Traditional and disease-related non-computed variables affect algorithms for cardiovascular risk estimation in Sjögren's syndrome and rheumatoid arthritis.

Abstract

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. A consecutive cohort of 77 RA and 68 SS patients without prior CV events was included. Clinical and serological features and traditional CV risk factors were collected. The 10-year CV risk was assessed by Reynold Risk Score (RSS) and "Progetto Cuore" algorithms. Prevalence of traditional CV risk factors and 10-year risk of fatal and non-fatal CV events assessed by RSS and "Progetto Cuore" were similar between the two cohorts. Multiple linear regression model showed that, among variables not included in both algorithms, body mass index (BMI) and disease activity were predictors of "Progetto Cuore" while BMI and bone erosions of RSS in RA. In SS, C-reactive protein was predictor of "Progetto Cuore" while hypertension, ESSDAI and LDL-cholesterol of RSS. The 10-year risk of fatal and non-fatal CV events is similar in RA and SS. Traditional CV risk factors, as hypertension, strongly contribute to CV risk in these patients. Inflammatory parameters and disease activity are two disease-specific variables which should be included in CV algorithm assessment in rheumatic disease patients.