Distal-vessel fractional flow reserve by computed tomography to monitor epicardial coronary artery disease

Michael Chen; Shone O Almeida; James W Sayre; Ronald P Karlsberg; René R Sevag Packard

doi:10.1093/ehjci/jead229

Distal-vessel fractional flow reserve by computed tomography to monitor epicardial coronary artery disease

Eur Heart J Cardiovasc Imaging. 2024 Jan 29;25(2):163-172. doi: 10.1093/ehjci/jead229.

Authors

Michael Chen¹, Shone O Almeida², James W Sayre³, Ronald P Karlsberg^{2

4}, René R Sevag Packard^{1

2

5

6

7

8

9

10}

Affiliations

¹ Division of Cardiology, Department of Medicine, David Geffen School of Medicine, University of California, 10833 Le Conte Ave., CHS Building Room 43-268, Los Angeles, CA 90095, USA.
² Cardiovascular Research Foundation of Southern California, Beverly Hills, CA, USA.
³ Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, CA, USA.
⁴ Cedars-Sinai Smidt Heart Institute, Los Angeles, CA, USA.
⁵ Ronald Reagan UCLA Medical Center, Los Angeles, CA, USA.
⁶ Veterans Affairs West Los Angeles Medical Center, Los Angeles, CA, USA.
⁷ Department of Physiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
⁸ Jonsson Comprehensive Cancer Center, University of California, Los Angeles, CA, USA.
⁹ Molecular Biology Institute, University of California, Los Angeles, CA, USA.
¹⁰ California NanoSystems Institute, University of California, Los Angeles, CA, USA.

Abstract

Aims: Coronary computed tomography angiography (CTA) and fractional flow reserve by computed tomography (FFR-CT) are increasingly utilized to characterize coronary artery disease (CAD). We evaluated the feasibility of distal-vessel FFR-CT as an integrated measure of epicardial CAD that can be followed serially, assessed the CTA parameters that correlate with distal-vessel FFR-CT, and determined the combination of clinical and CTA parameters that best predict distal-vessel FFR-CT and distal-vessel FFR-CT changes.

Methods and results: Patients (n = 71) who underwent serial CTA scans at ≥2 years interval (median = 5.2 years) over a 14-year period were included in this retrospective study. Coronary arteries were analysed blindly using artificial intelligence-enabled quantitative coronary CTA. Two investigators jointly determined the anatomic location and corresponding distal-vessel FFR-CT values at CT1 and CT2. A total of 45.3% had no significant change, 27.8% an improvement, and 26.9% a worsening in distal-vessel FFR-CT at CT2. Stepwise multiple logistic regression analysis identified a four-parameter model consisting of stenosis diameter ratio, lumen volume, low density plaque volume, and age, that best predicted distal-vessel FFR-CT ≤ 0.80 with an area under the curve (AUC) = 0.820 at CT1 and AUC = 0.799 at CT2. Improvement of distal-vessel FFR-CT was captured by a decrease in high-risk plaque and increases in lumen volume and remodelling index (AUC = 0.865), whereas increases in stenosis diameter ratio, medium density calcified plaque volume, and total cholesterol presaged worsening of distal-vessel FFR-CT (AUC = 0.707).

Conclusion: Distal-vessel FFR-CT permits the integrative assessment of epicardial atherosclerotic plaque burden in a vessel-specific manner and can be followed serially to determine changes in global CAD.

Keywords: FFR-CT; coronary CT angiogram; coronary artery disease; plaque characteristics; total plaque burden.

Published by Oxford University Press on behalf of the European Society of Cardiology 2023.

MeSH terms

Artificial Intelligence
Computed Tomography Angiography
Constriction, Pathologic
Coronary Angiography / methods
Coronary Artery Disease* / diagnostic imaging
Coronary Stenosis* / diagnostic imaging
Fractional Flow Reserve, Myocardial*
Humans
Plaque, Atherosclerotic* / diagnostic imaging
Predictive Value of Tests
ROC Curve
Retrospective Studies
Tomography, X-Ray Computed

Abstract

MeSH terms

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