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Revisiting Abnormalities of Ventricular Depolarization: Redefining Phenotypes and Associated Outcomes Using Tree-Based Dimensionality Reduction.

作者信息

Gurnani Mehak, Patlatzoglou Konstantinos, Barker Joseph, Bivona Derek, Pastika Libor, Sieliwonczyk Ewa, Zeidaabadi Boroumand, Inglese Paolo, Curran Lara, Arnold Ahran D, O'Regan Declan, Whinnett Zachary, Bilchick Kenneth C, Peters Nicholas S, Kramer Daniel B, Waks Jonathan W, Sau Arunashis, Ng Fu Siong

机构信息

National Heart and Lung Institute, Imperial College London London UK.

Department of Biomedical Engineering University of Virginia Charlottesville VA USA.

出版信息

J Am Heart Assoc. 2025 Jul;14(13):e040814. doi: 10.1161/JAHA.124.040814. Epub 2025 Jun 18.

DOI:10.1161/JAHA.124.040814
PMID:40530476
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12449969/
Abstract

BACKGROUND

Abnormal ventricular depolarization, evident as a broad QRS complex on an ECG, is traditionally categorized into left bundle-branch block (LBBB) and right bundle-branch block or nonspecific intraventricular conduction delay. This categorization, although physiologically accurate, may fail to capture the nuances of diseases subtypes.

METHODS

We used unsupervised machine learning to identify and characterize novel broad QRS phenogroups. First, we trained a variational autoencoder on 1.1 million ECGs and discovered 51 latent features that showed high disentanglement and ECG reconstruction accuracy. We then extracted these features from 42 538 ECGs with QRS durations >120 milliseconds and employed a reversed graph embedding method to model population heterogeneity as a tree structure with different branches representing phenogroups.

RESULTS

Six phenogroups were identified, including phenogroups of right bundle-branch block and LBBB with varying risk of cardiovascular disease and mortality. The higher risk right bundle-branch block phenogroup exhibited increased risk of cardiovascular death (adjusted hazard ratio [aHR], 1.46 [1.30-1.63], <0.0001) and all-cause mortality (aHR, 1.24 [1.16-1.33], <0.0001) compared with the baseline phenogroup. Within LBBB ECGs, tree position predicted future cardiovascular disease risk differentially. Additionally, for subjects with LBBB undergoing cardiac resynchronization therapy, tree position predicted cardiac resynchronization therapy response independent of covariates, including QRS duration (adjusted odds ratio [aOR], 0.47 [0.25-0.86], <0.05).

CONCLUSIONS

Our findings challenge the current paradigm, highlighting the potential for these phenogroups to enhance cardiac resynchronization therapy patient selection for subjects with LBBB and guide investigation and follow-up strategies for subjects with higher risk right bundle-branch block.

摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3df1/12449969/54ca3478d6d8/JAH3-14-e040814-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3df1/12449969/c7eec1303bb0/JAH3-14-e040814-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3df1/12449969/045cf650c721/JAH3-14-e040814-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3df1/12449969/0e47cef3b5de/JAH3-14-e040814-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3df1/12449969/d5ecea260e27/JAH3-14-e040814-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3df1/12449969/6f17023c283c/JAH3-14-e040814-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3df1/12449969/66b72e610b48/JAH3-14-e040814-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3df1/12449969/5553d62a09b3/JAH3-14-e040814-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3df1/12449969/a7cacd45533c/JAH3-14-e040814-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3df1/12449969/54ca3478d6d8/JAH3-14-e040814-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3df1/12449969/c7eec1303bb0/JAH3-14-e040814-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3df1/12449969/045cf650c721/JAH3-14-e040814-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3df1/12449969/0e47cef3b5de/JAH3-14-e040814-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3df1/12449969/d5ecea260e27/JAH3-14-e040814-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3df1/12449969/6f17023c283c/JAH3-14-e040814-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3df1/12449969/66b72e610b48/JAH3-14-e040814-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3df1/12449969/5553d62a09b3/JAH3-14-e040814-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3df1/12449969/a7cacd45533c/JAH3-14-e040814-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3df1/12449969/54ca3478d6d8/JAH3-14-e040814-g001.jpg

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Artificial intelligence-enabled electrocardiogram for mortality and cardiovascular risk estimation: a model development and validation study.人工智能心电图用于死亡率和心血管风险评估:一项模型开发和验证研究。
Lancet Digit Health. 2024 Nov;6(11):e791-e802. doi: 10.1016/S2589-7500(24)00172-9.
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Machine learning of ECG waveforms and cardiac magnetic resonance for response and survival after cardiac resynchronization therapy.
基于心电图波形和心脏磁共振的机器学习预测心脏再同步化治疗后的反应和生存。
Comput Biol Med. 2024 Aug;178:108627. doi: 10.1016/j.compbiomed.2024.108627. Epub 2024 May 22.
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Genotype-Phenotype Taxonomy of Hypertrophic Cardiomyopathy.肥厚型心肌病的基因型-表型分类。
Circ Genom Precis Med. 2023 Dec;16(6):e004200. doi: 10.1161/CIRCGEN.123.004200. Epub 2023 Nov 28.
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The emerging role of artificial intelligence enabled electrocardiograms in healthcare.人工智能赋能心电图在医疗保健中的新兴作用。
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