Key result
Standard exercise ECG evaluations for CAD suffer from four major historical methodological mistakes.
Why the study?
What are the common methodological mistakes made when evaluating the diagnostic characteristics of the standard exercise ECG test for coronary artery disease?
What are the common methodological mistakes made when evaluating the diagnostic characteristics of the standard exercise ECG test for coronary artery disease?
This review highlights critical methodological flaws in historical studies of the exercise ECG test to improve the design of future diagnostic test evaluations for CAD.
Past evaluations of exercise ECG for CAD are methodologically flawed; leaves open its true diagnostic accuracy and requires better-designed studies.
Motivated by recent systematic reviews of diagnostic tests for coronary artery disease (CAD) and a renewed interest in applying tests for screening healthy individuals, we felt it timely to review the lessons learned from past experience. Since the standard exercise test has been studied for some time, it provides a wealth of experience in this regard. Four major mistakes have been made when evaluating the diagnostic characteristics of the exercise test: (1) choosing subjects for test evaluation who represent a limited challenge to the diagnostic performance of the test; (2) not limiting the amount of workup bias in identifying patients for test evaluation; (3) utilizing soft end points instead of hard end points; and (4) using surrogates instead of an appropriate “gold standard.” We will step through each of these errors and provide illustrations for each. In a closing section, we will compare most of the diagnostic techniques that are available for CAD to the exercise test.
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Froelicher et al. (1999) conducted a review in Coronary artery disease (CAD). Standard exercise ECG test vs. Other diagnostic techniques was evaluated. The standard exercise ECG test for coronary artery disease has historically been evaluated with four major methodological mistakes, including workup bias, soft endpoints, and surrogate gold standards.
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