Open AccessTraditional and disease-related non-computed variables affect algorithms for cardiovascular risk estimation in Sjögren's syndrome and rheumatoid arthritis
Author(s) -
Giacomo Cafaro,
Carlo Perricone,
Ilenia Riccucci,
Roberto Bursi,
Santina Calvacchi,
Alessia Alunno,
Francesco Carubbi,
Roberto Gerli,
Elena Bartoloni
Publication year - 2021
Publication title -
clinical and experimental rheumatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.184
H-Index - 95
eISSN - 1593-098X
pISSN - 0392-856X
DOI - 10.55563/clinexprheumatol/xef8uz
Subject(s) - medicine , rheumatoid arthritis , cohort , algorithm , body mass index , framingham risk score , population , disease , environmental health , computer science
Several cardiovascular (CV) risk algorithms are available to predict CV events in the general population. Their performance and validity in rheumatic disease patients is suboptimal as some disease-specific variables which strongly contribute to the pathogenesis of CV disease are not included in these CV algorithms. We aimed to evaluate the performance of two CV algorithms and investigate which variables not included in the score contribute to CV risk score in a cohort of rheumatoid arthritis (RA) and Sjögren's syndrome (SS) patients.