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冠状动脉风险评分的一致性评估:对心血管风险筛查的意义

Concordance evaluation of coronary risk scores: implications for cardiovascular risk screening.

作者信息

Reynolds Timothy M, Twomey Patrick J, Wierzbicki Anthony S

机构信息

Consultant Chemical Pathologist, Queen's Hospital, Burton-on-Trent and Division of Clinical Sciences, Wolverhampton University, UK.

出版信息

Curr Med Res Opin. 2004 Jun;20(6):811-8. doi: 10.1185/030079904125003647.

Abstract

OBJECTIVE

To assess the similarities and differences in predicted high-risk individuals identified by different cardiovascular risk calculation algorithms Research design and methods: A representative population of 10000 individuals was modelled in a computer using baseline data from the National Health Survey for England. The effects of biological groups identified by each calculator depend on the variation in each major model parameters were then applied to each hypothetical individual. The predictive capacities of 3 different risk identification systems based on computer calculation (the Framingham algorithm), or on tabular methods (the Sheffield tables and the General Rule to Enable Atheroma Treatment) were evaluated.

RESULTS

All three models predict that similar numbers would receive treatment with 2.9 and 10% receiving treatment at 30 and 15% 10 year risk thresholds, respectively. However, concordance is limited as 0.3 or 6.8% are positive on all three systems; 1.6 or 9.7% on any two calculators at the 30 and 15% thresholds, respectively. The risk baseline assumptions in each model.

CONCLUSION

Care needs to be taken with applying risk calculators to populations different from which they were derived. Any cardiovascular risk scoring system needs to be thoroughly evaluated against epidemiological data before it is introduced and also needs to be updated in line with changing trends in risk factors.

摘要

目的

评估不同心血管风险计算算法所识别出的预测高风险个体之间的异同。研究设计与方法:利用来自英格兰国家健康调查的基线数据,在计算机上对10000名具有代表性的个体进行建模。每个计算器所识别的生物群体的影响取决于每个主要模型参数的变化,然后将这些变化应用于每个假设个体。评估了基于计算机计算(弗明汉算法)或表格法(谢菲尔德表格和动脉粥样硬化治疗通用规则)的3种不同风险识别系统的预测能力。

结果

所有三种模型预测,接受治疗的人数相似,在30%和15%的10年风险阈值下,分别有2.9%和10%的人接受治疗。然而,一致性有限,在所有三种系统中呈阳性的比例为0.3%或6.8%;在30%和15%的阈值下,在任何两种计算器上呈阳性的比例分别为1.6%或9.7%。每个模型中的风险基线假设。

结论

在将风险计算器应用于与其所源自的人群不同的人群时需谨慎。任何心血管风险评分系统在引入之前都需要根据流行病学数据进行全面评估,并且还需要根据风险因素的变化趋势进行更新。

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