Multicenter Evaluation of Stress-first Myocardial Perfusion Image Triage by Nuclear Technologists and Automated Quantification

Background: A stress-first myocardial perfusion imaging (MPI) protocol saves time, is cost effective, and decreases radiation exposure. A limitation of this protocol is the requirement for physician review of the stress images to determine the need for rest images. This hurdle could be eliminated if an experienced technologist and/or automated computer quantification could make this determination.

Methods: Images from consecutive patients who were undergoing a stress-first MPI with attenuation correction at two tertiary care medical centers were prospectively reviewed independently by a technologist and cardiologist blinded to clinical and stress test data. Their decision on the need for rest imaging along with automated computer quantification of perfusion results was compared with the clinical reference standard of an assessment of perfusion images by a board-certified nuclear cardiologist that included clinical and stress test data.

Results: A total of 250 patients (mean age 61 years and 55% female) who underwent a stress-first MPI were studied. According to the clinical reference standard, 42 (16.8%) and 208 (83.2%) stress-first images were interpreted as "needing" and "not needing" rest images, respectively. The technologists correctly classified 229 (91.6%) stress-first images as either "needing" (n = 28) or "not needing" (n = 201) rest images. Their sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 66.7%, 96.6%, 80.0%, and 93.5%, respectively. An automated stress TPD score ≥1.2 was associated with optimal sensitivity and specificity and correctly classified 179 (71.6%) stress-first images as either "needing" (n = 31) or "not needing" (n = 148) rest images. Its sensitivity, specificity, PPV, and NPV were 73.8%, 71.2%, 34.1%, and 93.1%, respectively. In a model whereby the computer or technologist could correct for the other's incorrect classification, 242 (96.8%) stress-first images were correctly classified. The composite sensitivity, specificity, PPV, and NPV were 83.3%, 99.5%, 97.2%, and 96.7%, respectively.

Conclusion: Technologists and automated quantification software had a high degree of agreement with the clinical reference standard for determining the need for rest images in a stress-first imaging protocol. Utilizing an experienced technologist and automated systems to screen stress-first images could expand the use of stress-first MPI to sites where the cardiologist is not immediately available for interpretation.