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2024年心血管事件风险预测算法和2013年动脉粥样硬化性心血管疾病算法对类风湿关节炎患者高心血管风险和颈动脉斑块的识别不足:来自墨西哥队列的研究结果

Inadequate identification of high cardiovascular risk and carotid plaques in rheumatoid arthritis patients by the 2024 Predicting Risk of Cardiovascular EVENTs and the 2013 Atherosclerotic Cardiovascular Disease algorithms: findings from a Mexican cohort.

作者信息

Guajardo-Jauregui Natalia, Cardenas-de la Garza Jesus Alberto, Galarza-Delgado Dionicio Angel, Azpiri-Lopez Jose Ramon, Arvizu-Rivera Rosa Icela, Polina-Lugo Rebeca Lizeth, Colunga-Pedraza Iris Jazmin

机构信息

Internal Medicine Department, Hospital Universitario "Dr. Jose Eleuterio Gonzalez", Universidad Autonoma de Nuevo Leon, Monterrey, Mexico.

Rheumatology Service, Internal Medicine Department, Hospital Universitario "Dr. Jose Eleuterio Gonzalez", Universidad Autonoma de Nuevo Leon, Monterrey, Mexico.

出版信息

Clin Rheumatol. 2025 Jan;44(1):161-169. doi: 10.1007/s10067-024-07249-z. Epub 2024 Dec 10.

Abstract

The American College of Cardiology/American Heart Association introduced the Predicting Risk of Cardiovascular EVENTs (PREVENT™) algorithm to estimate the 10-year risk of developing cardiovascular disease. We aimed to assess the cardiovascular risk (CVR) reclassification among rheumatoid arthritis (RA) patients using traditional CVR algorithms-the 2024 PREVENT™ and the 2013 Atherosclerotic Cardiovascular Disease (ASCVD)-and the presence of carotid plaque (CP). This was a cross-sectional study nested of a RA patients' cohort. A certified radiologist performed a high-resolution B-mode carotid ultrasound to identify the presence of CP. The CVR evaluation was performed by a cardiologist, blinded to carotid ultrasound results, using the PREVENT™ and the ASCVD algorithms. Cohen's kappa (k) coefficient assessed concordance between high-risk classification by CVR algorithms and CP presence. ROC curve analysis evaluated the algorithms' capacity to identify RA patients with CP. The cutoff point was determined by the Youden-Index, with p < 0.05 as statistically significant. A total of 210 RA patients were included. The reclassification of CVR due to CP was 34.3% for the PREVENT™ algorithm and 30.0% for the ASCVD algorithm. Of these, 44.4% and 71.4%, respectively, were initially classified as low risk. Concordance between CVR algorithms and carotid ultrasound showed slight agreement (k = 0.032 and k = 0.130, respectively). The PREVENT™ algorithm did not identify more than one-third of high-CVR RA patients with indication of starting statin therapy based on carotid ultrasound findings. The PREVENT™ and ASCVD algorithms showed poor performance in identifying RA patients with CP. Key Points • The presence of CP was identified in more than a third of the evaluated RA patients (35.7%), classifying them as high CVR. • CVR reclassification by the presence of CP was observed in 34.3% RA patients with the PREVENTTM algorithm and in 30.0% RA patients with the ASCVD algorithm. • Most of the reclassified patients belonged to the low-risk category, 44.4% with the PREVENTTM algorithm and 71.4% with the ASCVD algorithm. • When evaluating the concordance between the ASCVD algorithm and the carotid ultrasound for high-risk classification, a slight agreement was found (k = 0.130).

摘要

美国心脏病学会/美国心脏协会推出了心血管事件预测(PREVENT™)算法,以评估患心血管疾病的10年风险。我们旨在使用传统的心血管风险(CVR)算法——2024年PREVENT™和2013年动脉粥样硬化性心血管疾病(ASCVD)算法以及颈动脉斑块(CP)的存在情况,评估类风湿关节炎(RA)患者的心血管风险重新分类。这是一项嵌套于RA患者队列的横断面研究。一名经过认证的放射科医生进行了高分辨率B型颈动脉超声检查,以确定是否存在CP。CVR评估由一名对颈动脉超声结果不知情的心脏病专家使用PREVENT™和ASCVD算法进行。科恩kappa(k)系数评估了CVR算法的高风险分类与CP存在之间的一致性。ROC曲线分析评估了算法识别患有CP的RA患者的能力。截断点由约登指数确定,p<0.05具有统计学意义。总共纳入了210名RA患者。PREVENT™算法因CP导致的CVR重新分类为34.3%,ASCVD算法为30.0%。其中,分别有44.4%和71.4%最初被归类为低风险。CVR算法与颈动脉超声之间的一致性显示为轻微一致(k分别为0.032和0.130)。PREVENT™算法未能识别出超过三分之一的根据颈动脉超声检查结果有开始他汀类药物治疗指征的高CVR RA患者。PREVENT™和ASCVD算法在识别患有CP的RA患者方面表现不佳。要点•在超过三分之一的评估RA患者(35.7%)中发现了CP的存在,将他们归类为高CVR。•在34.3%的RA患者中观察到因CP导致的CVR重新分类,使用PREVENTTM算法;在30.0%的RA患者中使用ASCVD算法。•大多数重新分类的患者属于低风险类别,使用PREVENTTM算法的为44.4%,使用ASCVD算法的为71.4%。•在评估ASCVD算法与颈动脉超声在高风险分类方面的一致性时,发现轻微一致(k = 0.130)。

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