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Impact of machine learning–based coronary computed tomography angiography fractional flow reserve on treatment decisions and clinical outcomes in patients with suspected coronary artery disease

  • Computed Tomography
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Abstract

Objectives

This study investigated the impact of machine learning (ML)–based fractional flow reserve derived from computed tomography (FFRCT) compared to invasive coronary angiography (ICA) for therapeutic decision-making and patient outcome in patients with suspected coronary artery disease (CAD).

Methods

One thousand one hundred twenty-one consecutive patients with stable chest pain who underwent coronary computed tomography angiography (CCTA) followed ICA within 90 days between January 2007 and December 2016 were included in this retrospective study. Medical records were reviewed for the endpoint of major adverse cardiac events (MACEs). FFRCT values were calculated using an artificial intelligence (AI) ML platform. Disagreements between hemodynamic significant stenosis via FFRCT and severe stenosis on qualitative CCTA and ICA were also evaluated.

Results

After FFRCT results were revealed, a change in the proposed treatment regimen chosen based on ICA results was seen in 167 patients (14.9%). Over a median follow-up time of 26 months (4–48 months), FFRCT ≤ 0.80 was associated with MACE (HR, 6.84 (95% CI, 3.57 to 13.11); p < 0.001), with superior prognostic value compared to severe stenosis on ICA (HR, 1.84 (95% CI, 1.24 to 2.73), p = 0.002) and CCTA (HR, 1.47 (95% CI, 1.01 to 2.14, p = 0.045). Reserving ICA and revascularization for vessels with positive FFRCT could have reduced the rate of ICA by 54.5% and lead to 4.4% fewer percutaneous interventions.

Conclusions

This study indicated ML-based FFRCT had superior prognostic value when compared to severe anatomic stenosis on CCTA and adding FFRCT may direct therapeutic decision-making with the potential to improve efficiency of ICA.

Key Points

• ML-based FFR CT shows superior outcome prediction value when compared to severe anatomic stenosis on CCTA.

• FFR CT noninvasively informs therapeutic decision-making with potential to change diagnostic workflows and enhance efficiencies in patients with suspected CAD.

• Reserving ICA and revascularization for vessels with positive FFR CT may reduce the normalcy rate of ICA and improve its efficiency.

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Abbreviations

CABG:

Coronary artery bypass grafting

CAD:

Coronary artery disease

CCTA:

Coronary computed tomography angiography

FFR:

Fractional flow reserve

FFRCT :

Fractional flow reserve derived from computed tomography

ICA:

Invasive coronary angiography

HR:

Hazard ratio

LAD:

Left anterior descending artery

LCX:

Left circumflex

MACE:

Major adverse cardiac events

ML:

Machine learning

OMT:

Optimal medical treatment

PCI:

Percutaneous coronary intervention

RCA:

Right coronary artery

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Acknowledgments

The authors gratefully acknowledge the financial supports by The National Key Research and Development Program of China (2017YFC0113400 for L.J.Z.).

Funding

This study was supported by the National Key Research and Development Program of China (2017YFC0113400 for L.J.Z.).

Author information

Author notes

  1. Hong Yan Qiao and Chun Xiang Tang contributed equally to this work.

Authors and Affiliations

  1. Department of Medical Imaging, Jinling Hospital, Nanjing Medical University, Nanjing, 210002, Jiangsu, China

    Hong Yan Qiao, Chang Sheng Zhou, Meng Jie Lu, Guang Ming Lu & Long Jiang Zhang

  2. Department of Medical Imaging, Affiliated Hospital of Jiangnan University, Wuxi, 214041, Jiangsu, China

    Hong Yan Qiao & Jian Wei Jiang

  3. Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, Jiangsu, China

    Chun Xiang Tang, U. Joseph Schoepf, Fan Zhou & Long Jiang Zhang

  4. Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, MSC 226, 25 Courtenay Dr, Charleston, SC, 29425, USA

    U. Joseph Schoepf, Christian Tesche, Richard R. Bayer 2nd, Dante A Giovagnoli & H. Todd Hudson Jr

  5. Department of Cardiology and Intensive Care Medicine, Heart Center Munich-Bogenhausen, Munich, Germany

    Christian Tesche

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

    Christian Tesche

  7. Siemens Healthcare Ltd., No. 278 ZhouZhu Road, Shanghai, 201318, China

    Jing Yan

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  1. Hong Yan Qiao
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Corresponding author

Correspondence to Long Jiang Zhang.

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Guarantor

The scientific guarantor of this publication is Long Jiang Zhang.

Disclosures

U. Joseph Schoepf is a consultant for and/or receives research support from Astellas, Bayer, Bracco, Elucid BioImaging, General Electric, Guerbet, HeartFlow, and Siemens Healthineers. The other authors have no conflicts of interest to disclose.

Statistics and biometry

Meng Jie Lu kindly provided statistical advice for this manuscript. No complex statistical methods were necessary for this paper.

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Written informed consent was waived by the Institutional Review Board.

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Institutional Review Board approval was obtained.

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Qiao, H.Y., Tang, C.X., Schoepf, U.J. et al. Impact of machine learning–based coronary computed tomography angiography fractional flow reserve on treatment decisions and clinical outcomes in patients with suspected coronary artery disease. Eur Radiol 30, 5841–5851 (2020). https://doi.org/10.1007/s00330-020-06964-w

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  • DOI: https://doi.org/10.1007/s00330-020-06964-w

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