The potential value of discordant studies
Many aspects of the clinical application of gated-single photon emission computed tomography (SPECT) have been well established by multiple trials and studies. However, its utility in the elderly (i.e., the Medicare population in the USA) remains unclear. This is an important population due to its rapid growth, coupled with the increasing prevalence of coronary artery disease with age. A paper in this issue, Predictive value of exercise myocardial perfusion imaging in the Medicare population: the impact of the ability to exercise, indicates that while gated-SPECT clearly directs the performance of interventions at the level of the coronary arteries in the elderly, outcomes are worse for those receiving an intervention vs. those receiving medical therapy. While some literature supports this observation, there are also well documented studies that indicate that the opposite is the case. As consumers of discordant studies, we find ourselves in the unenviable position of having to pull at the threads of evidence and follow them through in an attempt to reconcile the conflicting literature. This is reminiscent of the mythical Gregorian knot, a knot that was impossible to unravel by conventional means. However, it was “solved” by cutting it with a sword. In our case, the sword that we have is the removal of bias. It has been said that there are no unbiased studies, since we only measure what we believe and we tend to believe what we measure. This is further compounded in clinical practice since the Hippocratic Oath requires that the physician first does no harm. Therefore it follows that whatever action is done is at least not detrimental to the patient. These are powerful belief systems that on the one hand allow us to rapidly discard “irrelevant” information and quickly get to the important point, but on the other hand they may inhibit us from seeing what is truly of value. Discordant and negative studies are important disruptors along the path to easy data assimilation, and force us to seek out sources of bias which otherwise may go unnoted. In the case of the above paper we might look past the perfusion data to the more important cardiac functional data which may contribute to changing the focus of diagnosis and treatment strategies, thus slicing through a little more of the knot.