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心血管风险预测指标增量值传统评估方法的局限性。

Limitations in the conventional assessment of the incremental value of predictors of cardiovascular risk.

作者信息

Sniderman Allan D, Pencina Michael, Thanassoulis George

机构信息

aMike Rosenbloom Laboratory for Cardiovascular Research, McGill University Health Centre, Montreal, Quebec, Canada bDepartment of Biostatistics and Bioinformatics, Duke University, DCRI, Durham, North Carolina, USA cDepartment of Medicine, Royal Victoria Hospital, McGill University Health Centre, Montreal, Quebec, Canada.

出版信息

Curr Opin Lipidol. 2015 Jun;26(3):210-4. doi: 10.1097/MOL.0000000000000181.

Abstract

PURPOSE OF REVIEW

Whether a factor significantly increases the area under the curve (AUC) of a receiver operating characteristic analysis has become the standard test of its utility. Thus, in many studies, apolipoprotein B and LDL particle number have not increased the AUC significantly beyond that produced by the conventional markers, and guideline groups have concluded on this basis that they should not be added to routine clinical practice. This article demonstrates this conclusion to be invalid.

RECENT FINDINGS

In conventional analyses, no distinctions have been drawn as to whether a novel predictor is causal or whether it is highly correlated with other markers already included in the risk algorithm. However, correlation among the markers will profoundly affect the incremental effect of a factor on the AUC. This distinction is particularly critical for factors that have been shown to play a causal role in the production of clinical event. Accordingly, the AUC approach is valid to determine the total discriminatory ability of a set of variables but is not appropriate to allocate attributable risk among the members of the set.

SUMMARY

For correlated predictors that describe different aspects of the same variable such as non-HDL-C and apoB or LDL-C and LDL particle number, discordance analysis offers a simple valid alternative to capture and compare the independent information contained by each predictor.

摘要

综述目的

一个因素是否能显著增加受试者工作特征分析的曲线下面积(AUC)已成为衡量其效用的标准测试。因此,在许多研究中,载脂蛋白B和低密度脂蛋白颗粒数量并未使AUC显著高于传统标志物所产生的AUC,指南制定小组据此得出结论,认为它们不应被纳入常规临床实践。本文证明这一结论是无效的。

最新发现

在传统分析中,对于新的预测指标是因果性的还是与风险算法中已包含的其他标志物高度相关,并未加以区分。然而,标志物之间的相关性将深刻影响一个因素对AUC的增量效应。这种区分对于已被证明在临床事件发生中起因果作用的因素尤为关键。因此,AUC方法对于确定一组变量的总体鉴别能力是有效的,但不适用于在该组变量成员之间分配归因风险。

总结

对于描述同一变量不同方面的相关预测指标,如非高密度脂蛋白胆固醇和载脂蛋白B或低密度脂蛋白胆固醇和低密度脂蛋白颗粒数量,不一致性分析提供了一种简单有效的替代方法,以获取和比较每个预测指标所包含的独立信息。

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