Objectives: To compare two algorithms for cardiovascular (CV) risk estimation in systemic sclerosis (SSc) patients, investigating correlations with disease characteristics.
Methods: Traditional CV risk factors and SSc-specific characteristics were assessed in a cohort of SSc patients. Framingham and QRISK3 algorithms were used to estimate the risk of developing a CV disease over the next 10 years.
Results: Seventy-two SSc patients were enrolled. Among those 56 without previous CV events, Framingham reported a median risk score of 9.6%, classifying 24 (42.9%) subjects at high risk. QRISK3 showed a median risk score of 15.8%, with 36 (64.3%) patients considered at high risk. Both algorithms revealed a significant role of some traditional risk factors and a noteworthy potential protective role of endothelin receptor antagonists (p = .003). QRISK3 was also significantly influenced by some SSc-specific characteristics, such as limited cutaneous subset (p = .01), interstitial lung disease (p = .04), and non-ischemic heart involvement (p = .03), with the first two maintaining statistical significance in the multivariate analysis (p = .02).
Conclusions: QRISK3 classifies more SSc patients at high risk to develop CV diseases than Framingham, reflecting the influence of some SSc-specific characteristics. If its predictive accuracy were prospectively verified, the use of QRISK3 as a tool in the early detection of SSc patients at high CV risk should be recommended.
Keywords: Cardiovascular risk; algorithm performance; cardiovascular diseases; risk factors; systemic sclerosis.
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