Severe Disease in Patients With Recent-Onset Psoriatic Arthritis. Prediction Model Based on Machine Learning
Por:
Queiro R., Seoane-Mato D., Laiz A., Galindez Agirregoikoa E., Montilla C., Park H.S., Pinto Tasende J.A., Bethencourt Baute J.J., Joven Ibáñez B., Toniolo E., Ramírez J., Pruenza García-Hinojosa C.
Publicada:
1 ene 2022
Resumen:
Objectives: To identify patient- and disease-related characteristics that make it possible to predict higher disease severity in 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. Severe disease was defined at each visit as fulfillment of at least 1 of the following criteria: need for systemic treatment, Health Assessment Questionnaire (HAQ) > 0.5, polyarthritis. The dataset contained data for the independent variables from the baseline visit and follow-up visit number 1. These were matched with the outcome measures from follow-up visits 1 and 2, respectively. We trained a logistic regression model and random forest–type and XGBoost machine learning algorithms to analyze the association between the outcome measure and the variables selected in the bivariate analysis. Results: The sample comprised 158 patients. At the first follow-up visit, 78.2% of the patients who attended the clinic had severe disease. This percentage decreased to 76.4% at the second visit. The variables predicting severe disease were patient global pain, treatment with synthetic DMARDs, clinical form at diagnosis, high CRP, arterial hypertension, and psoriasis affecting the gluteal cleft and/or perianal area. The mean values of the measures of validity of the machine learning algorithms were all = 80%. Conclusion: Our prediction model of severe disease advocates rigorous control of pain and inflammation, also addressing cardiometabolic comorbidities, in addition to actively searching for hidden psoriasis. Copyright © 2022 Queiro, Seoane-Mato, Laiz, Galindez Agirregoikoa, Montilla, Park, Pinto Tasende, Bethencourt Baute, Joven Ibáñez, Toniolo, Ramírez and Pruenza García-Hinojosa.
Filiaciones:
Queiro R.:
Faculty of Medicine, Rheumatology Service the Principality of Asturias Institute for Health Research (ISPA), Universidad de Oviedo, Oviedo, Spain
Seoane-Mato D.:
Research Unit, Spanish Society of Rheumatology, Madrid, Spain
Laiz A.:
Rheumatology and Autoimmune Disease Department, Hospital Universitari de la Santa Creu i Sant Pau, Barcelona, Spain
Galindez Agirregoikoa E.:
Rheumatology Service, Hospital Universitario Basurto, Bilbao, Spain
Montilla C.:
Rheumatology Service, Hospital Universitario de Salamanca, Salamanca, Spain
Park H.S.:
Rheumatology and Autoimmune Disease Department, Hospital Universitari de la Santa Creu i Sant Pau, Barcelona, Spain
Pinto Tasende J.A.:
Rheumatology Service-INIBIC, Complexo Hospitalario Universitario de A Coruña, A Coruña, Spain
Bethencourt Baute J.J.:
Rheumatology Service, Hospital Universitario de Canarias, Santa Cruz de Tenerife, Spain
Joven Ibáñez B.:
Rheumatology Service, Hospital Universitario, 12 de Octubre, Madrid, Spain
Toniolo E.:
Rheumatology Service, Hospital Universitari Son Llàtzer, Palma, Spain
Ramírez J.:
Arthritis Unit, Rheumatology Department, Hospital Clínic Barcelona, Barcelona, Spain
Pruenza García-Hinojosa C.:
Knowledge Engineering Institute, Universidad Autónoma de Madrid, Madrid, Spain
All Open Access, Gold
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