Combination of personalized computational modeling and machine learning for optimization of left ventricular pacing site in cardiac resynchronization therapy
Электронный научный архив УРФУ
Информация об архиве | Просмотр оригинала| Поле | Значение | |
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Combination of personalized computational modeling and machine learning for optimization of left ventricular pacing site in cardiac resynchronization therapy
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| Автор |
Dokuchaev, A.
Chumarnaya, T. Bazhutina, A. Khamzin, S. Lebedeva, V. Lyubimtseva, T. Zubarev, S. Lebedev, D. Solovyova, O. |
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| Тематика |
CARDIAC MODELING
CARDIAC RESYNCHRONIZATION THERAPY ELECTROPHYSIOLOGY HEART FAILURE HYBRID APPROACH MACHINE LEARNING OPTIMAL DESIGN FOR PACING LEAD POSITION PREDICTION ARTICLE BAYES THEOREM CARDIAC RESYNCHRONIZATION THERAPY CLASSIFIER COHORT ANALYSIS COMPUTER ASSISTED TOMOGRAPHY COMPUTER MODEL ELECTROCARDIOGRAM EVALUATION STUDY HEART ELECTROPHYSIOLOGY HEART FAILURE HEART LEFT VENTRICLE HEART LEFT VENTRICLE EJECTION FRACTION HEART LEFT VENTRICLE PACING SITE HEART RHYTHM HEART VENTRICLE CONTRACTION HEART VENTRICLE PACING HUMAN LOGISTIC REGRESSION ANALYSIS MACHINE LEARNING MAJOR CLINICAL STUDY NUCLEAR MAGNETIC RESONANCE IMAGING PATIENT SELECTION PERSONALIZED MEDICINE PREDICTION PREDICTOR VARIABLE PROOF OF CONCEPT RECEIVER OPERATING CHARACTERISTIC RETROSPECTIVE STUDY SIMULATION SUPERVISED MACHINE LEARNING TREATMENT RESPONSE VALIDATION STUDY |
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| Описание |
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 DPS 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 DPS was shorter in the responders. The max ML-score and DPS were found to be strong predictors of CRT response (ROC AUC = 0.85). In the group with max ML-score > 0.5 and DPS< 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. Copyright © 2023 Dokuchaev, Chumarnaya, Bazhutina, Khamzin, Lebedeva, Lyubimtseva, Zubarev, Lebedev and Solovyova.
Russian Science Foundation, RSF: 19-14-00134 This work was supported by Russian Science Foundation Grant No. 19-14-00134. |
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| Дата |
2024-04-05T16:28:44Z
2024-04-05T16:28:44Z 2023 |
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| Тип |
Article
Journal article (info:eu-repo/semantics/article) |info:eu-repo/semantics/publishedVersion |
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| Идентификатор |
Dokuchaev, A, Chumarnaya, T, Bazhutina, A, Khamzin, S, Lebedeva, V, Lyubimtseva, T, Zubarev, S, Lebedev, D & Solovyova, O 2023, 'Combination of personalized computational modeling and machine learning for optimization of left ventricular pacing site in cardiac resynchronization therapy', Frontiers in Physiology, Том. 14, 1162520. https://doi.org/10.3389/fphys.2023.1162520
Dokuchaev, A., Chumarnaya, T., Bazhutina, A., Khamzin, S., Lebedeva, V., Lyubimtseva, T., Zubarev, S., Lebedev, D., & Solovyova, O. (2023). Combination of personalized computational modeling and machine learning for optimization of left ventricular pacing site in cardiac resynchronization therapy. Frontiers in Physiology, 14, [1162520]. https://doi.org/10.3389/fphys.2023.1162520 1664-042X Final All Open Access, Gold, Green https://www.scopus.com/inward/record.uri?eid=2-s2.0-85165659555&doi=10.3389%2ffphys.2023.1162520&partnerID=40&md5=f1421f03573ffe2b1c564f6ccbb2383d https://www.frontiersin.org/articles/10.3389/fphys.2023.1162520/pdf http://elar.urfu.ru/handle/10995/130665 10.3389/fphys.2023.1162520 85165659555 001033654000001 |
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| Язык |
en
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| Связанные ресурсы |
info:eu-repo/grantAgreement/RSF//19-14-00134
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| Права |
Open access (info:eu-repo/semantics/openAccess)
cc-by https://creativecommons.org/licenses/by/4.0/ |
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| Формат |
application/pdf
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| Издатель |
Frontiers Media SA
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| Источник |
Frontiers in Physiology
Frontiers in Physiology |
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