Severe Disease in Patients With Recent-Onset Psoriatic Arthritis. Prediction Model Based on Machine Learning.

Autor:	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 HS; Rheumatology and Autoimmune Disease Department, Hospital Universitari de la Santa Creu i Sant Pau, Barcelona, Spain., Pinto Tasende JA; Rheumatology Service-INIBIC, Complexo Hospitalario Universitario de A Coruña, A Coruña, Spain., Bethencourt Baute JJ; 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.
Jazyk:	angličtina
Zdroj:	Frontiers in medicine [Front Med (Lausanne)] 2022 Apr 28; Vol. 9, pp. 891863. Date of Electronic Publication: 2022 Apr 28 (Print Publication: 2022).
DOI:	10.3389/fmed.2022.891863
Abstrakt:	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. Competing Interests: DS-M received honoraria from Galapagos for an educational event. AL received payment or honoraria for speakers’ bureaus and educational events, support for attending meetings, and participation on Advisory Boards from Novartis, Pfizer, Amgen, Janssen, and Lilly. EG received payment for presentations, support for attending meetings, and participation on Advisory Boards from Novartis, Pfizer, Amgen, Janssen, Lilly, AbbVie, MSD, Roche, and UCB. JP received payment for presentations, support for attending meetings, and participation on Advisory Boards from Janssen, Novartis, and Lilly. JB received payment for a presentation from Amgen and support for attending meetings from AbbVie and Pfizer. BJ received payment for speaker bureau, support for attending meetings, and participation on Advisory Boards from Novartis, UCB, and Amgen. JR received consulting fees, payment for presentations, support for attending meetings, and participation on Advisory Boards from MSD, Novartis, AbbVie, Pfizer, Janssen, Amgen, UCB, and Lilly. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2022 Queiro, Seoane-Mato, Laiz, Galindez Agirregoikoa, Montilla, Park, Pinto Tasende, Bethencourt Baute, Joven Ibáñez, Toniolo, Ramírez and Pruenza García-Hinojosa.)
Databáze:	MEDLINE
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