Predictors of high-risk coronary artery disease in subjects with normal SPECT myocardial perfusion imaging

Rine Nakanishi1, Heidi Gransar1, Piotr Slomka1

  • 1Departments of Imaging and Medicine, Cedars-Sinai Heart Institute, Cedars Sinai Medical Center, 8700 Beverly Boulevard, Los Angeles, CA, 90048, USA.

Insights

Normal stress SPECT myocardial perfusion imaging (MPI) can occur in high-risk coronary artery disease (CAD). Predictors include high pretest probability, abnormal summed stress score (SSS), and abnormal transit time (TID) or ejection fraction (EF) change.

Area of Science:

  • Cardiology
  • Nuclear Medicine
  • Diagnostic Imaging

Background:

  • Normal stress SPECT myocardial perfusion imaging (MPI) can be observed in patients with high-risk coronary artery disease (CAD) identified by invasive coronary angiography (ICA).
  • Predictors for high-risk CAD in patients with normal SPECT-MPI have not been previously described.

Purpose of the Study:

  • To identify predictors of high-risk CAD in patients presenting with normal SPECT-MPI.
  • To improve the assessment of high-risk CAD likelihood in patients undergoing stress SPECT-MPI.

Main Methods:

  • Studied 580 patients without known CAD who underwent stress-gated SPECT-MPI before ICA.
  • Defined high-risk CAD based on specific stenosis criteria in major coronary arteries.
  • Utilized tenfold cross-validated receiver operating characteristic (ROC) estimates to identify predictors.

Main Results:

  • Identified high pretest probability of CAD (OR 3.63), summed stress score (SSS) > 0 (OR 7.46), and abnormal transit time-intensity (TID) (OR 2.16) as predictors of high-risk CAD.
  • Found that ejection fraction (EF) change was also predictive (OR 0.93).
  • Observed an increased prevalence of high-risk CAD with a higher number of these predictors.

Conclusions:

  • Clinical, stress, and SPECT-MPI findings are associated with high-risk CAD in patients with normal SPECT-MPI.
  • Incorporating these predictors can enhance the assessment of high-risk CAD likelihood.
Abstract

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