Minimal disease activity (MDA) in patients with recent-onset psoriatic arthritis: predictive model based on machine learning
Authors: Queiro Rubén , Seoane-Mato Daniel , Laiz Ana , Agirregoikoa Eva Galíndez , Montilla Carlos , Park Hye-Sang , Pinto-Tasende Jose A. , Bethencourt Baute Juan J. , Ibáñez Beatriz Joven , Toniolo Elide , Ramírez Julio , García Ana Serrano ,   on behalf of Proyecto REAPSER Study Group, Cañete 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-Pedreño , Oliveira Leticia Lojo , Marco Teresa Navío , Cebrián Laura , de la Cueva Dobao Pablo , Steiner Martina , Muñoz-Fernández Santiago , Garrido Ricardo Valverde , León Manuel , Rubio Esteban , Jiménez Alejandro Muñoz , Fernández-Freire Lourdes Rodríguez , Luezas Julio Medina , Sánchez-González María D. , Muñoz 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 Caño , 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 
Published: June 2022

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.