AB1006 ASSESSMENT OF QRISK3 AS A PREDICTOR OF CARDIOVASCULAR DISEASE RISK IN PATIENTS WITH SYSTEMIC LUPUS ERYTHEMATOSUS
Authors: E. Pedretti, S. Bertocchi, M. Brunelli, I. P. Kodikara, I. Cavazzana, F. Franceschini, M. Fredi
Journal: Annals of the Rheumatic Diseases (2024)

<h3>Background:</h3> Systemic Lupus Erythematosus (SLE) is an independent risk factor for cardiovascular disease (CVD), which is one of the leading causes of death during the years after SLE diagnosis<sup>[1]</sup>. Traditional CVD risk algorithms might not fully capture non-traditional risk factors that can contribute to CVD development in autoimmune diseases such as SLE, underestimating risk in these patients. In 2017 a new model for CVD risk stratification, QRISK3, including specific items such as the presence of SLE and the use of glucocorticoids, was validated<sup>[2]</sup>. <h3>Objectives:</h3> (I) To apply QRISK3 and (II) to assess its performance in predicting CVD risk over the next 10 years (yrs) in a cohort of SLE patients. <h3>Methods:</h3> Patients with a diagnosis of SLE (2019 ACR/EULAR criteria) in follow-up at our Lupus Clinic were included between January-August 2023. QRISK3 scores were calculated at the most recent clinic appointment (T<sub>1</sub>) and as close to 10 yrs prior (T<sub>0</sub>), stratifying patients into two groups: low-risk (score &lt;10%) and high-risk (score ≥10%). Previous major adverse cardiovascular events (MACE) (<i>e.g.</i> coronary artery disease/myocardial infarction, transient ischemic attack/ischemic stroke, peripheral artery disease, pulmonary embolism) and age &lt;25/&gt;84 yrs were considered exclusion criteria for QRISK3 assessment. Traditional risk factors, ongoing therapies, comorbidities and SLE-specific evaluations were obtained through medical records. Data are presented as median (lower quartile; upper quartile) or mean and standard deviation depending on variable distribution. Subgroups with different CVD risk profile were compared using T-test. Logistic regression model was used to individuate variables independently associated with increased CVD risk. <h3>Results:</h3> 180 patients were enrolled (96% females, 92% Caucasians, median age 52 yrs [41-62], median disease duration 19 yrs [12-26]). At T<sub>1</sub> QRISK3 was calculated in 154 patients (26 were excluded for age or MACE) and revealed a mean risk score of 8.6% ±8.1 with 47 patients (31%) at high-risk. Statistical associations by univariate analysis of patients presenting high-risk profile are shown in Table 1; while in multivariate logistic regression analysis the two variables independently associated with increased CVD risk were age (p&lt;0.001) and having organ damage (SLICC p=0.001). At T<sub>0</sub> QRISK3 was calculated in 130 patients (50 were excluded for age, MACE or a follow up &lt;10 yrs) and revealed a mean risk score of 6.8% ±6.4, with 28 patients (22%) at high-risk significantly older (p&lt;0.001). During the 10-year follow-up period 13 patients (10% vs 7% of predicted) experienced at least one cardiovascular event (Table 2) and, of these, 46% had a QRISK3 score ≥10% at T<sub>0</sub>, whereas 19% of patients who did not have an event had a QRISK3 score ≥10%. Sensitivity and specificity at T<sub>0</sub> for QRISK3 ≥10% were 46.2% and 81.2%, while its positive and negative predictive value was 21.4% and 93.1% respectively. <h3>Conclusion:</h3> 31% of SLE patients presented a high-risk CVD profile and, according to previous data<sup>[2-4]</sup>, QRISK3 demonstrated a good performance in predicting risk of CVD in our cohort. Nevertheless, low positive predictive value and sensitivity reflect limitations of QRISK3 in its discriminatory ability, highlighting as the presence of SLE and active corticosteroid use may be not sufficient enough to accurately gauge CVD risk in SLE patients. Application of a SLE-adapted CVD risk scores, as well as monitoring of disease-specific features not included in QRISK3 (<i>e.g.</i> glucocorticoid overall exposure, antiphospholipid antibody positivity and disease activity), represent important aspects in the clinical practice to improve CVD risk estimation and management in SLE patients. <h3>REFERENCES:</h3> [1] Hak AE et al. Arthritis Rheum, 2009. [2] Hippisley-Cox J et al. BMJ, 2017. [3] Di Battista M et al. Clin Exp Rheumatol, 2020. [4] Barinotti A et al. Int J Cardiol, 2022 <h3>Acknowledgements:</h3> <b>NIL.</b> <h3>Disclosure of Interests:</h3> <b>None declared.</b>