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

2022; BioMed Central; Volume: 24; Issue: 1 Linguagem: Inglês

10.1186/s13075-022-02838-2

1478-6362

Rubén Queiró, Daniel Seoane‐Mato, A. Laíz, Eva Galíndez Agirregoikoa, Carlos Montilla, Hye Sang Park, J. Pinto-Tasende, Juan José Bethencourt Baute, Beatriz Joven Ibáñez, Elide Toniolo, Julio Ramírez, Ana Serrano García, Juan D. Cañete, Xavier Juanola, Jordi Fiter, Jordi Gratacós, Jesús Rodríguez, Jaime Notario Rosa, Andrés Lorenzo Martín, Anahy Brandy García, Pablo Coto Segura, Anna López Ferrer, Silvia Pérez Barrio, Andrés J. Plata Izquierdo, Sagrario Bustabad, Francisco J. Guimerá Martín-Neda, Eduardo Fonseca Capdevilla, R. Rivera, Andrea Cuervo, M. Alsina Gibert, Pilar Trenor Larraz, Isabel de la Morena Barrio, Laura Puchades Lanza, D. Bedoya Sanchis, Catalina Meliá Mesquida, Claudia Murillo, Manuel José Moreno Ramos, Dolores Beteta Fernández, Paloma Sánchez‐Pedreño Guillén, L. Lojo, Teresa Navío Marco, Laura Cebrián, P. de la Cueva Dobao, Martina Steiner, Santiago Muñoz‐Fernández, Ricardo Valverde Garrido, Manuel León Navarro, Estéban Rubio, Alejandro Muñóz, Lourdes Rodríguez Fernández‐Freire, Julio Medina Luezas, M. Sánchez-González, Carolina Sanz Muñoz, José Gallego, José Carlos Rosas, Gregorio Santos Soler, Francisco J. Mataix Díaz, Juan Carlos Nieto‐González, Carlos González, Juan G. Ovalles Bonilla, Ofelia Baniandrés Rodríguez, Fco Javier Nóvoa Medina, Dunia Luján, D. Ruiz-Montesinos, Ana M. Carrizosa Esquivel, Cristina Fernández‐Carballido, María Paz Martínez-Vidal, Laura García Fernández, Vega Jovaní, Rocío Caño, Sílvia Gómez, I. Belinchón, Ana Urruticoechea‐Arana, Marta Serra Torres, Raquel Almodóvar, J.L. López‐Estebaranz, María D. López Montilla, Antonio Vélez García‐Nieto,

Psoriasis: Treatment and Pathogenesis

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.