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Diagnostic safety of a machine learning-based automatic patient selection algorithm for stress-only myocardial perfusion SPECT

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Journal of Nuclear Cardiology Aims and scope

Abstract

Background

Stress-only myocardial perfusion imaging (MPI) markedly reduces radiation dose, scanning time, and cost. We developed an automated clinical algorithm to safely cancel unnecessary rest imaging with high sensitivity for obstructive coronary artery disease (CAD).

Methods and Results

Patients without known CAD undergoing both MPI and invasive coronary angiography from REFINE SPECT were studied. A machine learning score (MLS) for prediction of obstructive CAD was generated using stress-only MPI and pre-test clinical variables. An MLS threshold with a pre-defined sensitivity of 95% was applied to the automated patient selection algorithm. Obstructive CAD was present in 1309/2079 (63%) patients. MLS had higher area under the receiver operator characteristic curve (AUC) for prediction of CAD than reader diagnosis and TPD (0.84 vs 0.70 vs 0.78, P < .01). An MLS threshold of 0.29 had superior sensitivity than reader diagnosis and TPD for obstructive CAD (95% vs 87% vs 87%, P < .01) and high-risk CAD, defined as stenosis of the left main, proximal left anterior descending, or triple-vessel CAD (sensitivity 96% vs 89% vs 90%, P < .01).

Conclusions

The MLS is highly sensitive for prediction of both obstructive and high-risk CAD from stress-only MPI and can be applied to a stress-first protocol for automatic cancellation of unnecessary rest imaging.

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Abbreviations

CAD:

Coronary artery disease

ICA:

Invasive coronary angiography

LAD:

Left anterior descending

LCx:

Left circumflex artery

LMCA:

Left main coronary artery

MLS:

Machine learning score

MPI:

Myocardial perfusion imaging

SPECT:

Single-photon emission computed tomography

SSS:

Summed stress score

TPD:

Total perfusion deficit

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Disclosures

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. The work was supported in part by the Dr. Miriam and Sheldon Adelson Medical Research Foundation. D.S.B., and P.J.S. participate in software royalties for QPS software at Cedars-Sinai Medical Center. P.J.S. has received research Grant support from Siemens Medical Systems. D.S.B., A.J.E., and E.J.M. have served as consultants for GE Healthcare. M.D.C. has received research Grant support from Spectrum-Dynamics and consulting honoraria from Sanofi and GE Healthcare. T.D.R. has received research Grant support from GE Healthcare and Advanced Accelerator Applications. A.J.E. and his institution have received research support from GE Healthcare, Philips Healthcare, and Toshiba America Medical Systems. E.J.M. has served as a consultant for Bracco Inc, and he and his institution have received Grant support from Bracco Inc. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.

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Author notes

  1. Evann Eisenberg and Robert J. H. Miller have contributed equally as first author.

Authors and Affiliations

  1. Cedars-Sinai Medical Center, 8700 Beverly Boulevard, Ste. Metro 203, Los Angeles, CA, 90048, USA

    Evann Eisenberg MD, Robert J. H. Miller MD, Lien-Hsin Hu MD, Richard Rios PhD, Julian Betancur PhD, Peyman Azadani MD, Donghee Han MD, Joanna X. Liang BA, Yuka Otaki MD, PhD, Balaji K. Tamarappoo MD, PhD, Damini Dey PhD, Daniel S. Berman MD & Piotr J. Slomka PhD

  2. University of Calgary, Calgary, AB, Canada

    Robert J. H. Miller MD

  3. Taipei Veterans General Hospital, Taipei, Taiwan

    Lien-Hsin Hu MD

  4. Assuta Medical Centers, Tel Aviv, Israel

    Tali Sharir MD

  5. Columbia University Irving Medical Center and New York-Presbyterian Hospital, New York, NY, USA

    Andrew J. Einstein MD, PhD & Sabahat Bokhari MD

  6. Sacred Heart Medical Center, Springfield, OR, USA

    Mathews B. Fish MD

  7. University of Ottawa Heart Institute, Ottawa, ON, Canada

    Terrence D. Ruddy MD

  8. University Hospital Zurich, Zurich, Switzerland

    Philipp A. Kaufmann MD

  9. Yale University School of Medicine, New Haven, CT, USA

    Albert J. Sinusas MD & Edward J. Miller MD, PhD

  10. Cardiovascular Imaging Technologies LLC, Kansas City, MO, USA

    Timothy M. Bateman MD

  11. Brigham and Women’s Hospital, Boston, MA, USA

    Sharmila Dorbala MD, MPH & Marcelo Di Carli MD

Authors
  1. Evann Eisenberg MD
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  20. Yuka Otaki MD, PhD
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  24. Piotr J. Slomka PhD
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Corresponding author

Correspondence to Piotr J. Slomka PhD.

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Funding

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. The work was supported in part by the Dr. Miriam and Sheldon Adelson Medical Research Foundation.

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Eisenberg, E., Miller, R.J.H., Hu, LH. et al. Diagnostic safety of a machine learning-based automatic patient selection algorithm for stress-only myocardial perfusion SPECT. J. Nucl. Cardiol. 29, 2295–2307 (2022). https://doi.org/10.1007/s12350-021-02698-4

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  • DOI: https://doi.org/10.1007/s12350-021-02698-4

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