Minimal disease activity (MDA) in patients with recent-onset psoriatic arthritis

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,...

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Detalles Bibliográficos
Autores: Queiro, Ruben|||0000-0002-8418-7145, Seoane-Mato, Daniel|||0000-0001-5430-039X, Laiz, Ana|||0000-0002-2820-4801, Agirregoikoa, Eva Galíndez, Montilla, Carlos, Park, Hye S.|||0000-0002-4972-9527, Pinto-Tasende, Jose|||0000-0002-4993-8185, Bethencourt Baute, Juan José|||0000-0002-9382-9234, Joven Ibáñez, Beatriz, Toniolo, Elide|||0000-0002-8327-4861, Ramírez, Julio, García, Ana Serrano, Cañete, Juan D.|||0000-0003-2606-0573, Juanola, Xavier|||0000-0001-5998-6300, Fiter, Jordi|||0000-0002-8573-9749, Gratacós, Jordi|||0000-0003-4007-4103, Rodriguez Moreno, Jesus|||0000-0002-5962-7364, Notario, Jaime|||0000-0001-9676-6147, Lorenzo Martín, Andrés, Brandy García, Anahy, Coto-Segura, P.|||0000-0002-6455-4825, López Ferrer, Anna|||0000-0002-2121-963X, Pérez Barrio, Silvia, Plata Izquierdo, Andrés J., Bustabad, Sagrario|||0000-0002-4204-1236, Guimerá Martín-Neda, Francisco J., Fonseca, Eduardo|||0000-0002-8048-2681, Rivera, Raquel|||0000-0002-4604-0724, Cuervo, Andrea, Alsina, Mercè, Trenor Larraz, Pilar, de la Morena Barrio, Isabel, Puchades Lanza, Laura, Bedoya-Sanchís, Diego, Meliá Mesquida, Catalina, Murillo, Claudia, Moreno Ramos, Manuel José|||0000-0003-3800-7382, Beteta Fernandez, Dolores, Sánchez-Pedreño, Paloma|||0000-0002-4974-4583, Lojo-Oliveira, Leticia|||0000-0002-8809-5942, Navío Marco, Teresa, Cebrián, Laura, De la Cueva, Pablo|||0000-0003-4807-3551, Steiner, Martina, Muñoz-Fernández, Santiago|||0000-0002-2758-2085, Valverde Garrido, Ricardo, León, Manuel, Rubio, Esteban, Muñoz-Jiménez, Alejandro|||0000-0001-8884-9225, Rodriguez Fernández-Freire, Lourdes, Medina Luezas, Julio|||0000-0002-8718-5786, Sánchez-González, María D., Sanz Muñoz, Carolina, Senabre Gallego, José Miguel|||0000-0002-8602-4198, Rosas, José|||0000-0002-3916-6046, Santos Soler, Gregorio, Mataix Díaz, Francisco J., Nieto González, Juan Carlos|||0000-0002-5971-4185, González, Carlos M.|||0000-0001-5511-8274, Ovalles-Bonilla, Juan|||0000-0001-6300-5341, Baniandrés Rodríguez, Ofelia|||0000-0001-8183-3941, Nóvoa Medina, Francisco Javier, Luján, Dunia, Ruiz Montesino, Maria Dolores|||0000-0002-9080-121X, Carrizosa Esquivel, Ana M., Fernández-Carballido, Cristina|||0000-0002-0910-4944, Martínez-Vidal, María P., García Fernández, Laura, Jovani, Vega|||0000-0001-8529-4551, Caño Alameda, Rocío|||0000-0002-2803-4347, Gómez Sabater, Silvia|||0000-0002-2833-565X, Belinchón, Isabel|||0000-0002-6007-7320, Urruticoechea-Arana, Ana, Serra Torres, Marta, Almodóvar, Raquel, López-Estebaranz, José-Luis|||0000-0002-8816-1989, López Montilla, Maria Dolores, García-Nieto, Antonio Vélez
Tipo de recurso: artículo
Fecha de publicación:2022
País:España
Institución:Universitat Autònoma de Barcelona
Repositorio:Dipòsit Digital de Documents de la UAB
Idioma:inglés
OAI Identifier:oai:ddd.uab.cat:286341
Acceso en línea:https://ddd.uab.cat/record/286341
https://dx.doi.org/urn:doi:10.1186/s13075-022-02838-2
Access Level:acceso abierto
Palabra clave:Machine learning
Minimal disease activity
Predictive model
Recent-onset psoriatic arthritis
Descripción
Sumario: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.

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