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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

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Journal of Nuclear Cardiology Aims and scope

A Correction to this article was published on 04 January 2023

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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.

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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.

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Author notes

  1. Attila Feher and Konrad Pieszko have contributed equally to this work.

Authors and Affiliations

  1. 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 MD, PhD, Yi-Hwa Liu PhD, Albert J. Sinusas MD & Edward J. Miller MD, PhD

  2. Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA

    Attila Feher MD, PhD & Albert J. Sinusas MD

  3. Departments of Medicine (Division of Artificial Intelligence in Medicine), Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, USA

    Konrad Pieszko MD, PhD, Robert Miller MD, Mark Lemley CNMT, Aakash Shanbhag MS, Cathleen Huang BA & Piotr J. Slomka PhD

  4. Department of Cardiac Sciences, University of Calgary, Calgary, AB, Canada

    Robert Miller MD

  5. Division of Cardiology, Bridgeport Hospital, Yale University School of Medicine, Bridgeport, CT, USA

    Leonidas Miras MD

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  1. Attila Feher MD, PhD
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Contributions

AF contributed to statistical analysis and writing of the manuscript. KP contributed to data collection and writing of the manuscript. RM contributed to writing of the manuscript. ML, SA, CH, LM, and YL contributed to data collection and provided scientific direction. AJS and EJM provided scientific direction and interpretation, guided the data analysis, and contributed to writing of the manuscript. PS was responsible for funding acquisition, provided scientific direction and interpretation, guided the data analysis, and contributed to writing of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Attila Feher MD, PhD.

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Disclosures

Dr. Slomka participates in software royalties for QPS software at Cedars-Sinai Medical Center and received research grant support from Siemens Medical Systems. Dr. Miller has received grant support from and is a consultant for GE Healthcare. All other authors have no relevant disclosures.

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Feher, A., Pieszko, K., Miller, R. et al. 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. J. Nucl. Cardiol. 30, 590–603 (2023). https://doi.org/10.1007/s12350-022-03099-x

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