Traditional and disease-related non-computed variables affect algorithms for cardiovascular risk estimation in...

Area of Science:

• Rheumatology
• Cardiovascular Medicine
• Epidemiology

Background:

• Cardiovascular (CV) risk algorithms often underestimate risk in rheumatic diseases due to exclusion of disease-specific factors.
• Rheumatoid arthritis (RA) and Sjögren's syndrome (SS) patients have unique pathophysiological pathways contributing to CV disease.
• Accurate CV risk stratification is essential for managing these patient populations.

Purpose of the Study:

• To evaluate the performance of two established CV risk algorithms (Reynolds Risk Score and "Progetto Cuore") in RA and SS cohorts.
• To identify specific variables not included in these algorithms that significantly predict CV risk in RA and SS patients.
• To inform the development of more accurate CV risk assessment tools for individuals with rheumatic conditions.

Main Methods:

• A cohort of 77 RA patients and 68 SS patients without prior CV events was analyzed.
• Clinical, serological, and traditional CV risk factors were collected for all participants.
• Ten-year CV risk was calculated using the Reynolds Risk Score (RSS) and "Progetto Cuore" algorithms.

Main Results:

• The prevalence of traditional CV risk factors and predicted 10-year CV risk were comparable between RA and SS cohorts.
• In RA, body mass index (BMI) and disease activity predicted "Progetto Cuore" risk, while BMI and bone erosions predicted RSS.
• In SS, C-reactive protein predicted "Progetto Cuore" risk, whereas hypertension, ESSDAI, and LDL-cholesterol predicted RSS.

Conclusions:

• The 10-year risk for fatal and non-fatal CV events is similar in RA and SS patients.
• Traditional CV risk factors, such as hypertension, significantly influence CV risk in these rheumatic disease cohorts.
• Inflammatory parameters and disease activity are critical disease-specific variables that warrant inclusion in future CV risk assessment algorithms for rheumatic diseases.