Rheumatology Unit, First Department of Propaedeutic Internal Medicine, Joint Academic Rheumatology Program, School of Medicine, National and Kapodistrian University of Athens, Laiko General Hospital, Athens, Greece.
Cardiovascular Risk Research Laboratory, First Department of Propaedeutic Internal Medicine, School of Medicine, National and Kapodistrian University of Athens, Laiko General Hospital, Athens, Greece.
Lupus Sci Med. 2023 Mar;10(1). doi: 10.1136/lupus-2022-000864.
Studies show that generic cardiovascular risk (CVR) prediction tools may underestimate CVR in SLE. We examined, for the first time to our knowledge, whether generic and disease-adapted CVR scores may predict subclinical atherosclerosis progression in SLE.
We included all eligible patients with SLE without a history of cardiovascular events or diabetes mellitus, who had a 3-year carotid and femoral ultrasound follow-up examination. Five generic (Systematic Coronary Risk Evaluation (SCORE), Framingham Risk Score (FRS), Pooled Cohort Risk Equation, Globorisk, Prospective Cardiovascular Münster) and three 'SLE-adapted' CVR scores (modified Systematic Coronary Risk Evaluation (mSCORE), modified Framingham Risk Score (mFRS), QRESEARCH Risk Estimator V.3 (QRISK3)) were calculated at baseline. The performance of CVR scores to predict atherosclerosis progression (defined as new atherosclerotic plaque development) was tested with Brier Score (BS), area under the receiver operating characteristic curve (AUROC) and Matthews correlation coefficient (MCC), while rank correlation was tested with Harrell's -index. Binary logistic regression was also applied to examine determinants of subclinical atherosclerosis progression.
Twenty-six (21%) of 124 included patients (90% female, mean age 44.4±11.7 years) developed new atherosclerotic plaques after a mean of 39.7±3.8 months' follow-up period. Performance analysis showed that plaque progression was better predicted by the mFRS (BS 0.14, AUROC 0.80, MCC 0.22) and QRISK3 (BS 0.16, AUROC 0.75, MCC 0.25). -Index showed no superiority for discrimination between mFRS and QRISK3. In the multivariate analysis, QRISK3 (OR 4.24, 95% CI 1.30 to 13.78, p=0.016) among the CVR prediction scores and age (OR 1.13, 95% CI 1.06 to 1.21, p<0.001), cumulative glucocorticoid dose (OR 1.04, 95% CI 1.01 to 1.07, p=0.010) and antiphospholipid antibodies (OR 3.66, 95% CI 1.24 to 10.80, p=0.019) among disease-related CVR factors were independently associated with plaque progression.
Application of SLE-adapted CVR scores such as QRISK3 or mFRS, as well as monitoring for glucocorticoid exposure and the presence of antiphospholipid antibodies, can help to improve CVR assessment and management in SLE.
研究表明,通用心血管风险(CVR)预测工具可能会低估 SLE 患者的 CVR。我们首次研究了通用和疾病适应的 CVR 评分是否可以预测 SLE 患者的亚临床动脉粥样硬化进展。
我们纳入了所有无心血管事件或糖尿病病史的 SLE 患者,他们进行了为期 3 年的颈动脉和股动脉超声随访检查。计算了 5 种通用(系统性冠状动脉风险评估(SCORE)、弗雷明汉风险评分(FRS)、 pooled Cohort Risk Equation、Globorisk、Prospective Cardiovascular Münster)和 3 种“ SLE 适应”CVR 评分(改良系统性冠状动脉风险评估(mSCORE)、改良弗雷明汉风险评分(mFRS)、QRISK3)在基线时。使用 Brier 评分(BS)、接受者操作特征曲线下面积(AUROC)和马修斯相关系数(MCC)测试 CVR 评分预测动脉粥样硬化进展(定义为新的动脉粥样硬化斑块形成)的性能,同时使用 Harrell 的 - 指数测试秩相关。还应用二元逻辑回归来检查亚临床动脉粥样硬化进展的决定因素。
在平均 39.7±3.8 个月的随访后,26 名(21%)124 名纳入患者(90%为女性,平均年龄 44.4±11.7 岁)出现新的动脉粥样硬化斑块。性能分析表明,mFRS(BS 0.14、AUROC 0.80、MCC 0.22)和 QRISK3(BS 0.16、AUROC 0.75、MCC 0.25)更好地预测斑块进展。- 指数在 mFRS 和 QRISK3 之间没有显示出更好的判别能力。在多变量分析中,CVR 预测评分中的 QRISK3(OR 4.24、95%CI 1.30 至 13.78、p=0.016)和年龄(OR 1.13、95%CI 1.06 至 1.21、p<0.001)、累积糖皮质激素剂量(OR 1.04、95%CI 1.01 至 1.07、p=0.010)和抗磷脂抗体(OR 3.66、95%CI 1.24 至 10.80、p=0.019)是与斑块进展相关的疾病相关 CVR 因素的独立相关因素。
应用 SLE 适应的 CVR 评分,如 QRISK3 或 mFRS,以及监测糖皮质激素暴露和抗磷脂抗体的存在,可以帮助改善 SLE 患者的 CVR 评估和管理。