ArticleCardiovascular digital health journal2021
Rationale and design of the SafeHeart study: Development and testing of a mHealth tool for the prediction of arrhythmic events and implantable cardioverter-defibrillator therapy.
Article in Cardiovascular digital health journal, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
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Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it, 11 citations in OpenAlex.
- Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Effectiveness and Safety of Low-Sodium Oxybate in Participants with Idiopathic Hypersomnia: Primary Results from the DUET Study.CNS drugs · 2026Trial
- Circadian rest-activity rhythm disruption and ventricular arrhythmias in implantable cardioverter-defibrillator patients: a prospective wearable study.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 · 2026Article
- Artificial intelligence-enhanced wearable technology enables ventricular arrhythmia prediction.European heart journal. Digital health · 2026Article
- A narrative review of the use of PROMs and machine learning to impact value-based clinical decision-making.BMC medical informatics and decision making · 2025Review
- Incidence and risk factors for first and recurrent ICD shock therapy in patients with an implantable cardioverter defibrillator.Journal of interventional cardiac electrophysiology : an international journal of arrhythmias and pacing · 2025Article
- Relationship between activity and sleep, as measured through a wearable accelerometer, and appropriate cardioverter defibrillator interventions: a prospective SafeHeart substudy.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 · 2024Article
- Deep behavioural representation learning reveals risk profiles for malignant ventricular arrhythmias.NPJ digital medicine · 2024Article
- Behavioural digital biomarkers enable real-time monitoring of patient-reported outcomes: a substudy of the multicentre, prospective observational SafeHeart study.European heart journal. Quality of care & clinical outcomes · 2024Observational
- Long-term adherence to a wearable for continuous behavioural activity measuring in the SafeHeart implantable cardioverter defibrillator population.European heart journal. Digital health · 2024Article
- Visions for digital integrated cardiovascular care: HRS Digital Health Committee perspectives.Cardiovascular digital health journal · 2024Article
Corrections and comments
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Authors and funding
9 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Patients with an implantable cardioverter-defibrillator (ICD) are at a high risk of malignant ventricular arrhythmias. The use of remote ICD monitoring, wearable devices, and patient-reported outcomes generate large volumes of potential valuable data. Artificial intelligence-based methods can be used to develop personalized prediction models and improve early-warning systems. Objective: The purpose of this study was to develop an integrated web-based personalized prediction engine for ICD therapy. Methods: This international, multicenter, prospective, observational study consists of 2 phases: (1) a development study and (2) a feasibility study. We plan to enroll 400 participants with an ICD (with or without cardiac resynchronization therapy) on remote monitoring: 300 participants in the development study and 100 in the feasibility study. During 12-month follow-up, electronic health record data, remote monitoring data, accelerometry-assessed physical behavior data, and patient-reported data are collected. By using machine- and deep-learning approaches, a prediction engine is developed to assess the risk probability of ICD therapy (shock and antitachycardia pacing). The feasibility of the prediction engine as a clinical tool, the SafeHeart Platform, is assessed during the feasibility study. Results: Development study recruitment commenced in 2021. The feasibility study starts in 2022. Conclusion: SafeHeart is the first study to prospectively collect a multimodal data set to construct a personalized prediction engine for ICD therapy. Moreover, SafeHeart explores the integration and added value of detailed objective accelerometer data in the prediction of clinical events. The translation of the SafeHeart Platform to clinical practice is examined during the feasibility study.
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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 Socratic graph.