Diagnostic performance of virtual fractional flow reserve derived from routine coronary angiography using segmentation free reduced order (1-dimensional) flow modelling

Introduction Fractional flow reserve (FFR) improves assessment of the physiological significance of coronary lesions compared with conventional angiography. However, it is an invasive investigation. We tested the performance of a virtual FFR (1D-vFFR) using routine angiographic images and a rapidly...

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Main Authors: Kevin Mohee, Jonathan P Mynard, Gauravsingh Dhunnoo, Rhodri Davies, Perumal Nithiarasu, Julian P Halcox, Daniel R Obaid
Format: Article
Language:English
Published: SAGE Publishing 2020-11-01
Series:JRSM Cardiovascular Disease
Online Access:https://doi.org/10.1177/2048004020967578
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spelling doaj-8a8cf1b12e194e63ae10f500b8f756df2020-11-25T04:08:26ZengSAGE PublishingJRSM Cardiovascular Disease2048-00402020-11-01910.1177/2048004020967578Diagnostic performance of virtual fractional flow reserve derived from routine coronary angiography using segmentation free reduced order (1-dimensional) flow modellingKevin MoheeJonathan P MynardGauravsingh DhunnooRhodri DaviesPerumal NithiarasuJulian P HalcoxDaniel R ObaidIntroduction Fractional flow reserve (FFR) improves assessment of the physiological significance of coronary lesions compared with conventional angiography. However, it is an invasive investigation. We tested the performance of a virtual FFR (1D-vFFR) using routine angiographic images and a rapidly performed reduced order computational model. Methods Quantitative coronary angiography (QCA) was performed in 102 with coronary lesions assessed by invasive FFR. A 1D-vFFR for each lesion was created using reduced order (one-dimensional) computational flow modelling derived from conventional angiographic images and patient specific estimates of coronary flow. The diagnostic accuracy of 1D-vFFR and QCA derived stenosis was compared against the gold standard of invasive FFR using area under the receiver operator characteristic curve (AUC). Results QCA revealed the mean coronary stenosis diameter was 44% ± 12% and lesion length 13 ± 7 mm. Following angiography calculation of the 1DvFFR took less than one minute. Coronary stenosis (QCA) had a significant but weak correlation with FFR (r = −0.2, p = 0.04) and poor diagnostic performance to identify lesions with FFR <0.80 (AUC 0.39, p = 0.09), (sensitivity – 58% and specificity – 26% at a QCA stenosis of 50%). In contrast, 1D-vFFR had a better correlation with FFR (r = 0.32, p = 0.01) and significantly better diagnostic performance (AUC 0.67, p = 0.007), (sensitivity – 92% and specificity - 29% at a 1D-vFFR of 0.7). Conclusions 1D-vFFR improves the determination of functionally significant coronary lesions compared with conventional angiography without requiring a pressure-wire or hyperaemia induction. It is fast enough to influence immediate clinical decision-making but requires further clinical evaluation.https://doi.org/10.1177/2048004020967578
collection DOAJ
language English
format Article
sources DOAJ
author Kevin Mohee
Jonathan P Mynard
Gauravsingh Dhunnoo
Rhodri Davies
Perumal Nithiarasu
Julian P Halcox
Daniel R Obaid
spellingShingle Kevin Mohee
Jonathan P Mynard
Gauravsingh Dhunnoo
Rhodri Davies
Perumal Nithiarasu
Julian P Halcox
Daniel R Obaid
Diagnostic performance of virtual fractional flow reserve derived from routine coronary angiography using segmentation free reduced order (1-dimensional) flow modelling
JRSM Cardiovascular Disease
author_facet Kevin Mohee
Jonathan P Mynard
Gauravsingh Dhunnoo
Rhodri Davies
Perumal Nithiarasu
Julian P Halcox
Daniel R Obaid
author_sort Kevin Mohee
title Diagnostic performance of virtual fractional flow reserve derived from routine coronary angiography using segmentation free reduced order (1-dimensional) flow modelling
title_short Diagnostic performance of virtual fractional flow reserve derived from routine coronary angiography using segmentation free reduced order (1-dimensional) flow modelling
title_full Diagnostic performance of virtual fractional flow reserve derived from routine coronary angiography using segmentation free reduced order (1-dimensional) flow modelling
title_fullStr Diagnostic performance of virtual fractional flow reserve derived from routine coronary angiography using segmentation free reduced order (1-dimensional) flow modelling
title_full_unstemmed Diagnostic performance of virtual fractional flow reserve derived from routine coronary angiography using segmentation free reduced order (1-dimensional) flow modelling
title_sort diagnostic performance of virtual fractional flow reserve derived from routine coronary angiography using segmentation free reduced order (1-dimensional) flow modelling
publisher SAGE Publishing
series JRSM Cardiovascular Disease
issn 2048-0040
publishDate 2020-11-01
description Introduction Fractional flow reserve (FFR) improves assessment of the physiological significance of coronary lesions compared with conventional angiography. However, it is an invasive investigation. We tested the performance of a virtual FFR (1D-vFFR) using routine angiographic images and a rapidly performed reduced order computational model. Methods Quantitative coronary angiography (QCA) was performed in 102 with coronary lesions assessed by invasive FFR. A 1D-vFFR for each lesion was created using reduced order (one-dimensional) computational flow modelling derived from conventional angiographic images and patient specific estimates of coronary flow. The diagnostic accuracy of 1D-vFFR and QCA derived stenosis was compared against the gold standard of invasive FFR using area under the receiver operator characteristic curve (AUC). Results QCA revealed the mean coronary stenosis diameter was 44% ± 12% and lesion length 13 ± 7 mm. Following angiography calculation of the 1DvFFR took less than one minute. Coronary stenosis (QCA) had a significant but weak correlation with FFR (r = −0.2, p = 0.04) and poor diagnostic performance to identify lesions with FFR <0.80 (AUC 0.39, p = 0.09), (sensitivity – 58% and specificity – 26% at a QCA stenosis of 50%). In contrast, 1D-vFFR had a better correlation with FFR (r = 0.32, p = 0.01) and significantly better diagnostic performance (AUC 0.67, p = 0.007), (sensitivity – 92% and specificity - 29% at a 1D-vFFR of 0.7). Conclusions 1D-vFFR improves the determination of functionally significant coronary lesions compared with conventional angiography without requiring a pressure-wire or hyperaemia induction. It is fast enough to influence immediate clinical decision-making but requires further clinical evaluation.
url https://doi.org/10.1177/2048004020967578
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