In 1979, Diamond and Forrester introduced the idea of using pretest probability to select an optimum test to diagnose obstructive coronary artery disease (CAD) in stable symptomatic patients.² Since then, analysis of pretest probability has been a standard practice in cardiology. Consequently, the analysis of pretest probability of CAD has served as an effective gatekeeper for noninvasive testing and has been used to define its appropriateness of use in guidelines (Figures 31.1, 31.2, and Table 31.1).³,⁴ For example, noninvasive imaging testing for obstructive CAD is most cost-effective when it is applied to patients with an intermediate likelihood of CAD. Recent technological advances have extended the ability of noninvasive imaging methods to diagnose obstructive CAD reducing the need for invasive diagnostic catheterization. Because of this development, analysis of pretest probability requires appropriate adjustments. Multiple studies in contemporary era have shown that the traditional Diamond and Forrester model overestimates pretest probability of obstructive CAD, which potentially leads to selection of too many low-risk patients for testing. Recent efforts have focused on developing newer risk scores to estimate pretest probability of obstructive CAD suitable for contemporary noninvasive diagnosing tests.

Screening is very important to diagnose CAD and assess the prognosis to provide an appropriate treatment. Within the context of screening tests, it is important to avoid misconceptions about sensitivity, specificity, and predictive values.⁴ The sensitivity of a screening test can be described as the ability of a screening test to detect a true positive. A definition of sensitivity would be a screening test’s probability of correctly identifying, solely from among people who are known to have a condition. A definition of positive predictive value (PPV) would be a screening test’s probability, when returning a positive result, from among people who might or might not have a condition. On the other hand, the specificity of a test can be described as the ability of a screening test to detect a true negative. A definition of specificity would be a screening test’s probability of correctly identifying, solely from among people who are known not to have a condition. A clear definition of negative predictive value (NPV) would be a screening test’s probability, when returning a negative result, from among people who might or might not have a condition. By summarizing these definitions, sensitivity and specificity indicate the concordance of a test with respect to a chosen referent, PPV and NPV, respectively, that indicate the likelihood that a test can successfully identify whether people do or do not have a target condition, based on their test results.

Despite the above reservations concerning sensitivity and specificity in a screening situation, sensitivity and specificity can be useful in two circumstances but only if they are extremely high. A highly sensitive screening test is unlikely to produce false-negative outcomes, people who test negative on a screening test with high sensitivity are very unlikely to have the target condition. This statement can also be applied to a highly specific test. In fact, for many screening tests, unfortunately, either sensitivity or specificity is low despite the other being high, or neither sensitivity nor specificity is high.⁵ As a consequence, predictive values are more relevant than are sensitivity and specificity when people are being screened. Of note, predictive values also need caution to be adopted because PPVs and NPVs are directly related to the prevalence of the disease in the population. Assuming all other factors remain constant,

the PPV will increase with increasing prevalence; and NPV decreases with increase in prevalence.⁶

Overuse of low-value clinical services has received greater attention in recent years in response to a report of waste in US health care that estimated over 20% of total expenditure are spent on services that do not improve patient care. Ordering an appropriate diagnostic test is fundamental in providing high-quality cardiovascular care, efficiently guiding to an appropriate therapy, and evaluating the treatment effect. Cardiovascular imaging (CVI) must be used only when the test contributes in improving clinical outcomes and values. Since 2005, the American College of Cardiology (ACC) has been publishing multiple AUC documents and their updates covering CVI tests in common clinical scenarios in partnership with specialty and subspecialty societies. However, “rarely appropriate” imaging tests are still ordered in significant numbers. According to a meta-analysis, percentage of appropriate indications for transthoracic echocardiography (TTE) is 85%, transesophageal echocardiography (TEE) is 95%, stress echocardiography is 52%, CT angiography (CTA) is 55%, and single-photon emission CT is 68%.⁷ Educational interventions have been proposed and shown to be effective in reducing inappropriate ordering of tests.⁸ National and international educational systems may need to be organized to promote global level of consistent appropriate utilization. To make things complicated, a preferred imaging test for a given clinical scenario varies with each practice setting. Important considerations in modality choice are diagnostic performance, availability of the technology, and physician expertise. Safety considerations including radiation dose and cost should also be considered in relationship to these benefits when determining net value.