Trial reportCirculation. Arrhythmia and electrophysiology2018
Machine Learning Algorithm Predicts Cardiac Resynchronization Therapy Outcomes: Lessons From the COMPANION Trial.
Trial report in Circulation. Arrhythmia and electrophysiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
59 citing papers in PubMed, 1 synthesis or guideline pooled it, 108 citations in OpenAlex.
- Artificial intelligence models in prediction of response to cardiac resynchronization therapy: a systematic review.Heart failure reviews · 2024Pooled it
- Using Machine-Learning for Prediction of the Response to Cardiac Resynchronization Therapy: The SMART-AV Study.JACC. Clinical electrophysiology · 2021Trial
- A Review of the Integration of Artificial Intelligence in Cardiac Electrophysiology.Journal of clinical medicine · 2026Review
- Artificial Intelligence Techniques in Cardiac Neuromodulation: Mechanisms, Applications, and Pathways to Clinical Translation.Journal of arrhythmia · 2026Review
- Advances in the Interpretation of the Electrocardiogram by Artificial Intelligence.Diagnostics (Basel, Switzerland) · 2026Review
- A New Method of Modeling the Multi-stage Decision-Making Process of CRT Using Machine Learning with Uncertainty Quantification.Journal of imaging informatics in medicine · 2026Article
- Machine learning prediction of surgical approach and clinical heterogeneity in cervical spondylotic myelopathy.BMC musculoskeletal disorders · 2026Article
- Artificial intelligence in heart failure.The Egyptian heart journal : (EHJ) : official bulletin of the Egyptian Society of Cardiology · 2026Review
- Rapid prediction of cardiac activation in the left ventricle with geometric deep learning: a step towards cardiac resynchronization therapy planning.NPJ digital medicine · 2026Article
- Artificial intelligence in cardiology: an updated systematic review with ethical considerations and challenges in implementing artificial intelligence models.Annals of medicine and surgery (2012) · 2026Article
- Artificial intelligence in medicine: Current applications in cardiology, oncology, and radiology.World journal of methodology · 2025Review
- Review
- Review
- Signals in the Cells: Multimodal and Contextualized Machine Learning Foundations for Therapeutics.bioRxiv : the preprint server for biology · 2024Article
- The evolving landscape of cardiometabolic diseases.EBioMedicine · 2024Article
- Machine learning-based prediction of 1-year all-cause mortality in patients undergoing CRT implantation: validation of the SEMMELWEIS-CRT score in the European CRT Survey I dataset.European heart journal. Digital health · 2024Article
- Machine learning models using symptoms and clinical variables to predict coronary artery disease on coronary angiography.Postepy w kardiologii interwencyjnej = Advances in interventional cardiology · 2024Article
- Heartificial intelligence: in what ways will artificial intelligence lead to changes in cardiology over the next 10 years.The British journal of cardiology · 2024Article
- Predictive value of machine learning algorithm of coronary artery calcium score and clinical factors for obstructive coronary artery disease in hypertensive patients.BMC medical informatics and decision making · 2023Article
- A machine learning method integrating ECG and gated SPECT for cardiac resynchronization therapy decision support.European journal of nuclear medicine and molecular imaging · 2023Article
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Authors and funding
9 authors.
Funding
Abstract
backgroundCardiac resynchronization therapy (CRT) reduces morbidity and mortality in heart failure patients with reduced left ventricular function and intraventricular conduction delay. However, individual outcomes vary significantly. This study sought to use a machine learning algorithm to develop a model to predict outcomes after CRT. METHODS AND
resultsModels were developed with machine learning algorithms to predict all-cause mortality or heart failure hospitalization at 12 months post-CRT in the COMPANION trial (Comparison of Medical Therapy, Pacing, and Defibrillation in Heart Failure). The best performing model was developed with the random forest algorithm. The ability of this model to predict all-cause mortality or heart failure hospitalization and all-cause mortality alone was compared with discrimination obtained using a combination of bundle branch block morphology and QRS duration. In the 595 patients with CRT-defibrillator in the COMPANION trial, 105 deaths occurred (median follow-up, 15.7 months). The survival difference across subgroups differentiated by bundle branch block morphology and QRS duration did not reach significance (
conclusionsIn the COMPANION trial, a machine learning algorithm produced a model that predicted clinical outcomes after CRT. Applied before device implant, this model may better differentiate outcomes over current clinical discriminators and improve shared decision-making with patients.
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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.