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Predicting Severe Angiographic Coronary Artery Disease Using Computerization of Clinical and Exercise Test Data

Dat Do; Rachel Marcus; Victor Froelicher; Andras Janosi; Jeff West; J. Edwin Atwood; Jonathan Myers; Robert Chilton; Jeff Froning
Author and Funding Information

Affiliations: From the University of Texas in San Antonio, Vista, CA,  From the Cardiology Divisions at the Veterans Affairs Palo Alto Health Care System, Stanford University, Palo Alto, CA,  From the St. Janos's Municipal Hospital, Budapest, Hungary,  From the Sunnyside Biomedical, Vista, CA

Victor Froelicher, MD, Cardiology Division (111C), VA Palo Alto Health Care System, 3801 Miranda Ave, Palo Alto, CA 94304; e-mail: vicmd@aol.com


1998 by the American College of Chest Physicians


Chest. 1998;114(5):1437-1445. doi:10.1378/chest.114.5.1437
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Abstract

Currently the standard exercise test is shifting from being a tool for the cardiologist to utilization by the nonspecialist. This change could be facilitated by computerization similar to the interpretation programs available for the resting ECG. Therefore, we sought to determine if computerization of both exercise ECG measurements and prediction equations can substitute for visual analysis performed by cardiologists to predict which patients have severe angiographic coronary artery disease. We performed a retrospective analysis of consecutive patients referred for evaluation of possible or known coronary artery disease who underwent both exercise testing with digital recording of their exercise ECGs and coronary angiography at two university-affiliated Veteran's Affairs medical centers and a Hungarian hospital. There were 2,385 consecutive male patients with complete data who had exercise tests between 1987 and 1997. Measurements included clinical and exercise test data, and visual interpretation of the ECG paper tracings and >100 computed measurements from the digitized ECG recordings and compilation of angiographic data from clinical reports. The computer measurements had similar diagnostic power compared with visual interpretation. Computerized ECG measurements from maximal exercise or recovery were equivalent or superior to all other measurements. Prediction equations applied by computer were only able to correctly classify two or three more patients out of 100 tested than ECG measurements alone. β-Blockers had no effect on test characteristics while ST depression on the resting ECG decreased specificity. By setting probability limits using the scores from the equations, the population was divided into high-, intermediate-, and low-probability groups. A strategy using further testing in the intermediate group resulted in 86% sensitivity and 85% specificity for identifying patients with severe coronary disease. We conclude that computerized exercise ST measurements are comparable to visual ST measurements by a cardiologist and computerized scores only minimally improved the discriminatory power of the test. However, using these scores in a stratification algorithm allows the nonspecialist physician to improve the discriminatory characteristics of the standard exercise test even when resting ST depression is present. Computerization permitted accurate identification of patients with severe coronary disease who require referral.


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