Phase analysis single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) detects dyssynchrony in myocardial scar and increases specificity of MPI

John P. Bois, Chris Scott, Panithaya Chareonthaitawee, Raymond J. Gibbons, Martin Rodriguez-Porcel

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

Background: Myocardial perfusion imaging (MPI) with single-photon emission computed tomography (SPECT) is commonly used to assess patients with cardiovascular disease. However, in certain scenarios, it may have limited specificity in the identification of hemodynamically significant coronary artery disease (e.g., false positive), potentially resulting in additional unnecessary testing and treatment. Phase analysis (PA) is an emerging, highly reproducible quantitative technology that can differentiate normal myocardial activation (synchrony) from myocardial scar (dyssynchrony). The objective of this study is to determine if PA can improve the specificity SPECT MPI. Methods: An initial cohort of 340 patients (derivation cohort), referred for SPECT-MPI, was prospectively enrolled. Resting MPI studies were assessed for resting perfusion defects (scar). These were utilized as the reference standard for scar. Subsequently, we collected a second independent validation cohort of 138 patients and tested the potential of PA to reclassify patients for the diagnosis of “scar” or “no scar.” Patients were assigned to three categories depending upon their pre-test probability of scar based on multiple clinical and imaging parameters: ≤ 10% (no scar), 11–74% (indeterminate), and ≥ 75% (scar). The ability of PA variables to reclassify patients with scar to a higher group and those without scar to a lower group was then determined using the net reclassification index (NRI). Results: Entropy (≥ 59%) was independently associated with scar in both patient cohorts with an odds ratio greater than five. Furthermore, when added to multiple clinical/imaging variables, the use of entropy significantly improved the area under the curve for assessment of scar (0.67 vs. 0.59, p = 0.04). The use of entropy correctly reclassified 24% of patients without scar, by clinical model, to a lower risk category (as determined by pre-test probability) with an overall NRI of 18% in this validation cohort. Discussion: The use of PA entropy can improve the specificity of SPECT MPI and may serve as a useful adjunctive tool to the interpreting physician. The current study determined the optimal PA parameters to detect scar (derivation cohort) and applied these parameters to a second, independent, patient group and noted that entropy (≥ 59%) was independently associated with scar in both patient cohorts. Therefore, PA, which requires no additional imaging time or radiation, enhances the diagnostic capabilities of SPECT MPI. Conclusion: The use of PA entropy significantly improved the specificity of SPECT MPI and could influence the labeling of a patient as having or not having myocardial scar and thereby may influence not only diagnostic reporting but also potentially prognostic determination and therapeutic decision-making.

Original languageEnglish (US)
Article number11
JournalEJNMMI Research
Volume9
DOIs
StatePublished - Jan 1 2019

Keywords

  • Coronary artery disease
  • Diagnostic testing
  • Nuclear cardiology and PET

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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