1. Characterization of cardiac resynchronization therapy response through machine learning and personalized models.
- Author
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Taconné M, Le Rolle V, Galli E, Owashi KP, Al Wazzan A, Donal E, and Hernández A
- Subjects
- Humans, Male, Female, Aged, Models, Cardiovascular, Middle Aged, Precision Medicine, Cardiac Resynchronization Therapy methods, Machine Learning, Heart Failure therapy, Heart Failure physiopathology
- Abstract
Introduction: The characterization and selection of heart failure (HF) patients for cardiac resynchronization therapy (CRT) remain challenging, with around 30% non-responder rate despite following current guidelines. This study aims to propose a novel hybrid approach, integrating machine-learning and personalized models, to identify explainable phenogroups of HF patients and predict their CRT response., Methods: The paper proposes the creation of a complete personalized model population based on preoperative CRT patient strain curves. Based on the parameters and features extracted from these personalized models, phenotypes of patients are identified thanks to a clustering algorithm and a random forest classification is provided., Results: A close match was observed between the 162 experimental and simulated myocardial strain curves, with a mean RMSE of 4.48% (±1.08) for the 162 patients. Five phenogroups of personalized models were identified from the clustering, with response rates ranging from 52% to 94%. The classification results show a mean area under the curves (AUC) of 0.86 ± 0.06 and provided a feature importance analysis with 22 features selected. Results show both regional myocardial contractility (from 22.5% to 33.0%), tissue viability and electrical activation delays importance on CRT response for each HF patient (from 55.8 ms to 88.4 ms)., Discussion: The patient-specific model parameters' analysis provides an explainable interpretation of HF patient phenogroups in relation to physiological mechanisms that seem predictive of the CRT response. These novel combined approaches appear as promising tools to improve understanding of LV mechanical dyssynchrony for HF patient characterization and CRT selection., Competing Interests: Declaration of competing interest No conflict of interest exists. We wish to confirm that there are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)
- Published
- 2024
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