TY - JOUR
T1 - Disease burden in inflammatory arthritis
T2 - an unsupervised machine learning approach of the COVAD-2 e-survey dataset
AU - COVAD Study
AU - Venerito, Vincenzo
AU - Del Vescovo, Sergio
AU - Prieto-González, Sergio
AU - Fornaro, Marco
AU - Cavagna, Lorenzo
AU - Iannone, Florenzo
AU - Kuwana, Masataka
AU - Agarwal, Vishwesh
AU - Day, Jessica
AU - Joshi, Mrudula
AU - Saha, Sreoshy
AU - Jagtap, Kshitij
AU - Katchamart, Wanruchada
AU - Akarawatcharangura Goo, Phonpen
AU - Vaidya, Binit
AU - Velikova, Tsvetelina
AU - Sen, Parikshit
AU - Shinjo, Samuel Katsuyuki
AU - Tan, Ai Lyn
AU - Ziade, Nelly
AU - Milchert, Marcin
AU - Edgar Gracia-Ramos, Abraham
AU - Caballero-Uribe, Carlo V
AU - Chinoy, Hector
AU - Gupta, Latika
AU - Agarwal, Vikas
N1 - © The Author(s) 2025. Published by Oxford University Press on behalf of the British Society for Rheumatology.
V.V. and S.D.V. contributed equally. The complete list of authors of the COVAD Study Group as well as their affiliations are provided in the supplement.
PY - 2025/4/18
Y1 - 2025/4/18
N2 - 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.
AB - 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.
KW - inflammatory arthritis
KW - disease burden
KW - spondyloarthritis
KW - rheumatoid arthritis
KW - patient reported outcome
KW - PROMIS (Patient-Reported Outcome Measurement Information System)
KW - mental health
KW - comorbidities
KW - cluster analysis
KW - survey
U2 - 10.1093/rap/rkaf031
DO - 10.1093/rap/rkaf031
M3 - Article
C2 - 40256633
SN - 2514-1775
VL - 9
JO - Rheumatology Advances in Practice
JF - Rheumatology Advances in Practice
IS - 2
M1 - rkaf031
ER -
