{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T14:39:26Z","timestamp":1771684766976,"version":"3.50.1"},"reference-count":21,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,6,2]],"date-time":"2023-06-02T00:00:00Z","timestamp":1685664000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,6,2]],"date-time":"2023-06-02T00:00:00Z","timestamp":1685664000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R21-AR078339"],"award-info":[{"award-number":["R21-AR078339"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R21-AR078339"],"award-info":[{"award-number":["R21-AR078339"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R21-AR078339"],"award-info":[{"award-number":["R21-AR078339"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R21-AR078339"],"award-info":[{"award-number":["R21-AR078339"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R21-AR078339"],"award-info":[{"award-number":["R21-AR078339"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Harold and DuVal Bowen Fund"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Arthritis Res Ther"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>Many patients with rheumatoid arthritis (RA) require a trial of multiple biologic disease-modifying anti-rheumatic drugs (bDMARDs) to control their disease. With the availability of several bDMARD options, the history of bDMARDs may provide an alternative approach to understanding subphenotypes of RA. The objective of this study was to determine whether there exist distinct clusters of RA patients based on bDMARD prescription history to subphenotype RA.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>We studied patients from a validated electronic health record-based RA cohort with data from January 1, 2008, through July 31, 2019; all subjects prescribed\u2009\u2265\u20091 bDMARD or targeted synthetic (ts) DMARD were included. To determine whether subjects had similar b\/tsDMARD sequences, the sequences were considered as a Markov chain over the state-space of 5 classes of b\/tsDMARDs. The maximum likelihood estimator (MLE)-based approach was used to estimate the Markov chain parameters to determine the clusters. The EHR data of study subjects were further linked with a registry containing prospectively collected data for RA disease activity, i.e., clinical disease activity index (CDAI). As a proof of concept, we tested whether the clusters derived from b\/tsDMARD sequences correlated with clinical measures, specifically differing trajectories of CDAI.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>We studied 2172 RA subjects, mean age 52\u00a0years, RA duration 3.4\u00a0years, and 62% seropositive. We observed 550 unique b\/tsDMARD sequences and identified 4 main clusters: (1) TNFi persisters (65.7%), (2) TNFi and abatacept therapy (8.0%), (3) on rituximab or multiple b\/tsDMARDs (12.7%), (4) prescribed multiple therapies with tocilizumab predominant (13.6%). Compared to the other groups, TNFi persisters had the most favorable trajectory of CDAI over time.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>We observed that RA subjects can be clustered based on the sequence of b\/tsDMARD prescriptions over time and that the clusters were correlated with differing trajectories of disease activity over time. This study highlights an alternative approach to consider subphenotyping of patients with RA for studies aimed at understanding treatment response.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s13075-023-03072-0","type":"journal-article","created":{"date-parts":[[2023,6,2]],"date-time":"2023-06-02T04:32:19Z","timestamp":1685680339000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Utilizing biologic disease-modifying anti-rheumatic treatment sequences to subphenotype rheumatoid arthritis"],"prefix":"10.1186","volume":"25","author":[{"given":"Priyam","family":"Das","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dana","family":"Weisenfeld","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kumar","family":"Dahal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Debsurya","family":"De","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vivi","family":"Feathers","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jonathan S.","family":"Coblyn","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Michael E.","family":"Weinblatt","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nancy A.","family":"Shadick","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianxi","family":"Cai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4797-3200","authenticated-orcid":false,"given":"Katherine P.","family":"Liao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,6,2]]},"reference":[{"issue":"4","key":"3072_CR1","doi-asserted-by":"publisher","first-page":"1637","DOI":"10.1007\/s40744-021-00357-1","volume":"8","author":"EA Holdsworth","year":"2021","unstructured":"Holdsworth EA, Donaghy B, Fox KM, Desai P, Collier DH, Furst DE. Biologic and targeted synthetic DMARD utilization in the United States: Adelphi Real World disease specific programme for rheumatoid arthritis. Rheumatol Ther. 2021;8(4):1637\u201349.","journal-title":"Rheumatol Ther"},{"issue":"13","key":"3072_CR2","doi-asserted-by":"publisher","first-page":"1360","DOI":"10.1001\/jama.2018.13103","volume":"320","author":"D Aletaha","year":"2018","unstructured":"Aletaha D, Smolen JS. Diagnosis and management of rheumatoid arthritis. JAMA. 2018;320(13):1360\u201372.","journal-title":"JAMA"},{"issue":"5","key":"3072_CR3","doi-asserted-by":"publisher","first-page":"650","DOI":"10.1136\/annrheumdis-2017-212395","volume":"77","author":"T Frisell","year":"2018","unstructured":"Frisell T, Baecklund E, Bengtsson K, Di Giuseppe D, Forsblad-D\u2019Elia H, Askling J. Patient characteristics influence the choice of biological drug in RA, and will make non-TNFi biologics appear more harmful than TNFi biologics. Ann Rheum Dis. 2018;77(5):650\u20137.","journal-title":"Ann Rheum Dis"},{"issue":"1","key":"3072_CR4","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1186\/s12891-016-0897-y","volume":"17","author":"AN Mian","year":"2016","unstructured":"Mian AN, Ibrahim F, Scott IC, Bahadur S, Filkova M, Pollard L, et al. Changing clinical patterns in rheumatoid arthritis management over two decades: sequential observational studies. BMC Musculoskelet Disord. 2016;17(1):44.","journal-title":"BMC Musculoskelet Disord"},{"issue":"7","key":"3072_CR5","doi-asserted-by":"publisher","first-page":"924","DOI":"10.1002\/acr.24596","volume":"73","author":"L Fraenkel","year":"2021","unstructured":"Fraenkel L, Bathon JM, England BR, St.Clair EW, Arayssi T, Carandang K, et al. 2021 American College of Rheumatology guideline for the treatment of rheumatoid arthritis. Arthritis Care Res (Hoboken). 2021;73(7):924\u201339.","journal-title":"Arthritis Care Res (Hoboken)"},{"issue":"12","key":"3072_CR6","doi-asserted-by":"publisher","first-page":"4678","DOI":"10.1093\/rheumatology\/keac190","volume":"61","author":"SS Zhao","year":"2022","unstructured":"Zhao SS, Kearsley-Fleet L, Bosworth A, Watson K, Hyrich KL. Effectiveness of sequential biologic and targeted disease modifying anti-rheumatic drugs for rheumatoid arthritis. Rheumatology. 2022;61(12):4678\u201386.","journal-title":"Rheumatology"},{"issue":"2","key":"3072_CR7","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1007\/s40744-017-0078-y","volume":"4","author":"V Strand","year":"2017","unstructured":"Strand V, Miller P, Williams SA, Saunders K, Grant S, Kremer J. Discontinuation of biologic therapy in rheumatoid arthritis: analysis from the Corrona RA registry. Rheumatol and Ther. 2017;4(2):489\u2013502.","journal-title":"Rheumatol and Ther"},{"issue":"3","key":"3072_CR8","doi-asserted-by":"publisher","first-page":"e001882","DOI":"10.1136\/rmdopen-2021-001882","volume":"7","author":"J Law-Wan","year":"2021","unstructured":"Law-Wan J, Sparfel MA, Derolez S, Azzopardi N, Goupille P, Detert J, et al. Predictors of response to TNF inhibitors in rheumatoid arthritis: an individual patient data pooled analysis of randomised controlled trials. RMD Open. 2021;7(3):e001882.","journal-title":"RMD Open"},{"issue":"12","key":"3072_CR9","doi-asserted-by":"publisher","first-page":"1987","DOI":"10.1002\/art.41056","volume":"71","author":"Y Guan","year":"2019","unstructured":"Guan Y, Zhang H, Quang D, Wang Z, Parker SCJ, Pappas DA, et al. Machine learning to predict anti\u2013tumor necrosis factor drug responses of rheumatoid arthritis patients by integrating clinical and genetic markers. Arthritis Rheumatol. 2019;71(12):1987\u201396.","journal-title":"Arthritis Rheumatol"},{"issue":"4","key":"3072_CR10","doi-asserted-by":"publisher","first-page":"507","DOI":"10.1093\/rheumatology\/ken034","volume":"47","author":"JA Karlsson","year":"2007","unstructured":"Karlsson JA, Kristensen LE, Kapetanovic MC, Gulfe A, Saxne T, Geborek P. Treatment response to a second or third TNF-inhibitor in RA: results from the South Swedish Arthritis Treatment Group Register. Rheumatology. 2007;47(4):507\u201313.","journal-title":"Rheumatology"},{"issue":"11","key":"3072_CR11","doi-asserted-by":"publisher","first-page":"5209","DOI":"10.1109\/TSP.2007.898782","volume":"55","author":"Y Qi","year":"2007","unstructured":"Qi Y, Paisley JW, Carin L. Music analysis using hidden Markov mixture models. IEEE Trans Signal Process. 2007;55(11):5209\u201324.","journal-title":"IEEE Trans Signal Process"},{"issue":"8","key":"3072_CR12","doi-asserted-by":"publisher","first-page":"1120","DOI":"10.1002\/acr.20184","volume":"62","author":"KP Liao","year":"2010","unstructured":"Liao KP, Cai T, Gainer V, Goryachev S, Zeng-Treitler Q, Raychaudhuri S, et al. Electronic medical records for discovery research in rheumatoid arthritis. Arthritis Care Res. 2010;62(8):1120\u20137.","journal-title":"Arthritis Care Res"},{"issue":"12","key":"3072_CR13","doi-asserted-by":"publisher","first-page":"3759","DOI":"10.1093\/rheumatology\/keaa198","volume":"59","author":"S Huang","year":"2020","unstructured":"Huang S, Huang J, Cai T, Dahal KP, Cagan A, He Z, et al. Impact of ICD10 and secular changes on electronic medical record rheumatoid arthritis algorithms. Rheumatology (Oxford). 2020;59(12):3759\u201366.","journal-title":"Rheumatology (Oxford)"},{"issue":"1","key":"3072_CR14","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1093\/rheumatology\/keq263","volume":"50","author":"CK Iannaccone","year":"2011","unstructured":"Iannaccone CK, Lee YC, Cui J, Frits ML, Glass RJ, Plenge RM, et al. Using genetic and clinical data to understand response to disease-modifying anti-rheumatic drug therapy: data from the Brigham and Women\u2019s Hospital Rheumatoid Arthritis Sequential Study. Rheumatology (Oxford). 2011;50(1):40\u20136.","journal-title":"Rheumatology (Oxford)"},{"key":"3072_CR15","unstructured":"Nicolas P. Understanding Markov chains: examples and applications. Numerical methods for partial differential equations. Singapore: Springer; 2013."},{"key":"3072_CR16","unstructured":"Gupta R, Kumar R, Vassilvitskii S. On mixtures of markov chains. In: 30th conference on neural information processing systems. 2016. Available from: https:\/\/papers.nips.cc\/paper\/6078-on-mixtures-of-markov-chains.pdf. Cited 2022 Jan 30."},{"issue":"6","key":"3072_CR17","doi-asserted-by":"publisher","first-page":"747","DOI":"10.1080\/00273171.2017.1370364","volume":"52","author":"S de Haan-Rietdijk","year":"2017","unstructured":"de Haan-Rietdijk S, Kuppens P, Bergeman CS, Sheeber LB, Allen NB, Hamaker EL. On the use of mixed Markov models for intensive longitudinal data. Multivariate Behav Res. 2017;52(6):747\u201367.","journal-title":"Multivariate Behav Res"},{"issue":"1","key":"3072_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/03610917408548446","volume":"3","author":"T Calinski","year":"1974","unstructured":"Calinski T, Harabasz J. A dendrite method for cluster analysis. Commun Stat Simul Comput. 1974;3(1):1\u201327.","journal-title":"Commun Stat Simul Comput"},{"key":"3072_CR19","unstructured":"The MathWorks Inc. MATLAB version: 9.13.0 (R2022b). Natick: The MathWorks Inc.; 2022. https:\/\/www.mathworks.com."},{"issue":"1","key":"3072_CR20","doi-asserted-by":"publisher","first-page":"e000345","DOI":"10.1136\/rmdopen-2016-000345","volume":"3","author":"R Alten","year":"2017","unstructured":"Alten R, N\u00fc\u00dflein HG, Mariette X, Galeazzi M, Lorenz HM, Cantagrel A, et al. Baseline autoantibodies preferentially impact abatacept efficacy in patients with rheumatoid arthritis who are biologic na\u00efve: 6-month results from a real-world, international, prospective study. RMD Open. 2017;3(1):e000345.","journal-title":"RMD Open"},{"issue":"1","key":"3072_CR21","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1136\/annrheumdis-2020-217344","volume":"80","author":"G Nagy","year":"2021","unstructured":"Nagy G, Roodenrijs NM, Welsing PM, Kedves M, Hamar A, van der Goes MC, et al. EULAR definition of difficult-to-treat rheumatoid arthritis. Ann Rheum Dis. 2021;80(1):31\u20135.","journal-title":"Ann Rheum Dis"}],"container-title":["Arthritis Research &amp; Therapy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13075-023-03072-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13075-023-03072-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13075-023-03072-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,2]],"date-time":"2023-06-02T04:34:17Z","timestamp":1685680457000},"score":1,"resource":{"primary":{"URL":"https:\/\/arthritis-research.biomedcentral.com\/articles\/10.1186\/s13075-023-03072-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,2]]},"references-count":21,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["3072"],"URL":"https:\/\/doi.org\/10.1186\/s13075-023-03072-0","relation":{},"ISSN":["1478-6362"],"issn-type":[{"value":"1478-6362","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,2]]},"assertion":[{"value":"23 November 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 May 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 June 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study was reviewed and approved by the Mass General Brigham Institutional Review Board. Patient consent was waived by the approving ethics committee.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"M.W. receives research grant funding from Bristol Myers Squibb, Lilly, Amgen, and Jansen and stock options from Canfite, Inmedix, and Scipher. M.W. has served as a consultant for Abbvie, Aclaris, Amgen, Arena, Bayer, Bristol Myers Squibb, CorEvitas, EQRx, Genosco, Glaxo Smith Kline, Gilead, Horizon, Johnson and Johnson, Lilly, Pfizer, Revolo, Roche, Scipher, Set Point, and Tremeau. N.S. receives research grant funding from Amgen, UCB, Abbvie, BMS, and Genentech.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"93"}}