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. 2025 Apr 18;9(2):rkaf031.
doi: 10.1093/rap/rkaf031. eCollection 2025.

Disease burden in inflammatory arthritis: an unsupervised machine learning approach of the COVAD-2 e-survey dataset

Vincenzo Venerito  1 , Sergio Del Vescovo  1 , Sergio Prieto-González  2 , Marco Fornaro  1 , Lorenzo Cavagna  3   4 , Florenzo Iannone  1 , Masataka Kuwana  5 , Vishwesh Agarwal  6 , Jessica Day  7 , Mrudula Joshi  8 , Sreoshy Saha  9 , Kshitij Jagtap  10 , Wanruchada Katchamart  11 , Phonpen Akarawatcharangura Goo  12 , Binit Vaidya  13 , Tsvetelina Velikova  14 , Parikshit Sen  15 , Samuel Katsuyuki Shinjo  16 , Ai Lyn Tan  17 , Nelly Ziade  18   19 , Marcin Milchert  20 , Abraham Edgar Gracia-Ramos  21 , Carlo V Caballero-Uribe  22 ; COVAD Study; Hector Chinoy  23 , Latika Gupta  23   24   25 , Vikas Agarwal  26
Collaborators, Affiliations

Disease burden in inflammatory arthritis: an unsupervised machine learning approach of the COVAD-2 e-survey dataset

Vincenzo Venerito et al. Rheumatol Adv Pract. .

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.

Keywords: PROMIS (Patient-Reported Outcome Measurement Information System); cluster analysis; comorbidities; disease burden; inflammatory arthritis; mental health; patient reported outcome; rheumatoid arthritis; spondyloarthritis; survey.

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Figures

Figure 1.
Figure 1.
Workflow chart of the study methodology
Figure 2.
Figure 2.
Dendrogram of the unsupervised hierarchical clustering on principal components of patients with RA, PsA and AS based on their comorbidity profiles and PROMIS scores. The coloured boxes indicate the assignment of patients to the four identified clusters
See this image and copyright information in PMC

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