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Correlation of machine learning computed tomography-based fractional flow reserve with instantaneous wave free ratio to detect hemodynamically significant coronary stenosis

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Abstract

Background

Fractional flow reserve based on coronary CT angiography (CT-FFR) is gaining importance for non-invasive hemodynamic assessment of coronary artery disease (CAD). We evaluated the on-site CT-FFR with a machine learning algorithm (CT-FFRML) for the detection of hemodynamically significant coronary artery stenosis in comparison to the invasive reference standard of instantaneous wave free ratio (iFR®).

Methods

This study evaluated patients with CAD who had a clinically indicated coronary computed tomography angiography (cCTA) and underwent invasive coronary angiography (ICA) with iFR®-measurements. Standard cCTA studies were acquired with third-generation dual-source computed tomography and analyzed with on-site prototype CT-FFRML software.

Results

We enrolled 40 patients (73% males, mean age 67 ± 12 years) who had iFR®-measurement and CT-FFRML calculation. The mean calculation time of CT-FFRML values was 11 ± 2 min. The CT-FFRML algorithm showed, on per-patient and per-lesion level, respectively, a sensitivity of 92% (95% CI 64–99%) and 87% (95% CI 59–98%), a specificity of 96% (95% CI 81–99%) and 95% (95% CI 84–99%), a positive predictive value of 92% (95% CI 64–99%), and 87% (95% CI 59–98%), and a negative predictive value of 96% (95% CI 81–99%) and 95% (95% CI 84–99%). The area under the receiver operating characteristic curve for CT-FFRML on per-lesion level was 0.97 (95% CI 0.91–1.00). Per lesion, the Pearson’s correlation between the CT-FFRML and iFR® showed a strong correlation of r = 0.82 (p < 0.0001; 95% CI 0.715–0.920).

Conclusion

On-site CT-FFRML correlated well with the invasive reference standard of iFR® and allowed for the non-invasive detection of hemodynamically significant coronary stenosis.

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Abbreviations

CAD:

Coronary artery disease

cCTA:

Coronary computed tomography angiography

CT:

Computed tomography

CT-FFR:

Fractional flow reserve derived from coronary computed tomography angiography

CT-FFRML :

Fractional flow reserve derived from coronary computed tomography angiography based on machine learning algorithm

ESC:

European Society of Cardiology

FFR:

Fractional flow reserve

ICA:

Invasive coronary angiography

iFR® :

Instantaneous wave free ratio

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Acknowledgement

Supported by Siemens Healthineers for providing CT-FFRML software for research purposes, which is currently not commercially available. Furthermore, the authors would like to thank Philips Volcano Corporation (Koninklijke Philips N.V. Amsterdam, The Netherland) for their support.

Funding

UJS receives institutional research support and/or honoraria for consulting and speaking from Astellas, Bayer, Bracco, Elucid BioImaging, GE, Guerbet, HeartFlow, and Siemens. SB receives research support from Philips Volcano. All other authors declare that they have no financial disclosures. The presented CT-FFRML software is provided by Siemens and is currently not commercially available.

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Authors and Affiliations

  1. First Department of Medicine-Cardiology, University Medical Centre Mannheim, Mannheim, Germany

    Stefan Baumann, Markus Hirt, Marlon Rutsch, Tobias Becher, Theano Papavassiliu, Ibrahim Akin, Martin Borggrefe & Dirk Lossnitzer

  2. DZHK (German Centre for Cardiovascular Research), Partner Site Heidelberg/Mannheim, Mannheim, Germany

    Stefan Baumann, Markus Hirt, Marlon Rutsch, Tobias Becher, Theano Papavassiliu, Ibrahim Akin, Martin Borggrefe & Dirk Lossnitzer

  3. Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA

    U. Joseph Schoepf & Joseph W. Golden

  4. Department of Internal Medicine, St. Johannes-Hospital, Dortmund, Germany

    Christian Tesche

  5. Department of Cardiology, Kerckhoff Heart Center, Bad Nauheim, Germany

    Matthias Renker

  6. The Radiology Center, Sinsheim-Eberbach-Erbach-Walldorf-Heidelberg, Heidelberg, Germany

    Sebastian J. Buss

  7. Laboratory of Molecular Metabolism, The Rockefeller University, New York, NYC, USA

    Tobias Becher

  8. Community Clinic Mittelrhein, Kemperhof II, The Cardiology Clinic, Koblenz, Germany

    Waldemar Bojara

  9. Medical Faculty Mannheim, Department of Medical Statistics and Biomathematics, University Medical Center Mannheim, Heidelberg University, Mannheim, Germany

    Christel Weiss

  10. University Medical Center Mannheim, Faculty of Medicine Mannheim, Institute of Clinical Radiology and Nuclear Medicine, Heidelberg University, Mannheim, Germany

    Stefan O. Schoenberg, Holger Haubenreisser & Daniel Overhoff

  11. First Department of Medicine, Faculty of Medicine Mannheim, University Medical Centre Mannheim (UMM), University of Heidelberg, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany

    Stefan Baumann

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  1. Stefan Baumann
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Correspondence to Stefan Baumann.

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Baumann, S., Hirt, M., Schoepf, U.J. et al. Correlation of machine learning computed tomography-based fractional flow reserve with instantaneous wave free ratio to detect hemodynamically significant coronary stenosis. Clin Res Cardiol 109, 735–745 (2020). https://doi.org/10.1007/s00392-019-01562-3

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  • DOI: https://doi.org/10.1007/s00392-019-01562-3

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