{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T05:10:54Z","timestamp":1778217054125,"version":"3.51.4"},"reference-count":37,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T00:00:00Z","timestamp":1778112000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T00:00:00Z","timestamp":1778112000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["acrjournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Arthritis &amp; Rheumatology"],"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To develop, externally validate, and simplify a machine learning model to predict remission between 6 and 24 months in patients with rheumatoid arthritis (RA) initiating tumor necrosis factor inhibitors, JAK inhibitors, interleukin\u20106 inhibitors, abatacept, or rituximab using data from 11 international registries in the JAK\u2010pot collaboration.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We analyzed 21,675 treatment courses for model training, 5,418 courses for internal validation, and 1,807 courses from Switzerland for external validation. Remission was defined as a Clinical Disease Activity Index score \u22642.8 within 6 to 24 months after treatment initiation or switching. An XGBoost model was trained on 63 baseline demographic and clinical variables. Model performance was evaluated using the area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Predictor contributions were quantified using Shapley Additive Explanations values. A simplified model using the top 10 predictors and a logistic regression benchmark were also evaluated.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In external validation, the full model achieved an AUC of 0.797 (95% confidence interval 0.774\u20130.821), sensitivity of 0.804, specificity of 0.650, PPV of 0.454, and NPV of 0.902. The simplified model performed comparably (AUC 0.802). Logistic regression using the same 10 predictors achieved an AUC of 0.809. Key predictors included patient global assessment, 28\u2010tender joint count, previous biologic\/targeted synthetic disease\u2010modifying antirheumatic drug exposure, and Health Assessment Questionnaire Disability Index score.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Using routinely collected baseline data across 11 registries, prediction of remission showed limited discrimination and was best suited to ruling out remission. Performance was similar for a 10\u2010variable model and logistic regression, indicating limited incremental value of model complexity without richer predictors.<\/jats:p>\n                    <jats:p>\n                      <jats:boxed-text content-type=\"graphic\" position=\"anchor\">\n                        <jats:graphic xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" mimetype=\"image\/png\" position=\"anchor\" specific-use=\"enlarged-web-image\" xlink:href=\"graphic\/art70165-gra-0001-m.png\">\n                          <jats:alt-text>image<\/jats:alt-text>\n                        <\/jats:graphic>\n                      <\/jats:boxed-text>\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/art.70165","type":"journal-article","created":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T10:29:30Z","timestamp":1775471370000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning to Predict Remission Between 6 and 24 Months in Rheumatoid Arthritis: Insights From\n                    <scp>JAK,<\/scp>\n                    an International Registry Collaboration"],"prefix":"10.1002","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2982-4591","authenticated-orcid":false,"given":"Zubeyir","family":"Salis","sequence":"first","affiliation":[{"name":"University of Montpellier and Physiology and Experimental Medicine of the Heart and Muscles  Montpellier France"},{"name":"Division of Rheumatology Geneva University Hospital  Geneva Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Denis","family":"Mongin","sequence":"additional","affiliation":[{"name":"Division of Rheumatology Geneva University Hospital  Geneva Switzerland"},{"name":"Geneva Center for Inflammation Research, Faculty of Medicine University of Geneva  Geneva Switzerland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Denis","family":"Choquette","sequence":"additional","affiliation":[{"name":"Centre Hospitalier de l'Universit\u00e9 de Montr\u00e9al, Institut de Recherche en Rhumatologie  Montr\u00e9al Qu\u00e9bec Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Louis","family":"Coupal","sequence":"additional","affiliation":[{"name":"Centre Hospitalier de l'Universit\u00e9 de Montr\u00e9al, Institut de Recherche en Rhumatologie  Montr\u00e9al Qu\u00e9bec Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Catalin","family":"Codreanu","sequence":"additional","affiliation":[{"name":"Center of Rheumatic Diseases University of Medicine and Pharmacy  Bucharest Romania"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0474-5344","authenticated-orcid":false,"given":"Florenzo","family":"Iannone","sequence":"additional","affiliation":[{"name":"Rheumatology Unit, Department of Precision and Regenerative Medicine and Ionian Area University of Bari Aldo Moro  Bari Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Roberto","family":"Caporali","sequence":"additional","affiliation":[{"name":"Department of Clinical Sciences and Community Health Universit\u00e0 degli Studi di Milano and Rheumatology Unit, ASST G. 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