Cardiovascular risk assessment in patients with antiphospholipid syndrome: a cross-sectional performance analysis of nine clinical risk prediction tools

Abstract

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); aGAPSSCVD; 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 aGAPSSCVD (for non-triple antiphospholipid antibody-positive patients) performed better (z/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.