Disease burden in inflammatory arthritis: an unsupervised machine learning approach of the COVAD-2 e-survey dataset
2
Issued Date
2025-01-01
Resource Type
eISSN
25141775
Scopus ID
2-s2.0-105003312709
Journal Title
Rheumatology Advances in Practice
Volume
9
Issue
2
Rights Holder(s)
SCOPUS
Bibliographic Citation
Rheumatology Advances in Practice Vol.9 No.2 (2025)
Suggested Citation
Venerito V., Vescovo S.D., Prieto-González S., Fornaro M., Cavagna L., Iannone F., Kuwana M., Agarwal V., Day J., Joshi M., Saha S., Jagtap K., Katchamart W., Goo P.A., Vaidya B., Velikova T., Sen P., Shinjo S.K., Tan A.L., Ziade N., Milchert M., Gracia-Ramos A.E., Caballero-Uribe C.V., Chinoy H., Gupta L., Agarwal V. Disease burden in inflammatory arthritis: an unsupervised machine learning approach of the COVAD-2 e-survey dataset. Rheumatology Advances in Practice Vol.9 No.2 (2025). doi:10.1093/rap/rkaf031 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/109886
Title
Disease burden in inflammatory arthritis: an unsupervised machine learning approach of the COVAD-2 e-survey dataset
Author(s)
Author's Affiliation
Siriraj Hospital
Faculty of Biology, Medicine and Health
Seth GS Medical College and KEM Hospital
Hôtel-Dieu de France Hospital
Université Saint-Joseph de Beyrouth
Universidad del Norte
Hospital Clínic de Barcelona
Walter and Eliza Hall Institute of Medical Research
Fondazione IRCCS Policlinico San Matteo
Sanjay Gandhi Postgraduate Institute of Medical Sciences
University of Leeds, School of Medicine
Università degli studi di Bari Aldo Moro
Mymensingh Medical College
Sofia University St. Kliment Ohridski
University of Birmingham
Pomeranian Medical University in Szczecin
Nippon Medical School
Università degli Studi di Pavia, Facoltà di Medicina e Chirurgia
Maulana Azad Medical College
Instituto Mexicano del Seguro Social
Universidade de São Paulo
The Royal Wolverhampton NHS Trust
National Center for Rheumatic Diseases
Sassoon General Hospitals
Queen Savang Vadhana Memorial Hospital
Mahatma Gandhi Mission Medical College
Faculty of Biology, Medicine and Health
Seth GS Medical College and KEM Hospital
Hôtel-Dieu de France Hospital
Université Saint-Joseph de Beyrouth
Universidad del Norte
Hospital Clínic de Barcelona
Walter and Eliza Hall Institute of Medical Research
Fondazione IRCCS Policlinico San Matteo
Sanjay Gandhi Postgraduate Institute of Medical Sciences
University of Leeds, School of Medicine
Università degli studi di Bari Aldo Moro
Mymensingh Medical College
Sofia University St. Kliment Ohridski
University of Birmingham
Pomeranian Medical University in Szczecin
Nippon Medical School
Università degli Studi di Pavia, Facoltà di Medicina e Chirurgia
Maulana Azad Medical College
Instituto Mexicano del Seguro Social
Universidade de São Paulo
The Royal Wolverhampton NHS Trust
National Center for Rheumatic Diseases
Sassoon General Hospitals
Queen Savang Vadhana Memorial Hospital
Mahatma Gandhi Mission Medical College
Corresponding Author(s)
Other Contributor(s)
Abstract
Objectives: To comprehensively compare the disease burden among patients with RA, PsA and AS using Patient-Reported Outcome Measurement Information System (PROMIS) scores and to identify distinct patient clusters based on comorbidity profiles and PROMIS outcomes. Methods: Data from the global COVID-19 Vaccination in Autoimmune Diseases (COVAD) 2 e-survey were analysed. Patients with RA, PsA or AS undergoing treatment with DMARDs were included. PROMIS scores (global physical health, global mental health, fatigue 4a and physical function short form 10a), comorbidities and other variables were compared among the three groups, stratified by disease activity status. Unsupervised hierarchical clustering with eXtreme Gradient Boosting feature importance analysis was performed to identify patient subgroups based on comorbidity profiles and PROMIS outcomes. Results: The study included 2561 patients (1907 RA, 311 PsA, 343 AS). After adjusting for demographic factors, no significant differences in PROMIS scores were observed among the three groups, regardless of disease activity status. Clustering analysis identified four distinct patient groups: low burden, comorbid PsA/AS, low burden with depression and high-burden RA. Feature importance analysis revealed PROMIS global physical health as the strongest determinant of cluster assignment, followed by depression and diagnosis. The comorbid PsA/AS and high-burden RA clusters showed a higher prevalence of comorbidities (56.47% and 69.7%, respectively) and depression (41.18% and 41.67%, respectively), along with poorer PROMIS outcomes. Conclusion: Disease burden in inflammatory arthritis is determined by a complex interplay of factors, with physical health status and depression playing crucial roles. The identification of distinct patient clusters suggests the need for a paradigm shift towards more integrated care approaches that equally emphasize physical and mental health, regardless of the underlying diagnosis.