Minimal disease activity (MDA) in patients with recent-onset psoriatic arthritis: predictive model based on machine learning

Article number: 153 (2022)

Detalles Bibliográficos
Autores: Queiro, Rubén, Seoane-Mato, Daniel, Laiz, Ana, Galíndez Agirregoikoa, Eva, Montilla Morales, Carlos Alberto, Hye-Sang, Park, Pinto-Tasende, José A., Bethencourt Baute, Juan J., Joven Ibáñez, Beatriz, Toniolo, Elide, Ramírez, Julio, Serrano García, Ana
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2022
País:España
Institución:Universidad de Salamanca (USAL)
Repositorio:GREDOS. Repositorio Institucional de la Universidad de Salamanca
OAI Identifier:oai:gredos.usal.es:10366/162055
Acceso en línea:http://hdl.handle.net/10366/162055
Access Level:acceso abierto
Palabra clave:Recent‐onset psoriatic arthritis
Minimal disease activity
Predictive model
Machine learning
Arthritis
3205 Medicina Interna
artritis
id ES_d03e9f62e7740d3b092a9aa19dbffd87
oai_identifier_str oai:gredos.usal.es:10366/162055
network_acronym_str ES
network_name_str España
repository_id_str
spelling Minimal disease activity (MDA) in patients with recent-onset psoriatic arthritis: predictive model based on machine learningQueiro, RubénSeoane-Mato, DanielLaiz, AnaGalíndez Agirregoikoa, EvaMontilla Morales, Carlos AlbertoHye-Sang, ParkPinto-Tasende, José A.Bethencourt Baute, Juan J.Joven Ibáñez, BeatrizToniolo, ElideRamírez, JulioSerrano García, AnaRecent‐onset psoriatic arthritisMinimal disease activityPredictive modelMachine learningArthritis3205 Medicina InternaartritisArticle number: 153 (2022)[EN]Very few data are available on predictors of minimal disease activity (MDA) in patients with recent-onset psoriatic arthritis (PsA). Such data are crucial, since the therapeutic measures used to change the adverse course of PsA are more likely to succeed if we intervene early. In the present study, we used predictive models based on machine learning to detect variables associated with achieving MDA in patients with recent-onset PsA. We performed a multicenter observational prospective study (2-year follow-up, regular annual visits). The study population comprised patients aged ≥18 years who fulfilled the CASPAR criteria and less than 2 years since the onset of symptoms. The dataset contained data for the independent variables from the baseline visit and from follow-up visit number 1. These were matched with the outcome measures from follow-up visits 1 and 2, respectively. We trained a random forest-type machine learning algorithm to analyze the association between the outcome measure and the variables selected in the bivariate analysis. In order to understand how the model uses the variables to make its predictions, we applied the SHAP technique. We used a confusion matrix to visualize the performance of the model. The sample comprised 158 patients. 55.5% and 58.3% of the patients had MDA at the first and second follow-up visit, respectively. In our model, the variables with the greatest predictive ability were global pain, impact of the disease (PsAID), patient global assessment of disease, and physical function (HAQ-Disability Index). The percentage of hits in the confusion matrix was 85.94%. A key objective in the management of PsA should be control of pain, which is not always associated with inflammatory burden, and the establishment of measures to better control the various domains of PsA.Este trabajo fue apoyado por AbbVie, que no tuvo ningún rol en el diseño, recopilación de datos, análisis de datos, interpretación o redacción de este manuscrito.BioMed Central202520252022info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10366/162055reponame:GREDOS. Repositorio Institucional de la Universidad de Salamancainstname:Universidad de Salamanca (USAL)InglésAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:gredos.usal.es:10366/1620552026-06-07T06:28:51Z
dc.title.none.fl_str_mv Minimal disease activity (MDA) in patients with recent-onset psoriatic arthritis: predictive model based on machine learning
title Minimal disease activity (MDA) in patients with recent-onset psoriatic arthritis: predictive model based on machine learning
spellingShingle Minimal disease activity (MDA) in patients with recent-onset psoriatic arthritis: predictive model based on machine learning
Queiro, Rubén
Recent‐onset psoriatic arthritis
Minimal disease activity
Predictive model
Machine learning
Arthritis
3205 Medicina Interna
artritis
title_short Minimal disease activity (MDA) in patients with recent-onset psoriatic arthritis: predictive model based on machine learning
title_full Minimal disease activity (MDA) in patients with recent-onset psoriatic arthritis: predictive model based on machine learning
title_fullStr Minimal disease activity (MDA) in patients with recent-onset psoriatic arthritis: predictive model based on machine learning
title_full_unstemmed Minimal disease activity (MDA) in patients with recent-onset psoriatic arthritis: predictive model based on machine learning
title_sort Minimal disease activity (MDA) in patients with recent-onset psoriatic arthritis: predictive model based on machine learning
dc.creator.none.fl_str_mv Queiro, Rubén
Seoane-Mato, Daniel
Laiz, Ana
Galíndez Agirregoikoa, Eva
Montilla Morales, Carlos Alberto
Hye-Sang, Park
Pinto-Tasende, José A.
Bethencourt Baute, Juan J.
Joven Ibáñez, Beatriz
Toniolo, Elide
Ramírez, Julio
Serrano García, Ana
author Queiro, Rubén
author_facet Queiro, Rubén
Seoane-Mato, Daniel
Laiz, Ana
Galíndez Agirregoikoa, Eva
Montilla Morales, Carlos Alberto
Hye-Sang, Park
Pinto-Tasende, José A.
Bethencourt Baute, Juan J.
Joven Ibáñez, Beatriz
Toniolo, Elide
Ramírez, Julio
Serrano García, Ana
author_role author
author2 Seoane-Mato, Daniel
Laiz, Ana
Galíndez Agirregoikoa, Eva
Montilla Morales, Carlos Alberto
Hye-Sang, Park
Pinto-Tasende, José A.
Bethencourt Baute, Juan J.
Joven Ibáñez, Beatriz
Toniolo, Elide
Ramírez, Julio
Serrano García, Ana
author2_role author
author
author
author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Recent‐onset psoriatic arthritis
Minimal disease activity
Predictive model
Machine learning
Arthritis
3205 Medicina Interna
artritis
topic Recent‐onset psoriatic arthritis
Minimal disease activity
Predictive model
Machine learning
Arthritis
3205 Medicina Interna
artritis
description Article number: 153 (2022)
publishDate 2022
dc.date.none.fl_str_mv 2022
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10366/162055
url http://hdl.handle.net/10366/162055
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv Attribution-NonCommercial-NoDerivatives 4.0 Internacional
http://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution-NonCommercial-NoDerivatives 4.0 Internacional
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv BioMed Central
publisher.none.fl_str_mv BioMed Central
dc.source.none.fl_str_mv reponame:GREDOS. Repositorio Institucional de la Universidad de Salamanca
instname:Universidad de Salamanca (USAL)
instname_str Universidad de Salamanca (USAL)
reponame_str GREDOS. Repositorio Institucional de la Universidad de Salamanca
collection GREDOS. Repositorio Institucional de la Universidad de Salamanca
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869420156710551552
score 15,811543

Similares en LA Referencia