Utility of the Diamond-Forrester Classification in Stratifying Acute Chest Pain in an Academic Chest Pain Center

Diamond-Forrester 分类法在学术型胸痛中心急性胸痛分层中的应用

阅读:1

Abstract

BACKGROUND: Because the Diamond-Forrester (DF) model is predictive of obstructive coronary artery disease (CAD), it is often used to risk stratify acute chest pain patients. We sought to further evaluate the clinical utility of the DF model within a chest pain evaluation center. METHODS: Consecutive patients with chest pain and no known CAD or evidence of active ischemia were asked to participate in a prospective registry. Patients were classified based on cardiovascular risk factors, age, and DF classification. We compared data from the emergency department course, Duke Activity Status Index (DASI) and Seattle Angina Questionnaire (SAQ), hospitalization rates, and results of testing between patients with typical angina and all others. Multivariate logistic regression was also used to assess for predictors of CAD by computed tomography coronary angiography (CTCA) or positive exercise treadmill testing (ETT). RESULTS: Among 209 patients, 163 had atypical/noncardiac and 46 had typical chest pain. The SAQ and DASI scores were lower in the typical chest pain group (indicating more severe impairment), which were not statistically significantly different. There were no significant differences in risk factors or the results of CTCA, ETT, or cardiac catheterization. In the regression analysis, SAQ score, DASI score, and DF classification were not predictive of CAD by CTCA. Worsening angina frequency scores on the SAQ were marginally associated with positive ETT (OR, 1.04; P=0.04). CONCLUSION: In a contemporary low-risk acute chest pain population, typical angina, as defined by the DF classification, was not predictive of CAD or useful for identifying patients with higher symptom burden.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。