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Consider 'cost efficiency' for echo and chronic CAD

When it comes to cost effectiveness, echocardiography and chronic coronary artery disease (CAD), consider rethinking the terminology. That was one message from a March 25 presentation at the American College of Cardiology's 61st annual scientific session.

"There are no good cost-effectiveness data on echo in chronic CAD, and by that I mean established CAD," said Pamela S. Douglas, MD, of the Duke University Medical Center in Durham, N.C. She noted that previous evaluations looked at the initial diagnosis of a patient with symptoms and efforts to determine whether the symptoms were due to CAD or not. "But once the patient has received a diagnosis of CAD, there are very few studies as to what sort of diagnostic testing needs to be done on those patients, what the value of that testing might be and the cost relative to the value."

As an option, she suggested thinking in terms of cost efficiency, which she defined as achieving a desired goal such as making a diagnosis or excluding CAD at a reduced or minimum cost. "That is an additional way to think about value from imaging and chronic disease," she said.

Douglas looked back at previous meta-analysis research comparing echo and other modalities that was later used for a cost-effectiveness analysis. The findings favored echo for cost effectiveness but she pointed out that there were limitations, especially in contemporary practice. Those included the use of old technology, wide ranges at the time in the estimated disease prevalence, an anatomic gold standard for ischemia testing, vastly different reimbursement rates from today's and analytic shortcomings.

Importantly, she added, the analysis was test-based rather than an episode of care. She discussed preliminary data based on another study comparing nuclear and echo testing that found patient profiles and downstream testing varied by modality. For instance, 6.3 percent of patients in the echo group had additional stress testing versus 1.1 percent in the nuclear group.

"There were differences in downstream use of testing and intervention, depending on the initial test, which in turn depends on the patient population," Douglas said. "It becomes very difficult to model cost effectiveness or cost minimization with this kind of heterogeneity,"

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