Author + information
- Received March 27, 2017
- Revision received July 5, 2017
- Accepted July 5, 2017
- Published online July 2, 2018.
- Julian Betancur, PhDa,
- Yuka Otaki, MDa,
- Manish Motwani, MB, ChB, PhDa,
- Mathews B. Fish, MDb,
- Mark Lemley, CNMTb,
- Damini Dey, PhDa,
- Heidi Gransar, MSa,
- Balaji Tamarappoo, MD, PhDa,
- Guido Germano, PhDa,
- Tali Sharir, MDc,
- Daniel S. Berman, MDa and
- Piotr J. Slomka, PhDa,∗ ()
- aDepartments of Imaging, Medicine, and Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California
- bOregon Heart and Vascular Institute, Sacred Heart Medical Center, Springfield, Oregon
- cDepartment of Nuclear Cardiology, Assuta Medical Centers, Tel Aviv, Israel
- ↵∗Address for correspondence:
Dr. Piotr J. Slomka, Artificial Intelligence in Medicine Program, Cedars-Sinai Medical Center, 8700 Beverly Boulevard, Suite A047N, Los Angeles, California 90048.
Objectives This study evaluated the added predictive value of combining clinical information and myocardial perfusion single-photon emission computed tomography (SPECT) imaging (MPI) data using machine learning (ML) to predict major adverse cardiac events (MACE).
Background Traditionally, prognostication by MPI has relied on visual or quantitative analysis of images without objective consideration of the clinical data. ML permits a large number of variables to be considered in combination and at a level of complexity beyond the human clinical reader.
Methods A total of 2,619 consecutive patients (48% men; 62 ± 13 years of age) who underwent exercise (38%) or pharmacological stress (62%) with high-speed SPECT MPI were monitored for MACE. Twenty-eight clinical variables, 17 stress test variables, and 25 imaging variables (including total perfusion deficit [TPD]) were recorded. Areas under the receiver-operating characteristic curve (AUC) for MACE prediction were compared among: 1) ML with all available data (ML-combined); 2) ML with only imaging data (ML-imaging); 3) 5-point scale visual diagnosis (physician [MD] diagnosis); and 4) automated quantitative imaging analysis (stress TPD and ischemic TPD). ML involved automated variable selection by information gain ranking, model building with a boosted ensemble algorithm, and 10-fold stratified cross validation.
Results During follow-up (3.2 ± 0.6 years), 239 patients (9.1%) had MACE. MACE prediction was significantly higher for ML-combined than ML-imaging (AUC: 0.81 vs. 0.78; p < 0.01). ML-combined also had higher predictive accuracy compared with MD diagnosis, automated stress TPD, and automated ischemic TPD (AUC: 0.81 vs. 0.65 vs. 0.73 vs. 0.71, respectively; p < 0.01 for all). Risk reclassification for ML-combined compared with visual MD diagnosis was 26% (p < 0.001).
Conclusions ML combined with both clinical and imaging data variables was found to have high predictive accuracy for 3-year risk of MACE and was superior to existing visual or automated perfusion assessments. ML could allow integration of clinical and imaging data for personalized MACE risk computations in patients undergoing SPECT MPI.
This research was supported in part by grant R01HL089765 from the National Heart, Lung, and Blood Institute/National Institute of Health (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. Drs. Betancur and Otaki contributed equally to this work.
Drs. Berman, Germano, and Slomka have received royalties from Cedars-Sinai Medical Center. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.
- Received March 27, 2017.
- Revision received July 5, 2017.
- Accepted July 5, 2017.
- 2018 American College of Cardiology Foundation
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