention?",{"text":80,"@type":76},"The study pooled patient-level data from ACTION and ASCORE in rheumatoid arthritis patients initiating abatacept and used 10 machine learning models to predict 12-month retention, including a training/validation cohort and an independent test cohort.",{"name":82,"@type":73,"acceptedAnswer":83},"Which model performed best, and how was performance reported?",{"text":84,"@type":76},"A gradient-boosting classifier showed the best performance, with testing accuracy of 62%, ROC AUC (95% CI) of 0.620 (0.586–0.653), and an F1 score of 0.659 (0.625–0.689) in the test 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