OBJECTIVES: Our objective was to compare three algorithms for cardiovascular (CV) risk estimation, namely Framingham, ACC/AHA and QRISK3, in a cohort of patients with systemic lupus erythematosus (SLE). METHODS: Consecutive patients with SLE according to the ACR criteria were enrolled. Traditional risk factors, ongoing therapies, comorbidities and SLE-specific evaluations were assessed. In those without previous myocardial infarction or stroke, Framingham, ACC/AHA and QRISK3 algorithms were then used to estimate the individual risk of developing a CV disease over the next 10 years. RESULTS: Patients eligible for CV risk estimation were 123 out of 135 enrolled. Framingham index reported a median risk score of 4.7% (IQR 9.5-2.2), considering 29 patients (23.6%) at high CV risk. ACC/AHA index showed a median risk score of 1.4% (IQR 4.5-0.7), with 17 patients (13.8%) at high-risk. QRISK3 revealed a median risk score of 6.2% (IQR 12.5-2.8), making it possible to classify 44 patients (35.8%) at high CV risk. The subgroup analysis of subjects older than 40 years confirmed the same number of high-risk patients for both Framingham and ACC/AHA, whereas QRISK3 classified 38 subjects at high CV risk. CONCLUSIONS: QRISK3 classifies a greater number of SLE patients at high-risk of developing CV diseases over the next 10 years in comparison with classic algorithms as Framingham and ACC/AHA. If its predictive accuracy were confirmed by longitudinal data, QRISK3 could become an important tool in the early detection of a considerable part of CV high-risk SLE patients that would be underestimated when applying classic algorithms.
OBJECTIVES: Our objective was to compare three algorithms for cardiovascular (CV) risk estimation, namely Framingham, ACC/AHA and QRISK3, in a cohort of patients with systemic lupus erythematosus (SLE). METHODS: Consecutive patients with SLE according to the ACR criteria were enrolled. Traditional risk factors, ongoing therapies, comorbidities and SLE-specific evaluations were assessed. In those without previous myocardial infarction or stroke, Framingham, ACC/AHA and QRISK3 algorithms were then used to estimate the individual risk of developing a CV disease over the next 10 years. RESULTS:Patients eligible for CV risk estimation were 123 out of 135 enrolled. Framingham index reported a median risk score of 4.7% (IQR 9.5-2.2), considering 29 patients (23.6%) at high CV risk. ACC/AHA index showed a median risk score of 1.4% (IQR 4.5-0.7), with 17 patients (13.8%) at high-risk. QRISK3 revealed a median risk score of 6.2% (IQR 12.5-2.8), making it possible to classify 44 patients (35.8%) at high CV risk. The subgroup analysis of subjects older than 40 years confirmed the same number of high-risk patients for both Framingham and ACC/AHA, whereas QRISK3 classified 38 subjects at high CV risk. CONCLUSIONS: QRISK3 classifies a greater number of SLEpatients at high-risk of developing CV diseases over the next 10 years in comparison with classic algorithms as Framingham and ACC/AHA. If its predictive accuracy were confirmed by longitudinal data, QRISK3 could become an important tool in the early detection of a considerable part of CV high-risk SLEpatients that would be underestimated when applying classic algorithms.