Trial reportJournal of the American College of Cardiology2023
Predicting Malignant Ventricular Arrhythmias Using Real-Time Remote Monitoring.
Trial report in Journal of the American College of Cardiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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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
21 citing papers in PubMed, 25 citations in OpenAlex.
- Review
- Prediction of appropriate implantable cardioverter-defibrillator therapy using machine learning and routinely available clinical data.European heart journal. Digital health · 2026Article
- ANMCO position paper E-health integration in cardiology clinical care framework.European heart journal supplements : journal of the European Society of Cardiology · 2026Article
- Artificial intelligence-enhanced wearable technology enables ventricular arrhythmia prediction.European heart journal. Digital health · 2026Article
- Artificial Intelligence Powered Wearable and Portable Devices for Remote Cardiac Care and Population Health.Current cardiology reports · 2026Review
- Machine Learning-Based Risk Stratification for Sudden Cardiac Death Using Clinical and Device-Derived Data.Sensors (Basel, Switzerland) · 2025Article
- Artificial Intelligence and the Future of Cardiac Implantable Electronic Devices: Diagnostics, Monitoring, and Therapy.Journal of clinical medicine · 2025Review
- Artificial Intelligence in Cardiac Electrophysiology: A Clinically Oriented Review with Engineering Primers.Bioengineering (Basel, Switzerland) · 2025Review
- Utilizing multimodal artificial intelligence to advance cardiovascular diseases.Precision clinical medicine · 2025Review
- Hybrid CNN-Transformer-WOA model with XGBoost-SHAP feature selection for arrhythmia risk prediction in acute myocardial infarction patients.BMC medical informatics and decision making · 2025Article
- State of the Art of Artificial Intelligence in Clinical Electrophysiology in 2025: A Scientific Statement of the European Heart Rhythm Association (EHRA) of the ESC, the Heart Rhythm Society (HRS), and the ESC Working Group on E-Cardiology.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2025Article
- Prediction of sudden cardiac death using artificial intelligence: Current status and future directions.Heart rhythm · 2025Review
- Recent Advances in Cardiac Resynchronization Therapy: Current Treatment and Future Direction.Journal of clinical medicine · 2025Review
- Genetics of Sudden Cardiac Arrest: Overview of Genetic Risk Factors and Aetiologies.Arrhythmia & electrophysiology review · 2025Review
- Rhythm-Ready: Harnessing Smart Devices to Detect and Manage Arrhythmias.Current cardiology reports · 2024Review
- Deep behavioural representation learning reveals risk profiles for malignant ventricular arrhythmias.NPJ digital medicine · 2024Article
- Machine learning-derived cycle length variability metrics predict spontaneously terminating ventricular tachycardia in implantable cardioverter defibrillator recipients.European heart journal. Digital health · 2024Article
- Remote monitoring of cardiac implantable electronic devices and disease management.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2023Article
- Can Machine Learning Disrupt the Prediction of Sudden Death?Journal of the American College of Cardiology · 2023Article
- Syncope in Athletes: A Prelude to Sudden Cardiac Death?Missouri medicineArticle
Corrections and comments
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Authors and funding
7 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAlthough implantable cardioverter-defibrillator (ICD) therapies are associated with increased morbidity and mortality, the prediction of malignant ventricular arrhythmias has remained elusive.
objectivesThe purpose of this study was to evaluate whether daily remote-monitoring data may predict appropriate ICD therapies for ventricular tachycardia or ventricular fibrillation.
methodsThis was a post hoc analysis of IMPACT (Randomized trial of atrial arrhythmia monitoring to guide anticoagulation in patients with implanted defibrillator and cardiac resynchronization devices), a multicenter, randomized, controlled trial of 2,718 patients evaluating atrial tachyarrhythmias and anticoagulation for patients with heart failure and ICD or cardiac resynchronization therapy with defibrillator devices. All device therapies were adjudicated as either appropriate (to treat ventricular tachycardia or ventricular fibrillation) or inappropriate (all others). Remote monitoring data in the 30 days before device therapy were utilized to develop separate multivariable logistic regression and neural network models to predict appropriate device therapies.
resultsA total of 59,807 device transmissions were available for 2,413 patients (age 64 ± 11 years, 26% women, 64% ICD). Appropriate device therapies (141 shocks, 10 antitachycardia pacing) were delivered to 151 patients. Logistic regression identified shock lead impedance and ventricular ectopy as significantly associated with increased risk of appropriate device therapy (sensitivity 39%, specificity 91%, AUC: 0.72). Neural network modeling yielded significantly better (P < 0.01 for comparison) predictive performance (sensitivity 54%, specificity 96%, AUC: 0.90), and also identified patterns of change in atrial lead impedance, mean heart rate, and patient activity as predictors of appropriate therapies.
conclusionsDaily remote monitoring data may be utilized to predict malignant ventricular arrhythmias in the 30 days before device therapies. Neural networks complement and enhance conventional approaches to risk stratification.
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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.