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One-year outcomes of CCTA alone versus machine learning–based FFRCT for coronary artery disease: a single-center, prospective study

  • Cardiac
  • Published:
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Abstract

Objectives

To explore downstream management and outcomes of machine learning (ML)–based CT derived fractional flow reserve (FFRCT) strategy compared with an anatomical coronary computed tomography angiography (CCTA) alone assessment in participants with intermediate coronary artery stenosis.

Methods

In this prospective study conducted from April 2018 to March 2019, participants were assigned to either the CCTA or FFRCT group. The primary endpoint was the rate of invasive coronary angiography (ICA) that demonstrated non-obstructive disease at 90 days. Secondary endpoints included coronary revascularization and major adverse cardiovascular events (MACE) at 1-year follow-up.

Results

In total, 567 participants were allocated to the CCTA group and 566 to the FFRCT group. At 90 days, the rate of ICA without obstructive disease was higher in the CCTA group (33.3%, 39/117) than that (19.8%, 19/96) in the FFRCT group (risk difference [RD] = 13.5%, 95% confidence interval [CI]: 8.4%, 18.6%; p = 0.03). The ICA referral rate was higher in the CCTA group (27.5%, 156/567) than in the FFRCT group (20.3%, 115/566) (RD = 7.2%, 95% CI: 2.3%, 12.1%; p = 0.003). The revascularization-to-ICA ratio was lower in the CCTA group than that in the FFRCT group (RD = 19.8%, 95% CI: 14.1%, 25.5%, p = 0.002). MACE was more common in the CCTA group than that in the FFRCT group at 1 year (HR: 1.73; 95% CI: 1.01, 2.95; p = 0.04).

Conclusion

In patients with intermediate stenosis, the FFRCT strategy appears to be associated with a lower rate of referral for ICA, ICA without obstructive disease, and 1-year MACE when compared to the anatomical CCTA alone strategy.

Key Points

• In stable patients with intermediate stenosis, ML-based FFR CT strategy was associated with a lower referral ICA rate, a lower normalcy rate of ICA, and higher revascularization-to-ICA ratio than the CCTA strategy.

• Compared with the CCTA strategy, ML-based FFR CT shows superior outcome prediction value which appears to be associated with a lower rate of 1-year MACE.

• ML-based FFR CT strategy as a non-invasive “one-stop-shop” modality may be the potential to change diagnostic workflows in patients with suspected coronary artery disease.

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Abbreviations

CABG:

Coronary artery bypass grafting

CAD:

Coronary artery disease

CCTA:

Coronary computed tomography angiography

FFR:

Fractional flow reserve

FFRCT :

CT derived fractional flow reserve

HR:

Hazard ratio

ICA:

Invasive coronary angiography

LAD:

Left anterior descending artery

LCX:

Left circumflex

MACE:

Major adverse cardiovascular events

ML:

Machine learning

OMT:

Optimal medical therapy

PCI:

Percutaneous coronary intervention

RCA:

Right coronary artery

RD:

Risk difference

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Acknowledgements

The authors gratefully acknowledge the financial support of 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 Diagnostic Radiology, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, Jiangsu, China

    Hong Yan Qiao, Chun Xiang Tang, Meng Di Jiang, Chang Qing Yin, Chang Sheng Zhou, Fan Zhou, Meng Jie Lu, Guang Ming Lu, Qian Qian Ni & Long Jiang Zhang

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

    Hong Yan Qiao & Jian Wei Jiang

  3. 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, Richard R. Bayer 2nd & Christian Tesche

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

    Christian Tesche

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

    Christian Tesche

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  1. Hong Yan Qiao
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Corresponding authors

Correspondence to Guang Ming Lu or 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. One of the authors has significant statistical expertise. No complex statistical methods were necessary for this paper.

Informed consent

All participants provided written informed consent.

Ethical approval

Institutional Review Board approval was obtained.

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• Prospective

• Observational

• Performed at one institution

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Qiao, H.Y., Tang, C.X., Schoepf, U.J. et al. One-year outcomes of CCTA alone versus machine learning–based FFRCT for coronary artery disease: a single-center, prospective study. Eur Radiol 32, 5179–5188 (2022). https://doi.org/10.1007/s00330-022-08604-x

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  • DOI: https://doi.org/10.1007/s00330-022-08604-x

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