Minimal disease activity (MDA) in patients with recent-onset psoriatic arthritis: predictive model based on machine learning
Background: 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 pre...
| Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | article |
| Status: | Published version |
| Publication Date: | 2022 |
| Country: | España |
| Institution: | Institut d’Investigació Biomèdica Sant Pau (IIB Sant Pau) |
| Repository: | r-IIB SANT PAU. Repositorio Institucional de Producción Científica del Instituto de Investigación Biomédica Sant Pau |
| OAI Identifier: | oai:iibsantpau.fundanetsuite.com:p15742 |
| Online Access: | https://iibsantpau.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=15742 |
| Access Level: | Open access |
| Keyword: | adolescent adult clinical trial human machine learning multicenter study pain psoriatic arthritis severity of illness index treatment outcome Adolescent Adult Arthritis, Psoriatic Humans Machine Learning Pain Severity of Illness Index Treatment Outcome |
| id |
ES_3a71eaec1ff3cdf05e2d59ae90bb113f |
|---|---|
| oai_identifier_str |
oai:iibsantpau.fundanetsuite.com:p15742 |
| 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 R.Seoane-Mato D.Laiz A.Agirregoikoa E.G.Montilla C.Park H.-S.Pinto-Tasende J.A.Bethencourt Baute J.J.Ibáñez B.J.Toniolo E.Ramírez J.García A.S.Cañete J.D.Juanola X.Fiter J.Gratacós J.Rodriguez-Moreno J.Rosa J.N.Martín A.L.García A.B.Segura P.C.Ferrer A.L.Barrio S.P.Plata Izquierdo A.J.Bustabad S.Guimerá Martín-Neda F.J.Capdevilla E.F.Díaz R.R.Cuervo A.Gibert M.A.Larraz P.T.de la Morena Barrio I.Lanza L.P.Sanchís D.B.Mesquida C.M.Murillo C.Moreno Ramos M.J.Beteta M.D.Guillén P.S.-P.Oliveira L.L.Marco T.N.Cebrián L.de la Cueva Dobao P.Steiner M.Muñoz-Fernández S.Garrido R.V.León M.Rubio E.Jiménez A.M.Fernández-Freire L.R.Luezas J.M.Sánchez-González M.D.Muñoz C.S.Senabre J.M.Rosas J.C.Soler G.S.Mataix Díaz F.J.Nieto-González J.C.González C.Ovalles Bonilla J.G.Rodríguez O.B.Medina F.J.N.Luján D.Ruiz Montesino M.D.Carrizosa Esquivel A.M.Fernández-Carballido C.Martínez-Vidal M.P.Fernández L.G.Jovani V.Alameda R.C.Sabater S.G.Romero I.B.Urruticoechea-Arana A.Torres M.S.Almodóvar R.López Estebaranz J.L.López Montilla M.D.García-Nieto A.V.adolescentadultclinical trialhumanmachine learningmulticenter studypainpsoriatic arthritisseverity of illness indextreatment outcomeAdolescentAdultArthritis, PsoriaticHumansMachine LearningPainSeverity of Illness IndexTreatment OutcomeBackground: 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. Methods: 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. Results: 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%. Conclusions: 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. © 2022, The Author(s).BMC2022info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://iibsantpau.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=15742ARTHRITIS RESEARCH & THERAPYISSN: 14786354ISSNe: 14786362reponame:r-IIB SANT PAU. Repositorio Institucional de Producción Científica del Instituto de Investigación Biomédica Sant Pauinstname:Institut d’Investigació Biomèdica Sant Pau (IIB Sant Pau)Inglésinfo:eu-repo/semantics/openAccessoai:iibsantpau.fundanetsuite.com:p157422026-06-14T12:41:47Z |
| 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 R. adolescent adult clinical trial human machine learning multicenter study pain psoriatic arthritis severity of illness index treatment outcome Adolescent Adult Arthritis, Psoriatic Humans Machine Learning Pain Severity of Illness Index Treatment Outcome |
| 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 R. Seoane-Mato D. Laiz A. Agirregoikoa E.G. Montilla C. Park H.-S. Pinto-Tasende J.A. Bethencourt Baute J.J. Ibáñez B.J. Toniolo E. Ramírez J. García A.S. Cañete J.D. Juanola X. Fiter J. Gratacós J. Rodriguez-Moreno J. Rosa J.N. Martín A.L. García A.B. Segura P.C. Ferrer A.L. Barrio S.P. Plata Izquierdo A.J. Bustabad S. Guimerá Martín-Neda F.J. Capdevilla E.F. Díaz R.R. Cuervo A. Gibert M.A. Larraz P.T. de la Morena Barrio I. Lanza L.P. Sanchís D.B. Mesquida C.M. Murillo C. Moreno Ramos M.J. Beteta M.D. Guillén P.S.-P. Oliveira L.L. Marco T.N. Cebrián L. de la Cueva Dobao P. Steiner M. Muñoz-Fernández S. Garrido R.V. León M. Rubio E. Jiménez A.M. Fernández-Freire L.R. Luezas J.M. Sánchez-González M.D. Muñoz C.S. Senabre J.M. Rosas J.C. Soler G.S. Mataix Díaz F.J. Nieto-González J.C. González C. Ovalles Bonilla J.G. Rodríguez O.B. Medina F.J.N. Luján D. Ruiz Montesino M.D. Carrizosa Esquivel A.M. Fernández-Carballido C. Martínez-Vidal M.P. Fernández L.G. Jovani V. Alameda R.C. Sabater S.G. Romero I.B. Urruticoechea-Arana A. Torres M.S. Almodóvar R. López Estebaranz J.L. López Montilla M.D. García-Nieto A.V. |
| author |
Queiro R. |
| author_facet |
Queiro R. Seoane-Mato D. Laiz A. Agirregoikoa E.G. Montilla C. Park H.-S. Pinto-Tasende J.A. Bethencourt Baute J.J. Ibáñez B.J. Toniolo E. Ramírez J. García A.S. Cañete J.D. Juanola X. Fiter J. Gratacós J. Rodriguez-Moreno J. Rosa J.N. Martín A.L. García A.B. Segura P.C. Ferrer A.L. Barrio S.P. Plata Izquierdo A.J. Bustabad S. Guimerá Martín-Neda F.J. Capdevilla E.F. Díaz R.R. Cuervo A. Gibert M.A. Larraz P.T. de la Morena Barrio I. Lanza L.P. Sanchís D.B. Mesquida C.M. Murillo C. Moreno Ramos M.J. Beteta M.D. Guillén P.S.-P. Oliveira L.L. Marco T.N. Cebrián L. de la Cueva Dobao P. Steiner M. Muñoz-Fernández S. Garrido R.V. León M. Rubio E. Jiménez A.M. Fernández-Freire L.R. Luezas J.M. Sánchez-González M.D. Muñoz C.S. Senabre J.M. Rosas J.C. Soler G.S. Mataix Díaz F.J. Nieto-González J.C. González C. Ovalles Bonilla J.G. Rodríguez O.B. Medina F.J.N. Luján D. Ruiz Montesino M.D. Carrizosa Esquivel A.M. Fernández-Carballido C. Martínez-Vidal M.P. Fernández L.G. Jovani V. Alameda R.C. Sabater S.G. Romero I.B. Urruticoechea-Arana A. Torres M.S. Almodóvar R. López Estebaranz J.L. López Montilla M.D. García-Nieto A.V. |
| author_role |
author |
| author2 |
Seoane-Mato D. Laiz A. Agirregoikoa E.G. Montilla C. Park H.-S. Pinto-Tasende J.A. Bethencourt Baute J.J. Ibáñez B.J. Toniolo E. Ramírez J. García A.S. Cañete J.D. Juanola X. Fiter J. Gratacós J. Rodriguez-Moreno J. Rosa J.N. Martín A.L. García A.B. Segura P.C. Ferrer A.L. Barrio S.P. Plata Izquierdo A.J. Bustabad S. Guimerá Martín-Neda F.J. Capdevilla E.F. Díaz R.R. Cuervo A. Gibert M.A. Larraz P.T. de la Morena Barrio I. Lanza L.P. Sanchís D.B. Mesquida C.M. Murillo C. Moreno Ramos M.J. Beteta M.D. Guillén P.S.-P. Oliveira L.L. Marco T.N. Cebrián L. de la Cueva Dobao P. Steiner M. Muñoz-Fernández S. Garrido R.V. León M. Rubio E. Jiménez A.M. Fernández-Freire L.R. Luezas J.M. Sánchez-González M.D. Muñoz C.S. Senabre J.M. Rosas J.C. Soler G.S. Mataix Díaz F.J. Nieto-González J.C. González C. Ovalles Bonilla J.G. Rodríguez O.B. Medina F.J.N. Luján D. Ruiz Montesino M.D. Carrizosa Esquivel A.M. Fernández-Carballido C. Martínez-Vidal M.P. Fernández L.G. Jovani V. Alameda R.C. Sabater S.G. Romero I.B. Urruticoechea-Arana A. Torres M.S. Almodóvar R. López Estebaranz J.L. López Montilla M.D. García-Nieto A.V. |
| author2_role |
author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author |
| dc.subject.none.fl_str_mv |
adolescent adult clinical trial human machine learning multicenter study pain psoriatic arthritis severity of illness index treatment outcome Adolescent Adult Arthritis, Psoriatic Humans Machine Learning Pain Severity of Illness Index Treatment Outcome |
| topic |
adolescent adult clinical trial human machine learning multicenter study pain psoriatic arthritis severity of illness index treatment outcome Adolescent Adult Arthritis, Psoriatic Humans Machine Learning Pain Severity of Illness Index Treatment Outcome |
| description |
Background: 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. Methods: 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. Results: 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%. Conclusions: 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. © 2022, The Author(s). |
| publishDate |
2022 |
| dc.date.none.fl_str_mv |
2022 |
| 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 |
https://iibsantpau.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=15742 |
| url |
https://iibsantpau.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=15742 |
| dc.language.none.fl_str_mv |
Inglés |
| language_invalid_str_mv |
Inglés |
| dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess |
| eu_rights_str_mv |
openAccess |
| dc.publisher.none.fl_str_mv |
BMC |
| publisher.none.fl_str_mv |
BMC |
| dc.source.none.fl_str_mv |
ARTHRITIS RESEARCH & THERAPY ISSN: 14786354 ISSNe: 14786362 reponame:r-IIB SANT PAU. Repositorio Institucional de Producción Científica del Instituto de Investigación Biomédica Sant Pau instname:Institut d’Investigació Biomèdica Sant Pau (IIB Sant Pau) |
| instname_str |
Institut d’Investigació Biomèdica Sant Pau (IIB Sant Pau) |
| reponame_str |
r-IIB SANT PAU. Repositorio Institucional de Producción Científica del Instituto de Investigación Biomédica Sant Pau |
| collection |
r-IIB SANT PAU. Repositorio Institucional de Producción Científica del Instituto de Investigación Biomédica Sant Pau |
| repository.name.fl_str_mv |
|
| repository.mail.fl_str_mv |
|
| _version_ |
1869406236318892032 |
| score |
15,811543 |