Integration of coronary artery calcium scoring from CT attenuation scans by machine learning improves prediction of adverse cardiovascular events in patients undergoing SPECT/CT myocardial perfusion imaging

Attila Feher, Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, Dana 3, P.O. Box 208017, New Haven, CT, 06520, USA. attila.feher@yale.edu.
Konrad Pieszko, Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Robert Miller, Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Mark Lemley, Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Aakash Shanbhag, Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Cathleen Huang, Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Leonidas Miras, Division of Cardiology, Bridgeport Hospital, Yale University School of Medicine, Bridgeport, CT, USA.
Yi-Hwa Liu, Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, Dana 3, P.O. Box 208017, New Haven, CT, 06520, USA.
Albert J. Sinusas, Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, Dana 3, P.O. Box 208017, New Haven, CT, 06520, USA.
Edward J. Miller, Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, Dana 3, P.O. Box 208017, New Haven, CT, 06520, USA.
Piotr J. Slomka, Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA.

Document Type

Article

Publication Title

Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology

Abstract

BACKGROUND: Machine learning (ML) has been previously applied for prognostication in patients undergoing SPECT myocardial perfusion imaging (MPI). We evaluated whether including attenuation CT coronary artery calcification (CAC) scoring improves ML prediction of major adverse cardiovascular events (MACE) in patients undergoing SPECT/CT MPI. METHODS: From the REFINE SPECT Registry 4770 patients with SPECT/CT performed at a single center were included (age: 64 ± 12 years, 45% female). ML algorithm (XGBoost) inputs were clinical risk factors, stress variables, SPECT imaging parameters, and expert-observer CAC scoring using CT attenuation correction scans performed to obtain CT attenuation maps. The ML model was trained and validated using tenfold hold-out validation. Receiver Operator Characteristics (ROC) curves were analyzed for prediction of MACE. MACE-free survival was evaluated with standard survival analyses. RESULTS: During a median follow-up of 24.1 months, 475 patients (10%) experienced MACE. Higher area under the ROC curve for MACE was observed with ML when CAC scoring was included (CAC-ML score, 0.77, 95% confidence interval [CI] 0.75-0.79) compared to ML without CAC (ML score, 0.75, 95% CI 0.73-0.77, P = .005) and when compared to CAC score alone (0.71, 95% CI 0.68-0.73, P < .001). Among clinical, imaging, and stress parameters, CAC score had highest variable importance for ML. On survival analysis patients with high CAC-ML score (> 0.091) had higher event rate when compared to patients with low CAC-ML score (hazard ratio 5.3, 95% CI 4.3-6.5, P < .001). CONCLUSION: Integration of attenuation CT CAC scoring improves the predictive value of ML risk score for MACE prediction in patients undergoing SPECT MPI.

First Page

590

Last Page

603

DOI

10.1007/s12350-022-03099-x

Publication Date

4-1-2023

Identifier

36195826 (pubmed); 10.1007/s12350-022-03099-x (doi); S1071-3581(23)00162-9 (pii)

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