ArticleCirculation. Arrhythmia and electrophysiology2020
Machine Learning of 12-Lead QRS Waveforms to Identify Cardiac Resynchronization Therapy Patients With Differential Outcomes.
Article in Circulation. Arrhythmia and electrophysiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it, 46 citations in OpenAlex.
- Artificial intelligence models in prediction of response to cardiac resynchronization therapy: a systematic review.Heart failure reviews · 2024Pooled it
- Artificial intelligence in heart failure.The Egyptian heart journal : (EHJ) : official bulletin of the Egyptian Society of Cardiology · 2026Review
- Clinical phenotypes among patients that underwent cardiac resynchronization therapy using unsupervised learning integrating gated SPECT.Nuclear medicine communications · 2025Article
- Phenotyping of Mitral Valve Prolapse Without Severe Mitral Regurgitation Using Electrocardiographic and Echocardiographic Data.JACC. Clinical electrophysiology · 2025Article
- Optimizing outcomes from cardiac resynchronization therapy: what do recent data and insights say?Expert review of cardiovascular therapy · 2024Review
- [Intrinsic steady-state pattern of mouse cardiac electrophysiology: analysis using a characterized quantitative electrocardiogram strategy].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2024Article
- Machine learning of ECG waveforms and cardiac magnetic resonance for response and survival after cardiac resynchronization therapy.Computers in biology and medicine · 2024Article
- Useful Electrocardiographic Signs to Support the Prediction of Favorable Response to Cardiac Resynchronization Therapy.Journal of cardiovascular development and disease · 2023Review
- Electrocardiogram-based deep learning improves outcome prediction following cardiac resynchronization therapy.European heart journal · 2023Article
- Article
- Combination of personalized computational modeling and machine learning for optimization of left ventricular pacing site in cardiac resynchronization therapy.Frontiers in physiology · 2023Article
- A multimodal deep learning model for cardiac resynchronisation therapy response prediction.Medical image analysis · 2022Article
- Using Deep-Learning Algorithms to Simultaneously Identify Right and Left Ventricular Dysfunction From the Electrocardiogram.JACC. Cardiovascular imaging · 2022Article
- Machine learning derived ECG risk score improves cardiovascular risk assessment in conjunction with coronary artery calcium scoring.Frontiers in cardiovascular medicine · 2022Article
- Cardiac resynchronization considerations in left bundle branch block.Frontiers in physiology · 2022Review
- Machine Learning-Predicted Progression to Permanent Atrial Fibrillation After Catheter Ablation.Frontiers in cardiovascular medicine · 2022Article
- Artificial intelligence-enhanced electrocardiography in cardiovascular disease management.Nature reviews. Cardiology · 2021Review
- Emerging Topics Update of the Brazilian Heart Failure Guideline - 2021.Arquivos brasileiros de cardiologia · 2021Article
- Machine Learning Prediction of Cardiac Resynchronisation Therapy Response From Combination of Clinical and Model-Driven Data.Frontiers in physiology · 2021Article
- Left Atrial Wall Stress and the Long-Term Outcome of Catheter Ablation of Atrial Fibrillation: An Artificial Intelligence-Based Prediction of Atrial Wall Stress.Frontiers in physiology · 2021Article
Corrections and comments
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Authors and funding
13 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundCardiac resynchronization therapy (CRT) improves heart failure outcomes but has significant nonresponse rates, highlighting limitations in ECG selection criteria: QRS duration (QRSd) ≥150 ms and subjective labeling of left bundle branch block (LBBB). We explored unsupervised machine learning of ECG waveforms to identify CRT subgroups that may differentiate outcomes beyond QRSd and LBBB.
methodsWe retrospectively analyzed 946 CRT patients with conduction delay. Principal component analysis (PCA) dimensionality reduction obtained a 2-dimensional representation of preCRT 12-lead QRS waveforms.
resultsCompared with QRS PCA Group 2 (
conclusionsUnsupervised machine learning of ECG waveforms identified CRT subgroups with relevance beyond LBBB and QRSd. This method may assist in objective classification of bundle branch block morphology in CRT.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.