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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

  • Stefan BaumannEmail author
  • Markus Hirt
  • U. Joseph Schoepf
  • Marlon Rutsch
  • Christian Tesche
  • Matthias Renker
  • Joseph W. Golden
  • Sebastian J. Buss
  • Tobias Becher
  • Waldemar Bojara
  • Christel Weiss
  • Theano Papavassiliu
  • Ibrahim Akin
  • Martin Borggrefe
  • Stefan O. Schoenberg
  • Holger Haubenreisser
  • Daniel Overhoff
  • Dirk Lossnitzer
Original Paper

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.

Keywords

Coronary artery disease Coronary CT angiography Fractional flow reserve derived from coronary computed tomography angiography Instantaneous wave-free ratio Invasive coronary angiography Myocardial ischemia 

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

Notes

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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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Stefan Baumann
    • 1
    • 2
    • 11
    Email author
  • Markus Hirt
    • 1
    • 2
  • U. Joseph Schoepf
    • 3
  • Marlon Rutsch
    • 1
    • 2
  • Christian Tesche
    • 4
  • Matthias Renker
    • 5
  • Joseph W. Golden
    • 3
  • Sebastian J. Buss
    • 6
  • Tobias Becher
    • 1
    • 2
    • 7
  • Waldemar Bojara
    • 8
  • Christel Weiss
    • 9
  • Theano Papavassiliu
    • 1
    • 2
  • Ibrahim Akin
    • 1
    • 2
  • Martin Borggrefe
    • 1
    • 2
  • Stefan O. Schoenberg
    • 10
  • Holger Haubenreisser
    • 10
  • Daniel Overhoff
    • 10
  • Dirk Lossnitzer
    • 1
    • 2
  1. 1.First Department of Medicine-CardiologyUniversity Medical Centre MannheimMannheimGermany
  2. 2.DZHK (German Centre for Cardiovascular Research), Partner Site Heidelberg/MannheimMannheimGermany
  3. 3.Division of Cardiovascular Imaging, Department of Radiology and Radiological ScienceMedical University of South CarolinaCharlestonUSA
  4. 4.Department of Internal MedicineSt. Johannes-HospitalDortmundGermany
  5. 5.Department of CardiologyKerckhoff Heart CenterBad NauheimGermany
  6. 6.The Radiology CenterHeidelbergGermany
  7. 7.Laboratory of Molecular MetabolismThe Rockefeller UniversityNew YorkUSA
  8. 8.Community Clinic Mittelrhein, Kemperhof IIThe Cardiology ClinicKoblenzGermany
  9. 9.Medical Faculty Mannheim, Department of Medical Statistics and Biomathematics, University Medical Center MannheimHeidelberg UniversityMannheimGermany
  10. 10.University Medical Center Mannheim, Faculty of Medicine Mannheim, Institute of Clinical Radiology and Nuclear MedicineHeidelberg UniversityMannheimGermany
  11. 11.First Department of Medicine, Faculty of Medicine Mannheim, University Medical Centre Mannheim (UMM)University of HeidelbergMannheimGermany

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