Serial imaging and outcome prediction

When one considers the many types of cardiovascular events and the many causes of death in patients with suspected or known to have coronary artery disease (Tables 1, 2), one must marvel at how well myocardial perfusion imaging (MPI) with single-photon emission computed tomography (SPECT) and positron emission tomography (PET) have performed in predicting major adverse cardiac events (MACE). Predicting death and its timing is not easy and likely could not be done perfectly solely on the basis of the myocardial perfusion pattern, myocardial blood flow (MBF), left ventricular (LV) ejection fraction (EF), innervation, or even all of these combined. For that reason, many studies have incorporated additional pieces of information such as demographic, socioeconomic, psychosocial, environmental, biochemical, anatomic, and genetic factors to refine the process. There are fewer data on the prediction of the individual endpoints of MACE and even less on the mechanisms of the associations or causations. In some ways, mixing different endpoints into combined outcomes have hindered our ability to focus on the mechanistic aspects. For example, the implications and mechanisms of cardiac death are likely entirely different from those of non-fatal myocardial infarction, coronary revascularization, and quality of life measures. Another observation in outcome data is the paucity of data on serial imaging and the mechanisms involved in worsening or improvement and the relation of such to specific endpoints (Tables 3, 4, 5, 6, 7, 8, 9, 10). In this issue of the Journal, there is one report on MPI serial imaging. Comparison of two sets of images performed at two different time intervals is inherently complicated by the fact that the changes in myocardial perfusion or MBF are not only the result of the specific intervention (for example statin therapy) but are also influenced by biological variability and by the precision and accuracy of the method used. Visual methods might be accurate but not reproducible, and automated methods might be reproducible but not precise. A common practice is to compare patient data to a normal database, but alternative methods are available that compare the two sets of images to each other directly rather than comparing each to a normal database. Such an approach may improve the detection of smaller interval changes (improvement or worsening) since the intra-patient variation is expected to be smaller than the inter-patient variation. Given the fact that we have had more than four decades of experience in nuclear cardiac imaging (more than any other imaging modality except invasive coronary angiography!), that ischemic heart disease is a dynamic process, that the course of the disease could be altered by therapy, and that patient survival has improved over the decades, one would expect many more studies based on serial changes on imaging. It may very well be that the data are there but not reported because the analysis is not easy! It is hard to believe that repeat imaging was not performed in studies that report outcome data of more than 5 years of follow-up even in the current era of appropriate use criteria which have slowed the pace of routine serial imaging. When considering serial images, one needs to consider the initial results (first study), the final results (second study), and the change between the first and the second studies (expressed either as absolute or relative) (Table 9). Consider for example 2 patients: in patient A the defect size (or MPF) decreased from 50% on initial Reprint requests: Ami E. Iskandrian MD, MACC, MASNC, Division of Cardiovascular Disease, Department of Medicine, University of Alabama at Birmingham, 318 LHRB, 1900 University BLVD, Birmingham, AL 35294, USA; aiskand@uab.edu J Nucl Cardiol 2016;23:117–21. 1071-3581/$34.00 Copyright 2015 American Society of Nuclear Cardiology.