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Characterization of the Electrophysiologic Remodeling of Patients With Ischemic Cardiomyopathy by Clinical Measurements and Computer Simulations Coupled With Machine Learning

RATIONALE: Patients with ischemic cardiomyopathy (ICMP) are at high risk for malignant arrhythmias, largely due to electrophysiological remodeling of the non-infarcted myocardium. The electrophysiological properties of the non-infarcted myocardium of patients with ICMP remain largely unknown. OBJECT...

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Autores principales: Aronis, Konstantinos N., Prakosa, Adityo, Bergamaschi, Teya, Berger, Ronald D., Boyle, Patrick M., Chrispin, Jonathan, Ju, Suyeon, Marine, Joseph E., Sinha, Sunil, Tandri, Harikrishna, Ashikaga, Hiroshi, Trayanova, Natalia A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8317643/
https://www.ncbi.nlm.nih.gov/pubmed/34335294
http://dx.doi.org/10.3389/fphys.2021.684149
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author Aronis, Konstantinos N.
Prakosa, Adityo
Bergamaschi, Teya
Berger, Ronald D.
Boyle, Patrick M.
Chrispin, Jonathan
Ju, Suyeon
Marine, Joseph E.
Sinha, Sunil
Tandri, Harikrishna
Ashikaga, Hiroshi
Trayanova, Natalia A.
author_facet Aronis, Konstantinos N.
Prakosa, Adityo
Bergamaschi, Teya
Berger, Ronald D.
Boyle, Patrick M.
Chrispin, Jonathan
Ju, Suyeon
Marine, Joseph E.
Sinha, Sunil
Tandri, Harikrishna
Ashikaga, Hiroshi
Trayanova, Natalia A.
author_sort Aronis, Konstantinos N.
collection PubMed
description RATIONALE: Patients with ischemic cardiomyopathy (ICMP) are at high risk for malignant arrhythmias, largely due to electrophysiological remodeling of the non-infarcted myocardium. The electrophysiological properties of the non-infarcted myocardium of patients with ICMP remain largely unknown. OBJECTIVES: To assess the pro-arrhythmic behavior of non-infarcted myocardium in ICMP patients and couple computational simulations with machine learning to establish a methodology for the development of disease-specific action potential models based on clinically measured action potential duration restitution (APDR) data. METHODS AND RESULTS: We enrolled 22 patients undergoing left-sided ablation (10 ICMP) and compared APDRs between ICMP and structurally normal left ventricles (SNLVs). APDRs were clinically assessed with a decremental pacing protocol. Using genetic algorithms (GAs), we constructed populations of action potential models that incorporate the cohort-specific APDRs. The variability in the populations of ICMP and SNLV models was captured by clustering models based on their similarity using unsupervised machine learning. The pro-arrhythmic potential of ICMP and SNLV models was assessed in cell- and tissue-level simulations. Clinical measurements established that ICMP patients have a steeper APDR slope compared to SNLV (by 38%, p < 0.01). In cell-level simulations, APD alternans were induced in ICMP models at a longer cycle length compared to SNLV models (385–400 vs 355 ms). In tissue-level simulations, ICMP models were more susceptible for sustained functional re-entry compared to SNLV models. CONCLUSION: Myocardial remodeling in ICMP patients is manifested as a steeper APDR compared to SNLV, which underlies the greater arrhythmogenic propensity in these patients, as demonstrated by cell- and tissue-level simulations using action potential models developed by GAs from clinical measurements. The methodology presented here captures the uncertainty inherent to GAs model development and provides a blueprint for use in future studies aimed at evaluating electrophysiological remodeling resulting from other cardiac diseases.
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spelling pubmed-83176432021-07-29 Characterization of the Electrophysiologic Remodeling of Patients With Ischemic Cardiomyopathy by Clinical Measurements and Computer Simulations Coupled With Machine Learning Aronis, Konstantinos N. Prakosa, Adityo Bergamaschi, Teya Berger, Ronald D. Boyle, Patrick M. Chrispin, Jonathan Ju, Suyeon Marine, Joseph E. Sinha, Sunil Tandri, Harikrishna Ashikaga, Hiroshi Trayanova, Natalia A. Front Physiol Physiology RATIONALE: Patients with ischemic cardiomyopathy (ICMP) are at high risk for malignant arrhythmias, largely due to electrophysiological remodeling of the non-infarcted myocardium. The electrophysiological properties of the non-infarcted myocardium of patients with ICMP remain largely unknown. OBJECTIVES: To assess the pro-arrhythmic behavior of non-infarcted myocardium in ICMP patients and couple computational simulations with machine learning to establish a methodology for the development of disease-specific action potential models based on clinically measured action potential duration restitution (APDR) data. METHODS AND RESULTS: We enrolled 22 patients undergoing left-sided ablation (10 ICMP) and compared APDRs between ICMP and structurally normal left ventricles (SNLVs). APDRs were clinically assessed with a decremental pacing protocol. Using genetic algorithms (GAs), we constructed populations of action potential models that incorporate the cohort-specific APDRs. The variability in the populations of ICMP and SNLV models was captured by clustering models based on their similarity using unsupervised machine learning. The pro-arrhythmic potential of ICMP and SNLV models was assessed in cell- and tissue-level simulations. Clinical measurements established that ICMP patients have a steeper APDR slope compared to SNLV (by 38%, p < 0.01). In cell-level simulations, APD alternans were induced in ICMP models at a longer cycle length compared to SNLV models (385–400 vs 355 ms). In tissue-level simulations, ICMP models were more susceptible for sustained functional re-entry compared to SNLV models. CONCLUSION: Myocardial remodeling in ICMP patients is manifested as a steeper APDR compared to SNLV, which underlies the greater arrhythmogenic propensity in these patients, as demonstrated by cell- and tissue-level simulations using action potential models developed by GAs from clinical measurements. The methodology presented here captures the uncertainty inherent to GAs model development and provides a blueprint for use in future studies aimed at evaluating electrophysiological remodeling resulting from other cardiac diseases. Frontiers Media S.A. 2021-07-14 /pmc/articles/PMC8317643/ /pubmed/34335294 http://dx.doi.org/10.3389/fphys.2021.684149 Text en Copyright © 2021 Aronis, Prakosa, Bergamaschi, Berger, Boyle, Chrispin, Ju, Marine, Sinha, Tandri, Ashikaga and Trayanova. https://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 Physiology
Aronis, Konstantinos N.
Prakosa, Adityo
Bergamaschi, Teya
Berger, Ronald D.
Boyle, Patrick M.
Chrispin, Jonathan
Ju, Suyeon
Marine, Joseph E.
Sinha, Sunil
Tandri, Harikrishna
Ashikaga, Hiroshi
Trayanova, Natalia A.
Characterization of the Electrophysiologic Remodeling of Patients With Ischemic Cardiomyopathy by Clinical Measurements and Computer Simulations Coupled With Machine Learning
title Characterization of the Electrophysiologic Remodeling of Patients With Ischemic Cardiomyopathy by Clinical Measurements and Computer Simulations Coupled With Machine Learning
title_full Characterization of the Electrophysiologic Remodeling of Patients With Ischemic Cardiomyopathy by Clinical Measurements and Computer Simulations Coupled With Machine Learning
title_fullStr Characterization of the Electrophysiologic Remodeling of Patients With Ischemic Cardiomyopathy by Clinical Measurements and Computer Simulations Coupled With Machine Learning
title_full_unstemmed Characterization of the Electrophysiologic Remodeling of Patients With Ischemic Cardiomyopathy by Clinical Measurements and Computer Simulations Coupled With Machine Learning
title_short Characterization of the Electrophysiologic Remodeling of Patients With Ischemic Cardiomyopathy by Clinical Measurements and Computer Simulations Coupled With Machine Learning
title_sort characterization of the electrophysiologic remodeling of patients with ischemic cardiomyopathy by clinical measurements and computer simulations coupled with machine learning
topic Physiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8317643/
https://www.ncbi.nlm.nih.gov/pubmed/34335294
http://dx.doi.org/10.3389/fphys.2021.684149
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