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

Abstract

BACKGROUND Patients with an implantable cardioverterdefibrillator (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 earlywarning systems.OBJECTIVE The purpose of this study was to develop an integrated web-based personalized prediction engine for ICD therapy Q6. METHODS This international, multicenter, prospective, observational study consists of 2 phases: (1) a development study and (2) a feasibility study. A total of 400 participants with an ICD (with or without cardiac resynchronization therapy) on remote monitoring will be enrolled: 300 participants in the development study and 100 in the feasibility study. During 12-month followup, electronic health record data, remote monitoring data, accelerometry-assessed physical behavior data, and patientreported 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 (SafeHeart Q7 Platform) is assessed during the feasibility study. RESULTS Development study recruitment commences in 2021 Q8 . The feasibility study starts in 2022 Q9 .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.

Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review

“…Depending on the type of device used, three categories can be distinguished, namely, wristband devices, belts, and others. For example, in [ 45 , 47 , 55 , 68 , 70 , 80 , 85 , 98 , 103 , 107 , 108 , 112 ], the authors used smartwatches and smart wristbands to record vital signs. In addition, the authors in [ 40 , 44 , 49 , 63 , 64 , 76 , 78 , 83 , 91 , 93 , 96 , 102 , 110 , 120 , 121 ] used various wearable ECG devices such as smart vests and patches.…”

Visions for digital integrated cardiovascular care: HRS Digital Health Committee perspectives

“…Data from monitoring devices may not alter practice unless key outputs have been shown to be meaningful in clinical studies. 39 SafeHeart is an example of a study that leveraged multiple sources of data to provide real-time clinical decision support in patients at risk for an implantable cardiac defibrillator shock 40 via a clinical dashboard. Investigators at the Mayo Clinic developed a dashboard to display AI-based ECG algorithm outputs and Apple Watch tracings within the EHR, accessible by a single sign-on, to detect heart failure.…”

IoT Integration in Telemedicine: Investigating the Role of Internet of Things Devices in Facilitating Remote Patient Monitoring and Data Transmission

“…These devices continuously record electrocardiogram (ECG) data and employ machine learning algorithms to detect irregular heartbeats, promptly notifying patients and cardiologists (Jeong et al, 2022). This timely intervention, such as medication adjustments or catheter ablation, is crucial for preventing lifethreatening events and improving patients' quality of life (Frodi et al, 2021).…”