印度人群心血管风险评分预测准确性与亚临床动脉粥样硬化的相关性:横断面研究

Elanchezhian Selvaprakash, Kannan Kumaresan, Narendran Mani, Viswanathan Narasimhan
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引用次数: 0

摘要

背景:估计未来发生动脉粥样硬化性心血管(CV)事件的风险是初级预防 CV 事件的重要一步。然而,目前可用的各种心血管风险评分的相对准确性尚未在印度人中得到验证:本研究旨在比较三种临床相关的心血管风险评估算法在南印度人群中的准确性,并通过与亚临床动脉粥样硬化的测量指标相关联来验证风险评分:这项横断面研究在南印度一家三级医疗中心的胸痛门诊就诊患者中进行。研究对象年龄≥30 岁,既往无冠状动脉疾病(CAD),无重大心脏疾病。研究共纳入 110 名受试者。研究人员进行了详细的临床评估和常规检查。每位受试者的 10 年冠心病风险都是通过三种风险评分计算得出的:弗拉明汉、美国心脏病学会/美国心脏协会(ACC/AHA)和 Q 风险评分。然后将所有患者的风险评分与其相应的颈动脉多普勒测量的颈动脉内膜中层厚度(CIMT)和冠状动脉造影结果相关联:研究对象的平均年龄为(51.45±9.01)岁,女性占大多数(57.2%)。CAD 患者的 CIMT 明显增加。三种风险评分与颈动脉内膜内侧厚度之间存在明显的正相关(P<0.001)。随着颈动脉内膜内侧厚度的增加,风险评分也随之增加(如皮尔逊相关系数所示)。同样,所有三个风险评分与 CAG 评估的 CAD 严重程度也呈显著正相关(P<0.001)(如单因素方差分析所示)。ACC/AHA 评分是最佳评分,准确率为 69.9%,略高于 Q 风险评分(69.5%)。弗雷明汉评分的准确率为 68.3%:结论:ACC/AHA 和 Q 风险评分可能是目前最适合印度人群的冠心病风险评估算法。结论:ACC/AHA 和 Q 风险评分可能是目前最适合印度人群的 CV 风险评估算法,但还需要大规模的前瞻性研究来证实这些发现。
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Cardiovascular risk score predictive accuracy and subclinical atherosclerosis correlation in the Indian population: A cross-sectional study
Background: Estimation of the risk of future atherosclerotic cardiovascular (CV) events is an important step in the primary prevention of CV events. However, the relative accuracy of the various currently available CV risk scores is not validated in Indians. Aims and Objectives: The study was done to compare the accuracy of three clinically relevant CV risk assessment algorithms in the South Indian population and to validate the risk scores by correlating with the measures of subclinical atherosclerosis. Materials and Methods: This cross-sectional study was conducted among patients attending a chest pain clinic at a Tertiary care center in South India. The study included subjects ≥30 years of age, with no previous coronary artery disease (CAD) and major cardiac illness. Totally 110 subjects were included in the study. Detailed clinical evaluation and routine investigations were done. The 10-year CV risk for each subject was calculated using the three risk scores – Framingham, American College of Cardiology/American Heart Association (ACC/AHA), and Q risk score. The risk scores of all patients were then correlated with their corresponding carotid intima-media thickness (CIMT) measured using carotid Doppler and coronary angiography results. Results: The mean age of the study population was 51.45±9.01 years and the majority of them were females (57.2%). CAD patients demonstrated significantly increased CIMT. There was a significant positive correlation (P<0.001) between all three risk scores and carotid intimal medial thickness. As the carotid intimal medial thickness increases, the risk scores also increased (as shown by Pearsons’s correlation coefficient). Similarly, all three risk scores also showed a significant positive correlation (P<0.001) with the severity of CAD as assessed by CAG (as shown by one-way analysis of variance). The ACC/AHA score was the best score with a slightly higher accuracy of 69.9% than that of Q risk score (69.5%). The accuracy of Framingham’s score was found to be 68.3%. Conclusion: ACC/AHA and Q risk score may be the most appropriate CV risk assessment algorithm for use in Indian populations at present. However, large-scale prospective studies are needed to confirm these findings.
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