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Feasibility and prognostic role of machine learning-based FFRCT in patients with stent implantation

  • Computed Tomography
  • Published:
European Radiology Aims and scope Submit manuscript

An Editorial Comment to this article was published on 23 April 2021

Abstract

Objectives

To investigate the feasibility and prognostic implications of coronary CT angiography (CCTA) derived fractional flow reserve (FFRCT) in patients who have undergone stents implantation.

Methods

Firstly, the feasibility of FFRCT in stented vessels was validated. The diagnostic performance of FFRCT in identifying hemodynamically in-stent restenosis (ISR) in 33 patients with invasive FFR ≤ 0.88 as reference standard, intra-group correlation coefficient (ICC) between FFRCT and FFR was calculated. Secondly, prognostic value was assessed with 115 patients with serial CCTA scans after PCI. Stent characteristics (location, diameter, length, etc.), CCTA measurements (minimum lumen diameter [MLD], minimum lumen area [MLA], ISR), and FFRCT measurements (FFRCT, ΔFFRCT, ΔFFRCT/stent length) both at baseline and follow-up were recorded. Longitudinal analysis included changes of MLD, MLA, ISR, and FFRCT. The primary endpoint was major adverse cardiovascular events (MACE).

Results

Per-patient accuracy of FFRCT was 0.85 in identifying hemodynamically ISR. FFRCT had a good correlation with FFR (ICC = 0.84). 15.7% (18/115) developed MACE during 25 months since follow-up CCTA. Lasso regression identified age and follow-up ΔFFRCT/length as candidate variables. In the Cox proportional hazards model, age (hazard ratio [HR], 1.102 [95% CI, 1.032–1.177]; p = 0.004) and follow-up ΔFFRCT/length (HR, 1.014 [95% CI, 1.006–1.023]; p = 0.001) were independently associated with MACE (c-index = 0.856). Time-dependent ROC analysis showed AUC was 0.787 (95% CI, 0.594–0.980) at 25 months to predict adverse outcome. After bootstrap validation with 1000 resamplings, the bias-corrected c-index was 0.846.

Conclusions

Noninvasive ML-based FFRCT is feasible in patients following stents implantation and shows prognostic value in predicting adverse events after stents implantation in low-moderate risk patients.

Key Points

• Machine-learning-based FFRCT is feasible to evaluate the functional significance of in-stent restenosis in patients with stent implantation.

• Follow-up △FFRCT along with the stent length might have prognostic implication in patients with stent implantation and low-to-moderate risk after 2 years follow-up. The prognostic role of FFRCT in patients with moderate-to-high or high risk needs to be further studied.

• FFR CT might refine the clinical pathway of patients with stent implantation to invasive catheterization.

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Abbreviations

AUC:

Area under the curve

BMS:

Bare-metal stents

CAD:

Coronary artery disease

CCTA:

Coronary computed tomography angiography

CFD:

Computational fluid dynamics

DES:

Drug-eluting stents

FFRCT :

Fractional flow reserve derived from coronary computed tomography angiography

ISR:

In-stent restenosis

LAD:

Left anterior descending coronary artery

LCX:

Left circumflex coronary artery

MACE:

Major adverse cardiovascular events

ML:

Machine learning

MLA:

Minimum lumen area

MLD:

Minimum lumen diameter

PCI:

Percutaneous intervention

PDA:

Posterior descending artery

RCA:

Right coronary artery

TVR:

Target vessel revascularization

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Funding

This study has received funding from the General Project of Chinese Postdoctoral Science Foundation (2020M673677 for C.X.T.) and the National Key Research and Development Program of China (2017YFC0113400 for L.J.Z.).

Author information

Authors and Affiliations

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

    Chun Xiang Tang, Bang Jun Guo, Joseph U. Schoepf, Chun Yu Liu, Hong Yan Qiao, Fan Zhou, Guang Ming Lu, Chang Sheng Zhou & Long Jiang Zhang

  2. 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

    Joseph U. Schoepf & Richard R. Bayer II

Authors
  1. Chun Xiang Tang
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  2. Bang Jun Guo
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  3. Joseph U. Schoepf
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  5. Chun Yu Liu
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  6. Hong Yan Qiao
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  8. Guang Ming Lu
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  9. Chang Sheng Zhou
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  10. Long Jiang Zhang
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Corresponding authors

Correspondence to Chang Sheng Zhou or Long Jiang Zhang.

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Guarantor

The scientific guarantor of this publication is Long Jiang Zhang.

Conflict of interest

U. Joseph Schoepf is a consultant for and/or receives research support from Astellas, Bayer, GE, Guerbet, HeartFlow Inc., and Siemens. The other authors have no conflicts of interest to declare.

Statistics and biometry

Meng Jie Lu kindly provided statistical advice for this manuscript.

Informed consent

Written informed consent was not required for this study because of the retrospective nature of the study.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• Retrospective

• Diagnostic or prognostic study

• Multicenter study

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Chun Xiang Tang and Bang Jun Guo have made the same contribution to this study.

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Tang, C.X., Guo, B.J., Schoepf, J.U. et al. Feasibility and prognostic role of machine learning-based FFRCT in patients with stent implantation. Eur Radiol 31, 6592–6604 (2021). https://doi.org/10.1007/s00330-021-07922-w

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  • DOI: https://doi.org/10.1007/s00330-021-07922-w

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