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Impact of machine-learning-based coronary computed tomography angiography–derived fractional flow reserve on decision-making in patients with severe aortic stenosis undergoing transcatheter aortic valve replacement

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Abstract

Objectives

To evaluate feasibility and diagnostic performance of coronary CT angiography (CCTA)–derived fractional flow reserve (CT-FFR) for detection of significant coronary artery disease (CAD) and decision-making in patients with severe aortic stenosis (AS) undergoing transcatheter aortic valve replacement (TAVR) to potentially avoid additional pre-TAVR invasive coronary angiography (ICA).

Methods

Consecutive patients with severe AS (n = 95, 78.6 ± 8.8 years, 53% female) undergoing pre-procedural TAVR-CT followed by ICA with quantitative coronary angiography were retrospectively analyzed. CCTA datasets were evaluated using CAD Reporting and Data System (CAD-RADS) classification. CT-FFR measurements were computed using an on-site machine-learning algorithm. A combined algorithm was developed for decision-making to determine if ICA is needed based on pre-TAVR CCTA: [1] all patients with CAD-RADS ≥ 4 are referred for ICA; [2] patients with CAD-RADS 2 and 3 are evaluated utilizing CT-FFR and sent to ICA if CT-FFR ≤ 0.80; [3] patients with CAD-RADS < 2 or CAD-RADS 2-3 and normal CT-FFR are not referred for ICA.

Results

Twelve patients (13%) had significant CAD (≥ 70% stenosis) on ICA and were treated with PCI. Twenty-eight patients (30%) showed CT-FFR ≤ 0.80 and 24 (86%) of those were reported to have a maximum stenosis ≥ 50% during ICA. Using the proposed algorithm, significant CAD could be identified with a sensitivity, specificity, and positive and negative predictive value of 100%, 78%, 40%, and 100%, respectively, potentially decreasing the number of necessary ICAs by 65 (68%).

Conclusion

Combination of CT-FFR and CAD-RADS is able to identify significant CAD pre-TAVR and bears potential to significantly reduce the number of needed ICAs.

Key Points

• Coronary CT angiography–derived fractional flow reserve (CT-FFR) using machine learning together with the CAD Reporting and Data System (CAD-RADS) classification safely identifies significant coronary artery disease based on quantitative coronary angiography in patients prior to transcatheter aortic valve replacement.

• The combination of CT-FFR and CAD-RADS enables decision-making and bears the potential to significantly reduce the number of needed invasive coronary angiographies.

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Abbreviations

AUC:

Area under the curve

CAD:

Coronary artery disease

CAD-RADS:

Coronary Artery Disease Reporting and Data System

CCTA:

Coronary CT angiography

CFD:

Computational fluid dynamics

CT-FFR:

Fractional flow reserve derived from coronary CT angiography

ICA:

Invasive coronary angiography

IQR:

Interquartile range

LAD:

Left anterior descending artery

LCX:

Left circumflex artery

NPV:

Negative predictive value

PPV:

Positive predictive value

QCA:

Quantitative coronary angiography

RCA:

Right coronary artery

ROC:

Receiver-operating characteristics

TAVR:

Transcatheter aortic valve replacement

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Funding

No funding was received.

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Author notes

  1. Christian Tesche and Josua A. Decker contributed equally to this work.

Authors and Affiliations

  1. Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, 5 Courtenay Drive, Charleston, SC, 29425-2260, USA

    Verena Brandt, U. Joseph Schoepf, Gilberto J. Aquino, Akos Varga-Szemes, Tilman Emrich, Richard R. Bayer II, Christian Tesche & Josua A. Decker

  2. Department of Cardiology and Angiology, Robert-Bosch-Hospital, Stuttgart, Germany

    Verena Brandt & Raffi Bekeredjian

  3. Department of Diagnostic and Interventional Radiology, University Medical Center Mainz, Mainz, Germany

    Tilman Emrich

  4. German Center for Cardiovascular Research (DZHK), Partner Site Rhine-Main, Mainz, Germany

    Tilman Emrich

  5. Department of Diagnostic and Interventional Radiology, University Hospital Augsburg, Augsburg, Germany

    Florian Schwarz, Thomas J. Kroencke & Josua A. Decker

  6. Department of Cardiology, Clinic Augustinum Munich, Munich, Germany

    Christian Tesche

  7. Department of Cardiology, Munich University Clinic, Ludwig-Maximilians-University, Munich, Germany

    Christian Tesche

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  1. Verena Brandt
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Corresponding author

Correspondence to U. Joseph Schoepf.

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Guarantor

The scientific guarantor of this publication is Prof. U. Joseph Schoepf.

Conflict of interest

The authors of this manuscript declare relationships with the following companies: Dr. Schoepf receives institutional research support and / or honoraria for speaking and consulting from Bayer, Bracco, Elucid BioImaging, Guerbet, HeartFlow Inc., Keya Medical, and Siemens Healthineers. Dr. Varga-Szemes receives institutional research and travel support from Siemens Healthineers and is a consultant for Bayer and Elucid Bioimaging. Dr. Emrich receives travel support and speaker fee from Siemens Healthineers. Dr. Tesche has received speaker’s fees from Siemens Healthineers and Heartflow Inc. Dr Bayer receive institutional research support from Bayer, Siemens, and HeartFlow. The other authors have no conflicts of interest to disclose.

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Brandt, V., Schoepf, U.J., Aquino, G.J. et al. Impact of machine-learning-based coronary computed tomography angiography–derived fractional flow reserve on decision-making in patients with severe aortic stenosis undergoing transcatheter aortic valve replacement. Eur Radiol 32, 6008–6016 (2022). https://doi.org/10.1007/s00330-022-08758-8

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