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Guiding Myocardial Revascularization by Algorithmic Interpretation of FFR Pullback Curves: A Proof of Concept Study

Background: Coronary artery disease distribution along the vessel is a main determinant of FFR improvement after PCI. Identifying focal from diffuse disease from visual inspections of coronary angiogram (CA) and FFR pullback (FFR-PB) are operator-dependent. Computer science may standardize interpret...

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Autores principales: Argacha, Jean-François, Decamp, Jean, Vandeloo, Bert, Babin, Danilo, Lochy, Stijn, Van den Bussche, Karen, de Hemptinne, Quentin, Xaplanteris, Panagiotis, Magne, Julien, Segers, Patrick, Cosyns, Bernard
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7990785/
https://www.ncbi.nlm.nih.gov/pubmed/33778020
http://dx.doi.org/10.3389/fcvm.2021.623841
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author Argacha, Jean-François
Decamp, Jean
Vandeloo, Bert
Babin, Danilo
Lochy, Stijn
Van den Bussche, Karen
de Hemptinne, Quentin
Xaplanteris, Panagiotis
Magne, Julien
Segers, Patrick
Cosyns, Bernard
author_facet Argacha, Jean-François
Decamp, Jean
Vandeloo, Bert
Babin, Danilo
Lochy, Stijn
Van den Bussche, Karen
de Hemptinne, Quentin
Xaplanteris, Panagiotis
Magne, Julien
Segers, Patrick
Cosyns, Bernard
author_sort Argacha, Jean-François
collection PubMed
description Background: Coronary artery disease distribution along the vessel is a main determinant of FFR improvement after PCI. Identifying focal from diffuse disease from visual inspections of coronary angiogram (CA) and FFR pullback (FFR-PB) are operator-dependent. Computer science may standardize interpretations of such curves. Methods: A virtual stenting algorithm (VSA) was developed to perform an automated FFR-PB curve analysis. A survey analysis of the evaluations of 39 vessels with intermediate disease on CA and a distal FFR <0.8, rated by 5 interventional cardiologists, was performed. Vessel disease distribution and PCI strategy were successively rated based on CA and distal FFR (CA); CA and FFR-PB curve (CA/FFR-PB); and CA and VSA (CA/VSA). Inter-rater reliability was assessed using Fleiss kappa and an agreement analysis of CA/VSA rating with both algorithmic and human evaluation (operator) was performed. We hypothesize that VSA would increase rater agreement in interpretation of epicardial disease distribution and subsequent evaluation of PCI eligibility. Results: Inter-rater reliability in vessel disease assessment by CA, CA/FFR-PB, and CA/VSA were respectively, 0.32 (95% CI: 0.17–0.47), 0.38 (95% CI: 0.23–0.53), and 0.4 (95% CI: 0.25–0.55). The raters' overall agreement in vessel disease distribution and PCI eligibility was higher with the VSA than with the operator (respectively, 67 vs. 42%, and 80 vs. 70%, both p < 0.05). Compared to CA/FFR-PB, CA/VSA induced more reclassification toward a focal disease (92 vs. 56.2%, p < 0.01) with a trend toward more reclassification as eligible for PCI (70.6 vs. 33%, p = 0.06). Change in PCI strategy did not differ between CA/FFR-PB and CA/VSA (23.6 vs. 28.5%, p = 0.38). Conclusions: VSA is a new program to facilitate and standardize the FFR pullback curves analysis. When expert reviewers integrate VSA data, their assessments are less variable which might help to standardize PCI eligibility and strategy evaluations. Clinical Trial Registration: https://www.clinicaltrials.gov/ct2/show/NCT03824600.
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spelling pubmed-79907852021-03-26 Guiding Myocardial Revascularization by Algorithmic Interpretation of FFR Pullback Curves: A Proof of Concept Study Argacha, Jean-François Decamp, Jean Vandeloo, Bert Babin, Danilo Lochy, Stijn Van den Bussche, Karen de Hemptinne, Quentin Xaplanteris, Panagiotis Magne, Julien Segers, Patrick Cosyns, Bernard Front Cardiovasc Med Cardiovascular Medicine Background: Coronary artery disease distribution along the vessel is a main determinant of FFR improvement after PCI. Identifying focal from diffuse disease from visual inspections of coronary angiogram (CA) and FFR pullback (FFR-PB) are operator-dependent. Computer science may standardize interpretations of such curves. Methods: A virtual stenting algorithm (VSA) was developed to perform an automated FFR-PB curve analysis. A survey analysis of the evaluations of 39 vessels with intermediate disease on CA and a distal FFR <0.8, rated by 5 interventional cardiologists, was performed. Vessel disease distribution and PCI strategy were successively rated based on CA and distal FFR (CA); CA and FFR-PB curve (CA/FFR-PB); and CA and VSA (CA/VSA). Inter-rater reliability was assessed using Fleiss kappa and an agreement analysis of CA/VSA rating with both algorithmic and human evaluation (operator) was performed. We hypothesize that VSA would increase rater agreement in interpretation of epicardial disease distribution and subsequent evaluation of PCI eligibility. Results: Inter-rater reliability in vessel disease assessment by CA, CA/FFR-PB, and CA/VSA were respectively, 0.32 (95% CI: 0.17–0.47), 0.38 (95% CI: 0.23–0.53), and 0.4 (95% CI: 0.25–0.55). The raters' overall agreement in vessel disease distribution and PCI eligibility was higher with the VSA than with the operator (respectively, 67 vs. 42%, and 80 vs. 70%, both p < 0.05). Compared to CA/FFR-PB, CA/VSA induced more reclassification toward a focal disease (92 vs. 56.2%, p < 0.01) with a trend toward more reclassification as eligible for PCI (70.6 vs. 33%, p = 0.06). Change in PCI strategy did not differ between CA/FFR-PB and CA/VSA (23.6 vs. 28.5%, p = 0.38). Conclusions: VSA is a new program to facilitate and standardize the FFR pullback curves analysis. When expert reviewers integrate VSA data, their assessments are less variable which might help to standardize PCI eligibility and strategy evaluations. Clinical Trial Registration: https://www.clinicaltrials.gov/ct2/show/NCT03824600. Frontiers Media S.A. 2021-03-11 /pmc/articles/PMC7990785/ /pubmed/33778020 http://dx.doi.org/10.3389/fcvm.2021.623841 Text en Copyright © 2021 Argacha, Decamp, Vandeloo, Babin, Lochy, Van den Bussche, de Hemptinne, Xaplanteris, Magne, Segers and Cosyns. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Cardiovascular Medicine
Argacha, Jean-François
Decamp, Jean
Vandeloo, Bert
Babin, Danilo
Lochy, Stijn
Van den Bussche, Karen
de Hemptinne, Quentin
Xaplanteris, Panagiotis
Magne, Julien
Segers, Patrick
Cosyns, Bernard
Guiding Myocardial Revascularization by Algorithmic Interpretation of FFR Pullback Curves: A Proof of Concept Study
title Guiding Myocardial Revascularization by Algorithmic Interpretation of FFR Pullback Curves: A Proof of Concept Study
title_full Guiding Myocardial Revascularization by Algorithmic Interpretation of FFR Pullback Curves: A Proof of Concept Study
title_fullStr Guiding Myocardial Revascularization by Algorithmic Interpretation of FFR Pullback Curves: A Proof of Concept Study
title_full_unstemmed Guiding Myocardial Revascularization by Algorithmic Interpretation of FFR Pullback Curves: A Proof of Concept Study
title_short Guiding Myocardial Revascularization by Algorithmic Interpretation of FFR Pullback Curves: A Proof of Concept Study
title_sort guiding myocardial revascularization by algorithmic interpretation of ffr pullback curves: a proof of concept study
topic Cardiovascular Medicine
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7990785/
https://www.ncbi.nlm.nih.gov/pubmed/33778020
http://dx.doi.org/10.3389/fcvm.2021.623841
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