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Disease activity trajectories in rheumatoid arthritis: a tool for prediction of outcome
Objective: Predicting treatment response and disease progression in rheumatoid arthritis (RA) remains an elusive endeavour. Identifying subgroups of patients with similar progression is essential for understanding what hinders improvement. However, this cannot be achieved with response criteria base...
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Published in: | Scandinavian journal of rheumatology 2021, Vol.50 (1), p.1-10 |
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creator | Leu Agelii, M Andersson, MLE Jones, BL Sjöwall, C Kastbom, A Hafström, I Forslind, K Gjertsson, I |
description | Objective: Predicting treatment response and disease progression in rheumatoid arthritis (RA) remains an elusive endeavour. Identifying subgroups of patients with similar progression is essential for understanding what hinders improvement. However, this cannot be achieved with response criteria based on current versus previous Disease Activity Scores, as they lack the time component. We propose a longitudinal approach that identifies subgroups of patients while capturing their evolution across several clinical outcomes simultaneously (multi-trajectories).
Method: For exploration, the RA cohort BARFOT (n = 2829) was used to identify 24 month post-diagnosis simultaneous trajectories of 28-joint Disease Activity Score and its components. Measurements were available at inclusion (0), 3, 6, 12, 24, and 60 months. Multi-trajectories were found with latent class growth modelling. For validation, the TIRA-2 cohort (n = 504) was used. Radiographic changes, assessed by the modified Sharp van der Heijde score, were correlated with trajectory membership.
Results: Three multi-trajectories were identified, with 39.6% of the patients in the lowest and 18.9% in the highest (worst) trajectory. Patients in the worst trajectory had on average eight tender and six swollen joints after 24 months. Radiographic changes at 24 and 60 months were significantly increased from the lowest to the highest trajectory.
Conclusion: Multi-trajectories constitute a powerful tool for identifying subgroups of RA patients and could be used in future studies searching for predictive biomarkers for disease progression. The evolution and shape of the trajectories in TIRA-2 were very similar to those in BARFOT, even though TIRA-2 is a newer cohort. |
doi_str_mv | 10.1080/03009742.2020.1774646 |
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Method: For exploration, the RA cohort BARFOT (n = 2829) was used to identify 24 month post-diagnosis simultaneous trajectories of 28-joint Disease Activity Score and its components. Measurements were available at inclusion (0), 3, 6, 12, 24, and 60 months. Multi-trajectories were found with latent class growth modelling. For validation, the TIRA-2 cohort (n = 504) was used. Radiographic changes, assessed by the modified Sharp van der Heijde score, were correlated with trajectory membership.
Results: Three multi-trajectories were identified, with 39.6% of the patients in the lowest and 18.9% in the highest (worst) trajectory. Patients in the worst trajectory had on average eight tender and six swollen joints after 24 months. Radiographic changes at 24 and 60 months were significantly increased from the lowest to the highest trajectory.
Conclusion: Multi-trajectories constitute a powerful tool for identifying subgroups of RA patients and could be used in future studies searching for predictive biomarkers for disease progression. The evolution and shape of the trajectories in TIRA-2 were very similar to those in BARFOT, even though TIRA-2 is a newer cohort.</description><identifier>ISSN: 0300-9742</identifier><identifier>ISSN: 1502-7732</identifier><identifier>EISSN: 1502-7732</identifier><identifier>DOI: 10.1080/03009742.2020.1774646</identifier><identifier>PMID: 32856510</identifier><language>eng</language><publisher>England: Taylor & Francis</publisher><subject>Adult ; Aged ; american-college ; Arthritis, Rheumatoid - diagnostic imaging ; Arthritis, Rheumatoid - epidemiology ; association ; Clinical Medicine ; Cohort Studies ; criteria ; disability ; Disease Progression ; Female ; Humans ; hyperactivity ; Klinisk medicin ; league ; Male ; Medical and Health Sciences ; Medicin och hälsovetenskap ; Middle Aged ; Reumatologi och inflammation ; Rheumatology ; Rheumatology and Autoimmunity ; Severity of Illness Index ; Sweden - epidemiology ; validation</subject><ispartof>Scandinavian journal of rheumatology, 2021, Vol.50 (1), p.1-10</ispartof><rights>2021 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. 2021</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c646t-f411a44890f94c5fb4a56ed659d162b0f317ae08ca4e93232c4b0d242cf54ada3</citedby><cites>FETCH-LOGICAL-c646t-f411a44890f94c5fb4a56ed659d162b0f317ae08ca4e93232c4b0d242cf54ada3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,780,784,885,4024,27923,27924,27925</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32856510$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink><backlink>$$Uhttps://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-169323$$DView record from Swedish Publication Index$$Hfree_for_read</backlink><backlink>$$Uhttps://gup.ub.gu.se/publication/296467$$DView record from Swedish Publication Index$$Hfree_for_read</backlink><backlink>$$Uhttps://lup.lub.lu.se/record/f1baf1a4-5a81-4688-9510-44f8f9086cc1$$DView record from Swedish Publication Index$$Hfree_for_read</backlink><backlink>$$Uhttp://kipublications.ki.se/Default.aspx?queryparsed=id:144498601$$DView record from Swedish Publication Index$$Hfree_for_read</backlink></links><search><creatorcontrib>Leu Agelii, M</creatorcontrib><creatorcontrib>Andersson, MLE</creatorcontrib><creatorcontrib>Jones, BL</creatorcontrib><creatorcontrib>Sjöwall, C</creatorcontrib><creatorcontrib>Kastbom, A</creatorcontrib><creatorcontrib>Hafström, I</creatorcontrib><creatorcontrib>Forslind, K</creatorcontrib><creatorcontrib>Gjertsson, I</creatorcontrib><title>Disease activity trajectories in rheumatoid arthritis: a tool for prediction of outcome</title><title>Scandinavian journal of rheumatology</title><addtitle>Scand J Rheumatol</addtitle><description>Objective: Predicting treatment response and disease progression in rheumatoid arthritis (RA) remains an elusive endeavour. Identifying subgroups of patients with similar progression is essential for understanding what hinders improvement. However, this cannot be achieved with response criteria based on current versus previous Disease Activity Scores, as they lack the time component. We propose a longitudinal approach that identifies subgroups of patients while capturing their evolution across several clinical outcomes simultaneously (multi-trajectories).
Method: For exploration, the RA cohort BARFOT (n = 2829) was used to identify 24 month post-diagnosis simultaneous trajectories of 28-joint Disease Activity Score and its components. Measurements were available at inclusion (0), 3, 6, 12, 24, and 60 months. Multi-trajectories were found with latent class growth modelling. For validation, the TIRA-2 cohort (n = 504) was used. Radiographic changes, assessed by the modified Sharp van der Heijde score, were correlated with trajectory membership.
Results: Three multi-trajectories were identified, with 39.6% of the patients in the lowest and 18.9% in the highest (worst) trajectory. Patients in the worst trajectory had on average eight tender and six swollen joints after 24 months. Radiographic changes at 24 and 60 months were significantly increased from the lowest to the highest trajectory.
Conclusion: Multi-trajectories constitute a powerful tool for identifying subgroups of RA patients and could be used in future studies searching for predictive biomarkers for disease progression. The evolution and shape of the trajectories in TIRA-2 were very similar to those in BARFOT, even though TIRA-2 is a newer cohort.</description><subject>Adult</subject><subject>Aged</subject><subject>american-college</subject><subject>Arthritis, Rheumatoid - diagnostic imaging</subject><subject>Arthritis, Rheumatoid - epidemiology</subject><subject>association</subject><subject>Clinical Medicine</subject><subject>Cohort Studies</subject><subject>criteria</subject><subject>disability</subject><subject>Disease Progression</subject><subject>Female</subject><subject>Humans</subject><subject>hyperactivity</subject><subject>Klinisk medicin</subject><subject>league</subject><subject>Male</subject><subject>Medical and Health Sciences</subject><subject>Medicin och hälsovetenskap</subject><subject>Middle Aged</subject><subject>Reumatologi och inflammation</subject><subject>Rheumatology</subject><subject>Rheumatology and Autoimmunity</subject><subject>Severity of Illness Index</subject><subject>Sweden - epidemiology</subject><subject>validation</subject><issn>0300-9742</issn><issn>1502-7732</issn><issn>1502-7732</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>0YH</sourceid><recordid>eNp9kstu3CAUhlHVqpmmfYRWLLtxCvgYQ1eNkt6kkbrpZYkwhgmpbaaAE83bF3dm0lVmcQQ6-v5z04_Qa0ouKBHkHakJkS2wC0ZYSbUtcOBP0Io2hFVtW7OnaLUw1QKdoRcp3RJCQLbyOTqrmWh4Q8kK_br2yepksTbZ3_m8wznqW2tyiN4m7Cccb-w86hx8j3XMN9Fnn95jjXMIA3Yh4m20vS_qMOHgcJizCaN9iZ45PST76vCeox-fPn6_-lKtv33-enW5rkwZN1cOKNUAQhInwTSuA91w2_NG9pSzjriattoSYTRYWbOaGehIz4AZ14DudX2Oqn3ddG-3c6e20Y867lTQXh1Sv8vPKuCCEyi8fJTfxtD_Fx2FFABk0dKiXT-qHeZtia7EonG0064sphot6NJaKFnOrQCccJIIbgw9OfqmlCupzb9qTJZbtSf5a__zUoW4UYOfFeXLqQr_ds-Xvf7MNmU1-mTsMOjJhjkpBrXgAjjhBW32qIkhpWjdQ_Ey9GI3dbSbWuymDnYrujeHFnM32v5BdfRXAT7sAT8Vq4z6PsShV1nvhhBd1JPxSdWne_wFOPTmVw</recordid><startdate>2021</startdate><enddate>2021</enddate><creator>Leu Agelii, M</creator><creator>Andersson, MLE</creator><creator>Jones, BL</creator><creator>Sjöwall, C</creator><creator>Kastbom, A</creator><creator>Hafström, I</creator><creator>Forslind, K</creator><creator>Gjertsson, I</creator><general>Taylor & Francis</general><scope>0YH</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>ABXSW</scope><scope>ADTPV</scope><scope>AOWAS</scope><scope>D8T</scope><scope>DG8</scope><scope>ZZAVC</scope><scope>F1U</scope><scope>AGCHP</scope><scope>D95</scope></search><sort><creationdate>2021</creationdate><title>Disease activity trajectories in rheumatoid arthritis: a tool for prediction of outcome</title><author>Leu Agelii, M ; Andersson, MLE ; Jones, BL ; Sjöwall, C ; Kastbom, A ; Hafström, I ; Forslind, K ; Gjertsson, I</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c646t-f411a44890f94c5fb4a56ed659d162b0f317ae08ca4e93232c4b0d242cf54ada3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Adult</topic><topic>Aged</topic><topic>american-college</topic><topic>Arthritis, Rheumatoid - diagnostic imaging</topic><topic>Arthritis, Rheumatoid - epidemiology</topic><topic>association</topic><topic>Clinical Medicine</topic><topic>Cohort Studies</topic><topic>criteria</topic><topic>disability</topic><topic>Disease Progression</topic><topic>Female</topic><topic>Humans</topic><topic>hyperactivity</topic><topic>Klinisk medicin</topic><topic>league</topic><topic>Male</topic><topic>Medical and Health Sciences</topic><topic>Medicin och hälsovetenskap</topic><topic>Middle Aged</topic><topic>Reumatologi och inflammation</topic><topic>Rheumatology</topic><topic>Rheumatology and Autoimmunity</topic><topic>Severity of Illness Index</topic><topic>Sweden - epidemiology</topic><topic>validation</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Leu Agelii, M</creatorcontrib><creatorcontrib>Andersson, MLE</creatorcontrib><creatorcontrib>Jones, BL</creatorcontrib><creatorcontrib>Sjöwall, C</creatorcontrib><creatorcontrib>Kastbom, A</creatorcontrib><creatorcontrib>Hafström, I</creatorcontrib><creatorcontrib>Forslind, K</creatorcontrib><creatorcontrib>Gjertsson, I</creatorcontrib><collection>Taylor & Francis Open Access</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>SWEPUB Linköpings universitet full text</collection><collection>SwePub</collection><collection>SwePub Articles</collection><collection>SWEPUB Freely available online</collection><collection>SWEPUB Linköpings universitet</collection><collection>SwePub Articles full text</collection><collection>SWEPUB Göteborgs universitet</collection><collection>SWEPUB Lunds universitet full text</collection><collection>SWEPUB Lunds universitet</collection><jtitle>Scandinavian journal of rheumatology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Leu Agelii, M</au><au>Andersson, MLE</au><au>Jones, BL</au><au>Sjöwall, C</au><au>Kastbom, A</au><au>Hafström, I</au><au>Forslind, K</au><au>Gjertsson, I</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Disease activity trajectories in rheumatoid arthritis: a tool for prediction of outcome</atitle><jtitle>Scandinavian journal of rheumatology</jtitle><addtitle>Scand J Rheumatol</addtitle><date>2021</date><risdate>2021</risdate><volume>50</volume><issue>1</issue><spage>1</spage><epage>10</epage><pages>1-10</pages><issn>0300-9742</issn><issn>1502-7732</issn><eissn>1502-7732</eissn><abstract>Objective: Predicting treatment response and disease progression in rheumatoid arthritis (RA) remains an elusive endeavour. Identifying subgroups of patients with similar progression is essential for understanding what hinders improvement. However, this cannot be achieved with response criteria based on current versus previous Disease Activity Scores, as they lack the time component. We propose a longitudinal approach that identifies subgroups of patients while capturing their evolution across several clinical outcomes simultaneously (multi-trajectories).
Method: For exploration, the RA cohort BARFOT (n = 2829) was used to identify 24 month post-diagnosis simultaneous trajectories of 28-joint Disease Activity Score and its components. Measurements were available at inclusion (0), 3, 6, 12, 24, and 60 months. Multi-trajectories were found with latent class growth modelling. For validation, the TIRA-2 cohort (n = 504) was used. Radiographic changes, assessed by the modified Sharp van der Heijde score, were correlated with trajectory membership.
Results: Three multi-trajectories were identified, with 39.6% of the patients in the lowest and 18.9% in the highest (worst) trajectory. Patients in the worst trajectory had on average eight tender and six swollen joints after 24 months. Radiographic changes at 24 and 60 months were significantly increased from the lowest to the highest trajectory.
Conclusion: Multi-trajectories constitute a powerful tool for identifying subgroups of RA patients and could be used in future studies searching for predictive biomarkers for disease progression. The evolution and shape of the trajectories in TIRA-2 were very similar to those in BARFOT, even though TIRA-2 is a newer cohort.</abstract><cop>England</cop><pub>Taylor & Francis</pub><pmid>32856510</pmid><doi>10.1080/03009742.2020.1774646</doi><tpages>10</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Adult Aged american-college Arthritis, Rheumatoid - diagnostic imaging Arthritis, Rheumatoid - epidemiology association Clinical Medicine Cohort Studies criteria disability Disease Progression Female Humans hyperactivity Klinisk medicin league Male Medical and Health Sciences Medicin och hälsovetenskap Middle Aged Reumatologi och inflammation Rheumatology Rheumatology and Autoimmunity Severity of Illness Index Sweden - epidemiology validation |
title | Disease activity trajectories in rheumatoid arthritis: a tool for prediction of outcome |
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