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Combination of personalized computational modeling and machine learning for optimization of left ventricular pacing site in cardiac resynchronization therapy

Introduction: The 30–50% non-response rate to cardiac resynchronization therapy (CRT) calls for improved patient selection and optimized pacing lead placement. The study aimed to develop a novel technique using patient-specific cardiac models and machine learning (ML) to predict an optimal left vent...

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Autores principales: Dokuchaev, Arsenii, Chumarnaya, Tatiana, Bazhutina, Anastasia, Khamzin, Svyatoslav, Lebedeva, Viktoria, Lyubimtseva, Tamara, Zubarev, Stepan, Lebedev, Dmitry, Solovyova, Olga
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10367108/
https://www.ncbi.nlm.nih.gov/pubmed/37497440
http://dx.doi.org/10.3389/fphys.2023.1162520
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author Dokuchaev, Arsenii
Chumarnaya, Tatiana
Bazhutina, Anastasia
Khamzin, Svyatoslav
Lebedeva, Viktoria
Lyubimtseva, Tamara
Zubarev, Stepan
Lebedev, Dmitry
Solovyova, Olga
author_facet Dokuchaev, Arsenii
Chumarnaya, Tatiana
Bazhutina, Anastasia
Khamzin, Svyatoslav
Lebedeva, Viktoria
Lyubimtseva, Tamara
Zubarev, Stepan
Lebedev, Dmitry
Solovyova, Olga
author_sort Dokuchaev, Arsenii
collection PubMed
description Introduction: The 30–50% non-response rate to cardiac resynchronization therapy (CRT) calls for improved patient selection and optimized pacing lead placement. The study aimed to develop a novel technique using patient-specific cardiac models and machine learning (ML) to predict an optimal left ventricular (LV) pacing site (ML-PS) that maximizes the likelihood of LV ejection fraction (LVEF) improvement in a given CRT candidate. To validate the approach, we evaluated whether the distance D( PS ) between the clinical LV pacing site (ref-PS) and ML-PS is associated with improved response rate and magnitude. Materials and methods: We reviewed retrospective data for 57 CRT recipients. A positive response was defined as a more than 10% LVEF improvement. Personalized models of ventricular activation and ECG were created from MRI and CT images. The characteristics of ventricular activation during intrinsic rhythm and biventricular (BiV) pacing with ref-PS were derived from the models and used in combination with clinical data to train supervised ML classifiers. The best logistic regression model classified CRT responders with a high accuracy of 0.77 (ROC AUC = 0.84). The LR classifier, model simulations and Bayesian optimization with Gaussian process regression were combined to identify an optimal ML-PS that maximizes the ML-score of CRT response over the LV surface in each patient. Results: The optimal ML-PS improved the ML-score by 17 ± 14% over the ref-PS. Twenty percent of the non-responders were reclassified as positive at ML-PS. Selection of positive patients with a max ML-score >0.5 demonstrated an improved clinical response rate. The distance D( PS ) was shorter in the responders. The max ML-score and D( PS ) were found to be strong predictors of CRT response (ROC AUC = 0.85). In the group with max ML-score > 0.5 and D( PS )< 30 mm, the response rate was 83% compared to 14% in the rest of the cohort. LVEF improvement in this group was higher than in the other patients (16 ± 8% vs. 7 ± 8%). Conclusion: A new technique combining clinical data, personalized heart modelling and supervised ML demonstrates the potential for use in clinical practice to assist in optimizing patient selection and predicting optimal LV pacing lead position in HF candidates for CRT.
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spelling pubmed-103671082023-07-26 Combination of personalized computational modeling and machine learning for optimization of left ventricular pacing site in cardiac resynchronization therapy Dokuchaev, Arsenii Chumarnaya, Tatiana Bazhutina, Anastasia Khamzin, Svyatoslav Lebedeva, Viktoria Lyubimtseva, Tamara Zubarev, Stepan Lebedev, Dmitry Solovyova, Olga Front Physiol Physiology Introduction: The 30–50% non-response rate to cardiac resynchronization therapy (CRT) calls for improved patient selection and optimized pacing lead placement. The study aimed to develop a novel technique using patient-specific cardiac models and machine learning (ML) to predict an optimal left ventricular (LV) pacing site (ML-PS) that maximizes the likelihood of LV ejection fraction (LVEF) improvement in a given CRT candidate. To validate the approach, we evaluated whether the distance D( PS ) between the clinical LV pacing site (ref-PS) and ML-PS is associated with improved response rate and magnitude. Materials and methods: We reviewed retrospective data for 57 CRT recipients. A positive response was defined as a more than 10% LVEF improvement. Personalized models of ventricular activation and ECG were created from MRI and CT images. The characteristics of ventricular activation during intrinsic rhythm and biventricular (BiV) pacing with ref-PS were derived from the models and used in combination with clinical data to train supervised ML classifiers. The best logistic regression model classified CRT responders with a high accuracy of 0.77 (ROC AUC = 0.84). The LR classifier, model simulations and Bayesian optimization with Gaussian process regression were combined to identify an optimal ML-PS that maximizes the ML-score of CRT response over the LV surface in each patient. Results: The optimal ML-PS improved the ML-score by 17 ± 14% over the ref-PS. Twenty percent of the non-responders were reclassified as positive at ML-PS. Selection of positive patients with a max ML-score >0.5 demonstrated an improved clinical response rate. The distance D( PS ) was shorter in the responders. The max ML-score and D( PS ) were found to be strong predictors of CRT response (ROC AUC = 0.85). In the group with max ML-score > 0.5 and D( PS )< 30 mm, the response rate was 83% compared to 14% in the rest of the cohort. LVEF improvement in this group was higher than in the other patients (16 ± 8% vs. 7 ± 8%). Conclusion: A new technique combining clinical data, personalized heart modelling and supervised ML demonstrates the potential for use in clinical practice to assist in optimizing patient selection and predicting optimal LV pacing lead position in HF candidates for CRT. Frontiers Media S.A. 2023-07-11 /pmc/articles/PMC10367108/ /pubmed/37497440 http://dx.doi.org/10.3389/fphys.2023.1162520 Text en Copyright © 2023 Dokuchaev, Chumarnaya, Bazhutina, Khamzin, Lebedeva, Lyubimtseva, Zubarev, Lebedev and Solovyova. 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
Dokuchaev, Arsenii
Chumarnaya, Tatiana
Bazhutina, Anastasia
Khamzin, Svyatoslav
Lebedeva, Viktoria
Lyubimtseva, Tamara
Zubarev, Stepan
Lebedev, Dmitry
Solovyova, Olga
Combination of personalized computational modeling and machine learning for optimization of left ventricular pacing site in cardiac resynchronization therapy
title Combination of personalized computational modeling and machine learning for optimization of left ventricular pacing site in cardiac resynchronization therapy
title_full Combination of personalized computational modeling and machine learning for optimization of left ventricular pacing site in cardiac resynchronization therapy
title_fullStr Combination of personalized computational modeling and machine learning for optimization of left ventricular pacing site in cardiac resynchronization therapy
title_full_unstemmed Combination of personalized computational modeling and machine learning for optimization of left ventricular pacing site in cardiac resynchronization therapy
title_short Combination of personalized computational modeling and machine learning for optimization of left ventricular pacing site in cardiac resynchronization therapy
title_sort combination of personalized computational modeling and machine learning for optimization of left ventricular pacing site in cardiac resynchronization therapy
topic Physiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10367108/
https://www.ncbi.nlm.nih.gov/pubmed/37497440
http://dx.doi.org/10.3389/fphys.2023.1162520
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