Published in

Elsevier, International Journal of Cardiology, 1(22), p. 43-50

DOI: 10.1016/0167-5273(89)90134-4

Links

Tools

Export citation

Search in Google Scholar

Long-term survival and risk stratification in patients with angina at rest undergoing medical treatment.

This paper was not found in any repository, but could be made available legally by the author.
This paper was not found in any repository, but could be made available legally by the author.

Full text: Unavailable

Green circle
Preprint: archiving allowed
Orange circle
Postprint: archiving restricted
Red circle
Published version: archiving forbidden
Data provided by SHERPA/RoMEO

Abstract

In order to determine those factors which influence long-term prognosis in patients with angina at rest associated with transient ST-segment changes, 217 patients undergoing medical treatment were followed for a mean of 39 months. All patients underwent coronary arteriography. Univariate analysis identified 12 variables significantly related to prognosis. These were disease of the left main coronary artery; the number of diseased vessels; left ventricular end-diastolic pressure; ejection fraction; baseline electrocardiogram; presence of prior myocardial infarction; ST-segment depression and ventricular arrhythmias during pain; disease of the proximal anterior descending coronary artery; crescendo angina; hypertension; and age. Use of the Cox regression model for survival analysis revealed only 3 variables which were independent predictors of prognosis. They were disease of the left main coronary artery; the number of diseased vessels and left ventricular end-diastolic pressure. The model allowed stratification of patients into 3 groups. Survival at 3 years was 98% in the low risk group; 82% in the intermediate risk group; and 58% in the high risk group. These data indicate that disease of the left main coronary artery, the number of diseased vessels and left ventricular end-diastolic pressure are the independent predictors of prognosis in angina at rest. These variables may allow stratification of patients into groups having different long-term survivals.