Suppr超能文献

Improving the prediction of coronary heart disease to aid in the management of high cholesterol levels: what a difference a decade makes.

作者信息

Avins A L, Browner W S

机构信息

Veterans Affairs Medical Center, Department of Epidemiology and Biostatistics, University of California, San Francisco 94121, USA.

出版信息

JAMA. 1998 Feb 11;279(6):445-9. doi: 10.1001/jama.279.6.445.

Abstract

CONTEXT

A patient's coronary heart disease (CHD) risk must be correctly classified to successfully apply risk-based guidelines for treatment of hypercholesterolemia.

OBJECTIVE

To determine the classification accuracy of the National Cholesterol Education Program (NCEP) CHD risk-stratification system and compare it with a simple revised system that gives greater weight to age as a CHD risk factor.

DESIGN

Modeling of 10-year CHD risk, using equations from the Framingham Heart Study applied to a cross-sectional survey of the US population.

SUBJECTS

The 3284 subjects aged 20 to 74 years surveyed in the Second National Health and Nutrition Examination Survey (1978-1982) who had fasting lipid levels measured.

MAIN OUTCOME MEASURES

The area under the receiver operating characteristic curve (AUC) for 10-year CHD risk for the NCEP and revised scales.

RESULTS

Among all adults with a low-density lipoprotein cholesterol value of at least 4.1 mmol/L (160 mg/dL), the NCEP system showed fairly good discrimination (AUC=0.90), though there was a substantial decline among men 35 to 74 years old and women 55 to 74 years old (AUC=0.81). By contrast, the revised system showed superior performance in all hypercholesterolemic adults (AUC=0.94-0.97) as well as in the subgroup of men 35 to 74 years old and women 55 to 74 years old (AUC=0.94-0.96).

CONCLUSIONS

Simple modifications of the NCEP treatment criteria result in a substantially improved ability to discriminate between higher and lower CHD risk groups. Unlike the NCEP system, this revised system retains its classification ability in all age groups studied.

摘要

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验