抗磷脂综合征患者的心血管风险评估：九种临床风险预测工具的横断面性能分析。
Cardiovascular risk assessment in patients with antiphospholipid syndrome: a cross-sectional performance analysis of nine clinical risk prediction tools.
机构信息
Cardiovascular Risk Research Laboratory, First Department of Propaedeutic Internal Medicine, National and Kapodistrian University of Athens School of Medicine, Athens, Greece.
Rheumatology Unit, First Department of Propaedeutic Internal Medicine and Joint Academic Rheumatology Program, National and Kapodistrian University of Athens School of Medicine, Athens, Greece.
出版信息
RMD Open. 2023 Nov 27;9(4):e003601. doi: 10.1136/rmdopen-2023-003601.
OBJECTIVES
This study aimed to assess the performance of cardiovascular risk (CVR) prediction models reported by European Alliance of Associations for Rheumatology and European Society of Cardiology recommendations to identify high-atherosclerotic CVR (ASCVR) patients with antiphospholipid syndrome (APS).
METHODS
Six models predicting the risk of a first cardiovascular disease event (first-CVD) (Systematic Coronary Risk Evaluation (SCORE); modified-SCORE; Framingham risk score; Pooled Cohorts Risk Equation; Prospective Cardiovascular Münster calculator; Globorisk), three risk prediction models for patients with a history of prior arterial events (recurrent-CVD) (adjusted Global APS Score (aGAPSS); aGAPSS; Secondary Manifestations of Arterial Disease (SMART)) and carotid/femoral artery vascular ultrasound (VUS) were used to assess ASCVR in 121 APS patients (mean age: 45.8±11.8 years; women: 68.6%). We cross-sectionally examined the calibration, discrimination and classification accuracy of all prediction models to identify high ASCVR due to VUS-detected atherosclerotic plaques, and risk reclassification of patients classified as non high-risk according to first-CVD/recurrent-CVD tools to actual high risk based on VUS.
RESULTS
Spiegelhalter's z-test p values 0.47-0.57, area under the receiver-operating characteristics curve (AUROC) 0.56-0.75 and Matthews correlation coefficient (MCC) 0.01-0.35 indicated moderate calibration, poor-to-acceptable discrimination and negligible-to-moderate classification accuracy, respectively, for all risk models. Among recurrent-CVD tools, SMART and aGAPSS (for non-triple antiphospholipid antibody-positive patients) performed better (/AUROC/MCC: 0.47/0.64/0.29 and 0.52/0.69/0.29, respectively) than aGAPSS. VUS reclassified 34.2%-47.9% and 40.5%-52.6% of patients classified as non-high-ASCVR by first-CVD and recurrent-CVD prediction models, respectively. In patients aged 40-54 years, >40% VUS-guided reclassification was observed for first-CVD risk tools and >50% for recurrent-CVD prediction models.
CONCLUSION
Clinical CVR prediction tools underestimate actual high ASCVR in APS. VUS may help to improve CVR assessment and optimal risk factor management.
目的
本研究旨在评估欧洲风湿病联盟和欧洲心脏病学会推荐的心血管风险（CVR）预测模型在识别抗磷脂综合征（APS）中具有高动脉粥样硬化 CVR（ASCVR）的患者方面的表现。
方法
使用 6 种预测首次心血管疾病事件（first-CVD）风险的模型（系统性冠状动脉风险评估（SCORE）；改良 SCORE；弗雷明汉风险评分；汇总队列风险方程；前瞻性心血管明斯特计算器；Globorisk），3 种预测既往动脉事件（recurrent-CVD）患者风险的模型（调整后的全球 APS 评分（aGAPSS）；aGAPSS；动脉疾病的次要表现（SMART））和颈动脉/股动脉血管超声（VUS），评估 121 例 APS 患者的 ASCVR（平均年龄：45.8±11.8 岁；女性：68.6%）。我们通过血管超声检测到的动脉粥样硬化斑块，对所有预测模型的校准、区分和分类准确性进行了横断面检查，以识别出由于 VUS 检测到的动脉粥样硬化斑块而导致的 ASCVR 升高，并根据 first-CVD/recurrent-CVD 工具将分类为非高危的患者进行风险再分类，根据 VUS 将其重新归类为实际高危患者。
结果
Spiegelhalter 的 z 检验 p 值为 0.47-0.57，受试者工作特征曲线下面积（AUROC）为 0.56-0.75，马修斯相关系数（MCC）为 0.01-0.35，表明所有风险模型的校准均为中度，区分度差至可接受，分类准确性低至中度。在复发 CVD 工具中，SMART 和 aGAPSS（用于非三抗磷脂抗体阳性患者）的表现优于 aGAPSS（/AUROC/MCC：0.47/0.64/0.29 和 0.52/0.69/0.29）。VUS 将 first-CVD 和 recurrent-CVD 预测模型分类为非高 ASCVR 的患者分别重新分类 34.2%-47.9%和 40.5%-52.6%。在年龄为 40-54 岁的患者中，first-CVD 风险工具的 VUS 指导再分类率超过 40%，而复发 CVD 预测模型的再分类率超过 50%。
结论
临床 CVR 预测工具低估了 APS 患者的实际高 ASCVR。VUS 可能有助于改善 CVR 评估和最佳危险因素管理。
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