Prevalence and pattern of comorbidities in chronic rheumatic and musculoskeletal diseases: the COMORD study

Abstract

Increased risk of comorbidities has been reported in Rheumatic and Musculoskeletal Diseases (RMD). We aimed to evaluate the prevalence and pattern of comorbidities in RMD patients nationwide, to identify multimorbidity clusters and to evaluate the gap between recommendations and real screening. Cross-sectional, multicentric nationwide study. Prevalence of comorbidities was calculated according to six EULAR axes. Latent Class Analysis identified multimorbidity clusters. Comorbidities' screening was compared to international and local recommendations. In 769 patients (307 RA, 213 OA, 63 SLE, 103 axSpA, and 83 pSA), the most frequent comorbidities were cardiovascular risk factors and diseases (CVRFD) (hypertension 36.5%, hypercholesterolemia 30.7%, obesity 22.7%, smoking 22.1%, diabetes 10.4%, myocardial infarction 6.6%), osteoporosis (20.7%) and depression (18.1%). Three clusters of multimorbidity were identified: OA, RA and axSpA. The most optimal screening was found for CVRF (> = 93%) and osteoporosis (53%). For malignancies, mammograms were the most optimally prescribed (56%) followed by pap smears (32%) and colonoscopy (21%). Optimal influenza and pneumococcus vaccination were found in 22% and 17%, respectively. Comorbidities were prevalent in RMD and followed specific multimorbidity patterns. Optimal screening was adequate for CVRFD but suboptimal for malignant neoplasms, osteoporosis, and vaccination. The current study identified health priorities, serving as a framework for the implementation of future comorbidity management standardized programs, led by the rheumatologist and coordinated by specialized health care professionals.

Figure 1

Multimorbidity patterns identified by Latent Class Analysis: Osteoarthritis, Rheumatoid Arthritis and axial Spondyloarthritis patterns. AxSpA: Axial SpondyloArthritis, OA: Osteoarthritis, CAD: Coronary Artery Disease, RA: Rheumatoid Arthritis. Each comorbidity is represented by its prevalence in each spidergram (for example, Smoking’s prevalence is 60% in Cluster 3).