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Incorporating clinical parameters to improve the accuracy of angiography-derived computed fractional flow reserve( )
AIMS: Angiography-derived fractional flow reserve (angio-FFR) permits physiological lesion assessment without the need for an invasive pressure wire or induction of hyperaemia. However, accuracy is limited by assumptions made when defining the distal boundary, namely coronary microvascular resistanc...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9707918/ https://www.ncbi.nlm.nih.gov/pubmed/36712154 http://dx.doi.org/10.1093/ehjdh/ztac045 |
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author | Gosling, Rebecca C Gunn, Eleanor Wei, Hua Liang Gu, Yuanlin Rammohan, Vignesh Hughes, Timothy Hose, David Rodney Lawford, Patricia V Gunn, Julian P Morris, Paul D |
author_facet | Gosling, Rebecca C Gunn, Eleanor Wei, Hua Liang Gu, Yuanlin Rammohan, Vignesh Hughes, Timothy Hose, David Rodney Lawford, Patricia V Gunn, Julian P Morris, Paul D |
author_sort | Gosling, Rebecca C |
collection | PubMed |
description | AIMS: Angiography-derived fractional flow reserve (angio-FFR) permits physiological lesion assessment without the need for an invasive pressure wire or induction of hyperaemia. However, accuracy is limited by assumptions made when defining the distal boundary, namely coronary microvascular resistance (CMVR). We sought to determine whether machine learning (ML) techniques could provide a patient-specific estimate of CMVR and therefore improve the accuracy of angio-FFR. METHODS AND RESULTS: Patients with chronic coronary syndromes underwent coronary angiography with FFR assessment. Vessel-specific CMVR was computed using a three-dimensional computational fluid dynamics simulation with invasively measured proximal and distal pressures applied as boundary conditions. Predictive models were created using non-linear autoregressive moving average with exogenous input (NARMAX) modelling with computed CMVR as the dependent variable. Angio-FFR (VIRTUheart™) was computed using previously described methods. Three simulations were run: using a generic CMVR value (Model A); using ML-predicted CMVR based upon simple clinical data (Model B); and using ML-predicted CMVR also incorporating echocardiographic data (Model C). The diagnostic (FFR ≤ or >0.80) and absolute accuracies of these models were compared. Eighty-four patients underwent coronary angiography with FFR assessment in 157 vessels. The mean measured FFR was 0.79 (±0.15). The diagnostic and absolute accuracies of each personalized model were: (A) 73% and ±0.10; (B) 81% and ±0.07; and (C) 89% and ±0.05, P < 0.001. CONCLUSION: The accuracy of angio-FFR was dependent in part upon CMVR estimation. Personalization of CMVR from standard clinical data resulted in a significant reduction in angio-FFR error. |
format | Online Article Text |
id | pubmed-9707918 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-97079182023-01-27 Incorporating clinical parameters to improve the accuracy of angiography-derived computed fractional flow reserve( ) Gosling, Rebecca C Gunn, Eleanor Wei, Hua Liang Gu, Yuanlin Rammohan, Vignesh Hughes, Timothy Hose, David Rodney Lawford, Patricia V Gunn, Julian P Morris, Paul D Eur Heart J Digit Health Original Article AIMS: Angiography-derived fractional flow reserve (angio-FFR) permits physiological lesion assessment without the need for an invasive pressure wire or induction of hyperaemia. However, accuracy is limited by assumptions made when defining the distal boundary, namely coronary microvascular resistance (CMVR). We sought to determine whether machine learning (ML) techniques could provide a patient-specific estimate of CMVR and therefore improve the accuracy of angio-FFR. METHODS AND RESULTS: Patients with chronic coronary syndromes underwent coronary angiography with FFR assessment. Vessel-specific CMVR was computed using a three-dimensional computational fluid dynamics simulation with invasively measured proximal and distal pressures applied as boundary conditions. Predictive models were created using non-linear autoregressive moving average with exogenous input (NARMAX) modelling with computed CMVR as the dependent variable. Angio-FFR (VIRTUheart™) was computed using previously described methods. Three simulations were run: using a generic CMVR value (Model A); using ML-predicted CMVR based upon simple clinical data (Model B); and using ML-predicted CMVR also incorporating echocardiographic data (Model C). The diagnostic (FFR ≤ or >0.80) and absolute accuracies of these models were compared. Eighty-four patients underwent coronary angiography with FFR assessment in 157 vessels. The mean measured FFR was 0.79 (±0.15). The diagnostic and absolute accuracies of each personalized model were: (A) 73% and ±0.10; (B) 81% and ±0.07; and (C) 89% and ±0.05, P < 0.001. CONCLUSION: The accuracy of angio-FFR was dependent in part upon CMVR estimation. Personalization of CMVR from standard clinical data resulted in a significant reduction in angio-FFR error. Oxford University Press 2022-09-05 /pmc/articles/PMC9707918/ /pubmed/36712154 http://dx.doi.org/10.1093/ehjdh/ztac045 Text en © The Author(s) 2022. Published by Oxford University Press on behalf of the European Society of Cardiology. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Article Gosling, Rebecca C Gunn, Eleanor Wei, Hua Liang Gu, Yuanlin Rammohan, Vignesh Hughes, Timothy Hose, David Rodney Lawford, Patricia V Gunn, Julian P Morris, Paul D Incorporating clinical parameters to improve the accuracy of angiography-derived computed fractional flow reserve( ) |
title | Incorporating clinical parameters to improve the accuracy of angiography-derived computed fractional flow reserve( ) |
title_full | Incorporating clinical parameters to improve the accuracy of angiography-derived computed fractional flow reserve( ) |
title_fullStr | Incorporating clinical parameters to improve the accuracy of angiography-derived computed fractional flow reserve( ) |
title_full_unstemmed | Incorporating clinical parameters to improve the accuracy of angiography-derived computed fractional flow reserve( ) |
title_short | Incorporating clinical parameters to improve the accuracy of angiography-derived computed fractional flow reserve( ) |
title_sort | incorporating clinical parameters to improve the accuracy of angiography-derived computed fractional flow reserve( ) |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9707918/ https://www.ncbi.nlm.nih.gov/pubmed/36712154 http://dx.doi.org/10.1093/ehjdh/ztac045 |
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