학술논문
Minimal disease activity (MDA) in patients with recent-onset psoriatic arthritis: predictive model based on machine learning
Document Type
Report
Author
Queiro, Rubén; Seoane-Mato, Daniel; Laiz, Ana; Agirregoikoa, Eva Galíndez; Montilla, Carlos; Park, Hye-Sang; Pinto-Tasende, Jose A.; Bethencourt Baute, Juan J.; Ibáéez, Beatriz Joven; Toniolo, Elide; Ramírez, Julio; García, Ana Serrano; Caéete, Juan D.; Juanola, Xavier; Fiter, Jordi; Gratacós, Jordi; Rodriguez-Moreno, Jesús; Rosa, Jaime Notario; Martín, Andrés Lorenzo; García, Anahy Brandy; Segura, Pablo Coto; Ferrer, Anna López; Barrio, Silvia Pérez; Plata Izquierdo, Andrés J.; Bustabad, Sagrario; Guimerá Martín-Neda, Francisco J.; Capdevilla, Eduardo Fonseca; Díaz, Raquel Rivera; Cuervo, Andrea; Gibert, Mercè Alsina; Larraz, Pilar Trenor; de la Morena Barrio, Isabel; Lanza, Laura Puchades; Sanchís, Diego Bedoya; Mesquida, Catalina Meliá; Murillo, Claudia; Moreno Ramos, Manuel J.; Beteta, María D.; Guillén, Paloma Sánchez-Pedreéo; Oliveira, Leticia Lojo; Marco, Teresa Navío; Cebrián, Laura; de la Cueva Dobao, Pablo; Steiner, Martina; Muéoz-Fernández, Santiago; Garrido, Ricardo Valverde; León, Manuel; Rubio, Esteban; Jiménez, Alejandro Muéoz; Fernández-Freire, Lourdes Rodríguez; Luezas, Julio Medina; Sánchez-González, María D.; Muéoz, Carolina Sanz; Senabre, José M.; Rosas, José C.; Soler, Gregorio Santos; Mataix Díaz, Francisco J.; Nieto-González, Juan C.; González, Carlos; Ovalles Bonilla, Juan G.; Rodríguez, Ofelia Baniandrés; Medina, Fco Javier Nóvoa; Luján, Dunia; Ruiz Montesino, María D.; Carrizosa Esquivel, Ana M.; Fernández-Carballido, Cristina; Martínez-Vidal, María P.; Fernández, Laura García; Jovani, Vega; Alameda, Rocío Caéo; Sabater, Silvia Gómez; Romero, Isabel Belinchón; Urruticoechea-Arana, Ana; Torres, Marta Serra; Almodóvar, Raquel; López Estebaranz, José L.; López Montilla, María D.; García-Nieto, Antonio Vélez
Source
Arthritis Research & Therapy. June 24, 2022, Vol. 24 Issue 1
Subject
Language
English
ISSN
1478-6354
Abstract
Author(s): Rubén Queiro[sup.1] , Daniel Seoane-Mato[sup.2] , Ana Laiz[sup.3] , Eva Galíndez Agirregoikoa[sup.4] , Carlos Montilla[sup.5] , Hye-Sang Park[sup.3] , Jose A. Pinto-Tasende[sup.6] , Juan J. Bethencourt Baute[sup.7] , Beatriz [...]
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 [greater than or equai to]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. Keywords: Recent-onset psoriatic arthritis, Minimal disease activity, Predictive model, 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 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 [greater than or equai to]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. Keywords: Recent-onset psoriatic arthritis, Minimal disease activity, Predictive model, Machine learning