Predicting biologic therapy outcome of patients with spondyloarthritis

Detalhes bibliográficos
Autor(a) principal: Barata, Carolina
Data de Publicação: 2021
Outros Autores: Maria Rodrigues, Ana, Canhão, Helena, Vinga, Susana, Carvalho, Alexandra M.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/124268
Resumo: Funding Information: This study was supported by the Portuguese Foundation for Science and Technology (Fundação para a Ciência e Tecnologia) through the Instituto de Telecomunicações (UIDB/50008/2020), INESC-ID (UIDB/50021/2020), and projects MATISSE (DSAIPA/DS/0026/2019) and PREDICT (PTDC/CCI-CIF/29877/2017). This work has received funding from the European Union's Horizon 2020 Research and Innovation Programme under grant agreement no. 951970 (OLISSIPO project). We also acknowledge Sociedade Portuguesa de Reumatologia and all Reuma.pt contributors.
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spelling Predicting biologic therapy outcome of patients with spondyloarthritisJoint models for longitudinal and survival analysisData miningDrug survivalElectronic medical recordsJoint modelsMedical recordsRheumatic diseaseSpondyloarthritisSurvival analysisHealth InformaticsHealth Information ManagementSDG 3 - Good Health and Well-beingFunding Information: This study was supported by the Portuguese Foundation for Science and Technology (Fundação para a Ciência e Tecnologia) through the Instituto de Telecomunicações (UIDB/50008/2020), INESC-ID (UIDB/50021/2020), and projects MATISSE (DSAIPA/DS/0026/2019) and PREDICT (PTDC/CCI-CIF/29877/2017). This work has received funding from the European Union's Horizon 2020 Research and Innovation Programme under grant agreement no. 951970 (OLISSIPO project). We also acknowledge Sociedade Portuguesa de Reumatologia and all Reuma.pt contributors.Background: Rheumatic diseases are one of the most common chronic diseases worldwide. Among them, spondyloarthritis (SpA) is a group of highly debilitating diseases, with an early onset age, which significantly impacts patients' quality of life, health care systems, and society in general. Recent treatment options consist of using biologic therapies, and establishing the most beneficial option according to the patients' characteristics is a challenge that needs to be overcome. Meanwhile, the emerging availability of electronic medical records has made necessary the development of methods that can extract insightful information while handling all the challenges of dealing with complex, real-world data. Objective: The aim of this study was to achieve a better understanding of SpA patients' therapy responses and identify the predictors that affect them, thereby enabling the prognosis of therapy success or failure. Methods: A data mining approach based on joint models for the survival analysis of the biologic therapy failure is proposed, which considers the information of both baseline and time-varying variables extracted from the electronic medical records of SpA patients from the database, Reuma.pt. Results: Our results show that being a male, starting biologic therapy at an older age, having a larger time interval between disease start and initiation of the first biologic drug, and being human leukocyte antigen (HLA)-B27 positive are indicators of a good prognosis for the biological drug survival; meanwhile, having disease onset or biologic therapy initiation occur in more recent years, a larger number of education years, and higher values of C-reactive protein or Bath Ankylosing Spondylitis Functional Index (BASFI) at baseline are all predictors of a greater risk of failure of the first biologic therapy. Conclusions: Among this Portuguese subpopulation of SpA patients, those who were male, HLA-B27 positive, and with a later biologic therapy starting date or a larger time interval between disease start and initiation of the first biologic therapy showed longer therapy adherence. Joint models proved to be a valuable tool for the analysis of electronic medical records in the field of rheumatic diseases and may allow for the identification of potential predictors of biologic therapy failure.NOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM)Centro de Estudos de Doenças Crónicas (CEDOC)Comprehensive Health Research Centre (CHRC) - pólo NMSRUNBarata, CarolinaMaria Rodrigues, AnaCanhão, HelenaVinga, SusanaCarvalho, Alexandra M.2021-09-09T00:27:38Z2021-072021-07-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10362/124268engPURE: 33427731https://doi.org/10.2196/26823info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-05-22T17:55:58Zoai:run.unl.pt:10362/124268Portal AgregadorONGhttps://www.rcaap.pt/oai/openairemluisa.alvim@gmail.comopendoar:71602024-05-22T17:55:58Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Predicting biologic therapy outcome of patients with spondyloarthritis
Joint models for longitudinal and survival analysis
title Predicting biologic therapy outcome of patients with spondyloarthritis
spellingShingle Predicting biologic therapy outcome of patients with spondyloarthritis
Barata, Carolina
Data mining
Drug survival
Electronic medical records
Joint models
Medical records
Rheumatic disease
Spondyloarthritis
Survival analysis
Health Informatics
Health Information Management
SDG 3 - Good Health and Well-being
title_short Predicting biologic therapy outcome of patients with spondyloarthritis
title_full Predicting biologic therapy outcome of patients with spondyloarthritis
title_fullStr Predicting biologic therapy outcome of patients with spondyloarthritis
title_full_unstemmed Predicting biologic therapy outcome of patients with spondyloarthritis
title_sort Predicting biologic therapy outcome of patients with spondyloarthritis
author Barata, Carolina
author_facet Barata, Carolina
Maria Rodrigues, Ana
Canhão, Helena
Vinga, Susana
Carvalho, Alexandra M.
author_role author
author2 Maria Rodrigues, Ana
Canhão, Helena
Vinga, Susana
Carvalho, Alexandra M.
author2_role author
author
author
author
dc.contributor.none.fl_str_mv NOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM)
Centro de Estudos de Doenças Crónicas (CEDOC)
Comprehensive Health Research Centre (CHRC) - pólo NMS
RUN
dc.contributor.author.fl_str_mv Barata, Carolina
Maria Rodrigues, Ana
Canhão, Helena
Vinga, Susana
Carvalho, Alexandra M.
dc.subject.por.fl_str_mv Data mining
Drug survival
Electronic medical records
Joint models
Medical records
Rheumatic disease
Spondyloarthritis
Survival analysis
Health Informatics
Health Information Management
SDG 3 - Good Health and Well-being
topic Data mining
Drug survival
Electronic medical records
Joint models
Medical records
Rheumatic disease
Spondyloarthritis
Survival analysis
Health Informatics
Health Information Management
SDG 3 - Good Health and Well-being
description Funding Information: This study was supported by the Portuguese Foundation for Science and Technology (Fundação para a Ciência e Tecnologia) through the Instituto de Telecomunicações (UIDB/50008/2020), INESC-ID (UIDB/50021/2020), and projects MATISSE (DSAIPA/DS/0026/2019) and PREDICT (PTDC/CCI-CIF/29877/2017). This work has received funding from the European Union's Horizon 2020 Research and Innovation Programme under grant agreement no. 951970 (OLISSIPO project). We also acknowledge Sociedade Portuguesa de Reumatologia and all Reuma.pt contributors.
publishDate 2021
dc.date.none.fl_str_mv 2021-09-09T00:27:38Z
2021-07
2021-07-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/124268
url http://hdl.handle.net/10362/124268
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv PURE: 33427731
https://doi.org/10.2196/26823
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv mluisa.alvim@gmail.com
_version_ 1817545820540502016

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