Trial reportEuropace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology2022
Combining home monitoring temporal trends from implanted defibrillators and baseline patient risk profile to predict heart failure hospitalizations: results from the SELENE HF study.
Trial report in 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, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 2 of them syntheses that pooled it.
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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.
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Who cites it
46 citing papers in PubMed, 2 syntheses or guidelines pooled it, 86 citations in OpenAlex.
- Pooled it
- Impact of Multisensor CIED-based Heart Failure Monitoring on Mortality, Heart Failure Hospitalizations and Outpatient Visits: A Systematic Review.Current heart failure reports · 2025Pooled it
- Personalized Intervention Strategy Based on a Risk Score Generated From Subcutaneous Insertable Cardiac Monitor: Results From Phase 1 of ALLEVIATE-HF.Journal of the American Heart Association · 2024Trial
- Expert Consensus Statement: Committee on Implantable Devices and Committee on Social Issues, Japanese Heart Rhythm Society (JHRS) Statement on Remote Monitoring of Cardiac Implantable Electronic Devices.Journal of arrhythmia · 2026Article
- Clinical Utility of the TRENDS Remote Monitoring Function Integrated into a Wearable Cardioverter-Defibrillator.Sensors (Basel, Switzerland) · 2026Article
- Remote non-invasive ICT monitoring for heart failure: a feasibility study.Heart and vessels · 2026Observational
- Integrating remote CIED monitoring into heart failure care: evidence, challenges, and opportunities.Frontiers in cardiovascular medicine · 2026Review
- Predicting heart failure decompensation: focus on non-invasive monitoring.Cardiology journal · 2026Review
- Predicting acute decompensated heart failure using circadian markers from heart rate time series.ESC heart failure · 2025Article
- Noninvasive Multiparameter Monitoring for the Detection of Decompensated Heart Failure: Exploratory Study.JMIR formative research · 2025Article
- Article
- Comparative Diagnostic Efficacy of HeartLogic and TriageHF Algorithms in Remote Monitoring of Heart Failure: A Cohort Study.Journal of cardiovascular development and disease · 2025Article
- Remote monitoring and heart failure.European heart journal supplements : journal of the European Society of Cardiology · 2025Article
- Recent Advances in Cardiac Resynchronization Therapy: Current Treatment and Future Direction.Journal of clinical medicine · 2025Review
- Artificial intelligence in heart failure - a comprehensive literature review.Cardiology journal · 2025Review
- Clinical experience with remote programming of cardiac implantable electronic devices.Frontiers in digital health · 2025Article
- Implementation of a Cardiovascular Implantable Electronic Device Heart Failure Prediction Tool-Guided Management Pathway.The American journal of cardiology · 2024Article
- Remote Management of Heart Failure in Patients with Implantable Devices.Diagnostics (Basel, Switzerland) · 2024Review
- Biotronik Implantable Cardioverter Defibrillator for Measurement of Intrathoracic Impedance: A Report of Two Cases.Annals of noninvasive electrocardiology : the official journal of the International Society for Holter and Noninvasive Electrocardiology, Inc · 2024Article
- Personalized Heart Failure Management: Bridging Technology and Care.Journal of the American Heart Association · 2024Article
Corrections and comments
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Authors and funding
21 authors at 12 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimsWe developed and validated an algorithm for prediction of heart failure (HF) hospitalizations using remote monitoring (RM) data transmitted by implanted defibrillators. METHODS AND
resultsThe SELENE HF study enrolled 918 patients (median age 69 years, 81% men, median ejection fraction 30%) with cardiac resynchronization therapy (44%), dual-chamber (38%), or single-chamber defibrillators with atrial diagnostics (18%). To develop a predictive algorithm, temporal trends of diurnal and nocturnal heart rates, ventricular extrasystoles, atrial tachyarrhythmia burden, heart rate variability, physical activity, and thoracic impedance obtained by daily automatic RM were combined with a baseline risk-stratifier (Seattle HF Model) into one index. The primary endpoint was the first post-implant adjudicated HF hospitalization. After a median follow-up of 22.5 months since enrolment, patients were randomly allocated to the algorithm derivation group (n = 457; 31 endpoints) or algorithm validation group (n = 461; 29 endpoints). In the derivation group, the index showed a C-statistics of 0.89 [95% confidence interval (CI): 0.83-0.95] with 2.73 odds ratio (CI 1.98-3.78) for first HF hospitalization per unitary increase of index value (P < 0.001). In the validation group, sensitivity of predicting primary endpoint was 65.5% (CI 45.7-82.1%), median alerting time 42 days (interquartile range 21-89), and false (or unexplained) alert rate 0.69 (CI 0.64-0.74) [or 0.63 (CI 0.58-0.68)] per patient-year. Without the baseline risk-stratifier, the sensitivity remained 65.5% and the false/unexplained alert rates increased by ≈10% to 0.76/0.71 per patient-year.
conclusionWith the developed algorithm, two-thirds of first post-implant HF hospitalizations could be predicted timely with only 0.7 false alerts per patient-year.
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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.