Rationale and design of the REgistry of Fast Myocardial Perfusion Imaging with NExt generation SPECT (REFINE SPECT)

Abstract

Background

We aim to establish a multicenter registry collecting clinical, imaging, and follow-up data for patients who undergo myocardial perfusion imaging (MPI) with the latest generation SPECT scanners.

Methods

REFINE SPECT (REgistry of Fast Myocardial Perfusion Imaging with NExt generation SPECT) uses a collaborative design with multicenter contribution of clinical data and images into a comprehensive clinical-imaging database. All images are processed by quantitative software. Over 290 individual imaging variables are automatically extracted from each image dataset and merged with clinical variables. In the prognostic cohort, patient follow-up is performed for major adverse cardiac events. In the diagnostic cohort (patients with correlating invasive angiography), angiography and revascularization results within 6 months are obtained.

Results

To date, collected prognostic data include scans from 20,418 patients in 5 centers (57% male, 64.0 ± 12.1 years) who underwent exercise (48%) or pharmacologic stress (52%). Diagnostic data include 2079 patients in 9 centers (67% male, 64.7 ± 11.2 years) who underwent exercise (39%) or pharmacologic stress (61%).

Conclusion

The REFINE SPECT registry will provide a resource for collaborative projects related to the latest generation SPECT-MPI. It will aid in the development of new artificial intelligence tools for automated diagnosis and prediction of prognostic outcomes.

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Figure 1

Abbreviations

BMI:

Body mass index

CABG:

Coronary artery bypass grafting

CAD:

Coronary artery disease

CZT:

Cadmium zinc telluride

EF:

Ejection fraction

ICA:

Invasive coronary angiography

MACE:

Major adverse cardiac events

MI:

Myocardial infarction

MPI:

Myocardial perfusion imaging

PCI:

Percutaneous coronary intervention

SPECT:

Single-photon emission computed tomography

TPD:

Total perfusion deficit

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Acknowledgments

This research was supported in part by Grant R01HL089765 from the National Heart, Lung, and Blood Institute/National Institutes of Health (NHLBI/NIH) (PI: Piotr Slomka). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Disclosures

Drs. Germano, Berman, and Slomka participate in software royalties for QPS software at Cedars-Sinai Medical Center. Dr. Slomka has received research grant support from Siemens Medical Systems. Drs. Berman, Dorbala, Einstein, and Miller have served as consultants for GE Healthcare. Dr. Dorbala has served as a consultant to Bracco Diagnostics; her institution has received grant support from Astellas. Dr. Di Carli has received research grant support from Spectrum Dynamics and consulting honoraria from Sanofi and GE Healthcare. Dr. Ruddy has received research grant support from GE Healthcare and Advanced Accelerator Applications. Dr. Einstein and his institution has received research support from GE Healthcare, Philips Healthcare and Toshiba America Medical Systems. Dr. Miller has served as a consultant for Bracco Inc; and he and his institution has received grant support from Bracco Inc. Dr. Berman’s institution has received grant support from HeartFlow. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.

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Correspondence to Piotr J. Slomka PhD.

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Slomka, P.J., Betancur, J., Liang, J.X. et al. Rationale and design of the REgistry of Fast Myocardial Perfusion Imaging with NExt generation SPECT (REFINE SPECT). J. Nucl. Cardiol. 27, 1010–1021 (2020). https://doi.org/10.1007/s12350-018-1326-4

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Keywords

  • SPECT
  • High-efficiency SPECT
  • Myocardial perfusion imaging
  • Coronary artery disease
  • Machine learning
  • Artificial intelligence
  • Quantitative analysis