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Computational instantaneous wave‐free ratio (IFR) for patient‐specific coronary artery stenoses using 1D network models

Jason Carson Orcid Logo, Carl Roobottom, Robin Alcock, Perumal Nithiarasu Orcid Logo, Jason Carson

International Journal for Numerical Methods in Biomedical Engineering, Volume: 35, Issue: 11

Swansea University Authors: Jason Carson Orcid Logo, Perumal Nithiarasu Orcid Logo, Jason Carson

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DOI (Published version): 10.1002/cnm.3255

Abstract

In this work, we estimate the diagnostic threshold of the instantaneous wave‐free ratio (iFR) through the use of a one‐dimensional haemodynamic framework. To this end, we first compared the computed fractional flow reserve (cFFR) predicted from a 1D computational framework with invasive clinical mea...

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Published in: International Journal for Numerical Methods in Biomedical Engineering
ISSN: 2040-7939 2040-7947
Published: Wiley 2019
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URI: https://cronfa.swan.ac.uk/Record/cronfa51923
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spelling 2019-10-09T15:00:09.4099162 v2 51923 2019-09-16 Computational instantaneous wave‐free ratio (IFR) for patient‐specific coronary artery stenoses using 1D network models c1f2c28fbe6a41c5134b45abde5abb93 0000-0001-6634-9123 Jason Carson Jason Carson true false 3b28bf59358fc2b9bd9a46897dbfc92d 0000-0002-4901-2980 Perumal Nithiarasu Perumal Nithiarasu true false ced1a1a2f38e4b283f16f138ce1131c5 Jason Carson Jason Carson true false 2019-09-16 MEDE In this work, we estimate the diagnostic threshold of the instantaneous wave‐free ratio (iFR) through the use of a one‐dimensional haemodynamic framework. To this end, we first compared the computed fractional flow reserve (cFFR) predicted from a 1D computational framework with invasive clinical measurements. The framework shows excellent promise and utilises minimal patient data from a cohort of 52 patients with a total of 66 stenoses. The diagnostic accuracy of the cFFR model was 75.76%, with a sensitivity of 71.43%, a specificity of 77.78%, a positive predictive value of 60%, and a negative predictive value of 85.37%. The validated model was then used to estimate the diagnostic threshold of iFR. The model determined a quadratic relationship between cFFR and the ciFR. The iFR diagnostic threshold was determined to be 0.8910 from a receiver operating characteristic curve that is in the range of 0.89 to 0.9 that is normally reported in clinical studies. Journal Article International Journal for Numerical Methods in Biomedical Engineering 35 11 Wiley 2040-7939 2040-7947 coronary arteries, FFR, haemodynamic modelling, iFR 30 11 2019 2019-11-30 10.1002/cnm.3255 COLLEGE NANME Biomedical Engineering COLLEGE CODE MEDE Swansea University UKRI, MR/S004076/1 UKRI, MR/S004076/1 2019-10-09T15:00:09.4099162 2019-09-16T18:18:02.7463469 Jason Carson 0000-0001-6634-9123 1 Carl Roobottom 2 Robin Alcock 3 Perumal Nithiarasu 0000-0002-4901-2980 4 Jason Carson 5 51923__16218__dc8613cb12254428bca9d590a9e58f2b.pdf Carson2019.pdf 2020-01-07T13:47:47.7318614 Output 1614622 application/pdf Version of Record true Released under the terms of a Creative Commons Attribution License (CC-BY). true eng https://creativecommons.org/licenses/by/4.0/
title Computational instantaneous wave‐free ratio (IFR) for patient‐specific coronary artery stenoses using 1D network models
spellingShingle Computational instantaneous wave‐free ratio (IFR) for patient‐specific coronary artery stenoses using 1D network models
Jason Carson
Perumal Nithiarasu
Jason Carson
title_short Computational instantaneous wave‐free ratio (IFR) for patient‐specific coronary artery stenoses using 1D network models
title_full Computational instantaneous wave‐free ratio (IFR) for patient‐specific coronary artery stenoses using 1D network models
title_fullStr Computational instantaneous wave‐free ratio (IFR) for patient‐specific coronary artery stenoses using 1D network models
title_full_unstemmed Computational instantaneous wave‐free ratio (IFR) for patient‐specific coronary artery stenoses using 1D network models
title_sort Computational instantaneous wave‐free ratio (IFR) for patient‐specific coronary artery stenoses using 1D network models
author_id_str_mv c1f2c28fbe6a41c5134b45abde5abb93
3b28bf59358fc2b9bd9a46897dbfc92d
ced1a1a2f38e4b283f16f138ce1131c5
author_id_fullname_str_mv c1f2c28fbe6a41c5134b45abde5abb93_***_Jason Carson
3b28bf59358fc2b9bd9a46897dbfc92d_***_Perumal Nithiarasu
ced1a1a2f38e4b283f16f138ce1131c5_***_Jason Carson
author Jason Carson
Perumal Nithiarasu
Jason Carson
author2 Jason Carson
Carl Roobottom
Robin Alcock
Perumal Nithiarasu
Jason Carson
format Journal article
container_title International Journal for Numerical Methods in Biomedical Engineering
container_volume 35
container_issue 11
publishDate 2019
institution Swansea University
issn 2040-7939
2040-7947
doi_str_mv 10.1002/cnm.3255
publisher Wiley
document_store_str 1
active_str 0
description In this work, we estimate the diagnostic threshold of the instantaneous wave‐free ratio (iFR) through the use of a one‐dimensional haemodynamic framework. To this end, we first compared the computed fractional flow reserve (cFFR) predicted from a 1D computational framework with invasive clinical measurements. The framework shows excellent promise and utilises minimal patient data from a cohort of 52 patients with a total of 66 stenoses. The diagnostic accuracy of the cFFR model was 75.76%, with a sensitivity of 71.43%, a specificity of 77.78%, a positive predictive value of 60%, and a negative predictive value of 85.37%. The validated model was then used to estimate the diagnostic threshold of iFR. The model determined a quadratic relationship between cFFR and the ciFR. The iFR diagnostic threshold was determined to be 0.8910 from a receiver operating characteristic curve that is in the range of 0.89 to 0.9 that is normally reported in clinical studies.
published_date 2019-11-30T04:03:59Z
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score 10.999161

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