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运动试验的自动化和列线图分析。

Automated and nomographic analysis of exercise tests.

作者信息

Sketch M H, Mohiuddin S M, Nair C K, Mooss A N, Runco V

出版信息

JAMA. 1980 Mar 14;243(10):1052-5.

PMID:7354563
Abstract

A study was conducted to evaluate the validity and usefulness of a commercially available microprocessor for automated exercise ECG analysis and to develop a nomogram for estimating the severity of coronary artery disease. Results of visual analysis, automated analysis, and coronary arteriography were correlated for the 107 patients studied. Automated analysis was shown to be valid and useful. The ST integrals (area of ST depression) recorded after exercise were superior to those recorded during exercise because they manifested higher specificity and predictive value, even though their sensitivity was slightly lower. Using postexercise integrals, it was possible to differentiate mild and severe disease. From multiple-regression analysis of ST integrals, duration of exercise, and the severity of coronary artery disease, a nomogram was derived to estimate severity of coronary artery disease.

摘要

进行了一项研究,以评估一种商用微处理器用于自动运动心电图分析的有效性和实用性,并制定一个用于估计冠状动脉疾病严重程度的列线图。对107例研究对象的视觉分析、自动分析和冠状动脉造影结果进行了相关性分析。结果表明自动分析是有效且有用的。运动后记录的ST积分(ST段压低面积)优于运动期间记录的积分,因为尽管其敏感性略低,但特异性和预测价值更高。使用运动后积分可以区分轻度和重度疾病。通过对ST积分、运动持续时间和冠状动脉疾病严重程度进行多元回归分析,得出了一个用于估计冠状动脉疾病严重程度的列线图。

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