Literature DB >> 34252387

Rationale and design of a digital trial using smartphones to detect subclinical atrial fibrillation in a population at risk: The eHealth-based Bavarian Alternative Detection of Atrial Fibrillation (eBRAVE-AF) trial.

Luisa Freyer1, Lukas von Stülpnagel1, Peter Spielbichler1, Nikolay Sappler2, Felix Wenner1, Michael Schreinlechner2, Aresa Krasniqi1, Amira Behroz1, Elodie Eiffener1, Martin Zens3, Theresa Dolejsi2, Steffen Massberg1, Konstantinos D Rizas4, Axel Bauer5.   

Abstract

Current guidelines recommend opportunistic screening for subclinical atrial fibrillation (AF) taking advantage of e-health-based technologies. However, the efficacy of a fully scalable e-health-based strategy for AF detection in a head-to-head comparison with routine symptom-based screening is unknown. eBRAVE-AF is an investigator-initiated, digital, prospective, randomized, siteless, open-label, cross-over study to evaluate an e-health-based strategy for detection of AF in a real-world setting. 67,488 policyholders of a large German health insurance company (Versicherungskammer Bayern, Germany) selected by age ≥ 50 years and a CHA2DS2-VASc score ≥ 1 (females ≥2) are invited to participate. Subjects with known AF or on treatment with oral anticoagulation are excluded. After obtaining electronic informed consent, at least 4,400 participants will be randomly assigned to an e-health-based screening strategy or routine symptom-based screening. The e-health-based strategy consists of repetitive one-minute photoplethysmographic (PPG) pulse wave assessments using a certified smartphone app (Preventicus Heartbeats, Preventicus, Jena, Germany), followed by a confirmatory 14-day ECG patch (CardioMem CM 100 XT, Getemed, Teltow, Germany) in case of abnormal findings. After 6 months, participants are crossed over to the other study arm. Primary endpoint is the incidence of newly diagnosed AF leading to oral anticoagulation indicated by an independent physician. Clinical follow-up will be at least 12 months. In both groups, follow-up is performed by 4-week app-based questionnaires, personal contact in case of abnormal findings, and matching with claim-based insurance data and medical reports. At time of writing enrollment is completed. First results are expected to be available in mid-2021.
Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.

Entities:  

Year:  2021        PMID: 34252387     DOI: 10.1016/j.ahj.2021.06.008

Source DB:  PubMed          Journal:  Am Heart J        ISSN: 0002-8703            Impact factor:   4.749


  2 in total

Review 1.  Current Advancement in Diagnosing Atrial Fibrillation by Utilizing Wearable Devices and Artificial Intelligence: A Review Study.

Authors:  Yu-Chiang Wang; Xiaobo Xu; Adrija Hajra; Samuel Apple; Amrin Kharawala; Gustavo Duarte; Wasla Liaqat; Yiwen Fu; Weijia Li; Yiyun Chen; Robert T Faillace
Journal:  Diagnostics (Basel)       Date:  2022-03-11

Review 2.  A Powerful Paradigm for Cardiovascular Risk Stratification Using Multiclass, Multi-Label, and Ensemble-Based Machine Learning Paradigms: A Narrative Review.

Authors:  Jasjit S Suri; Mrinalini Bhagawati; Sudip Paul; Athanasios D Protogerou; Petros P Sfikakis; George D Kitas; Narendra N Khanna; Zoltan Ruzsa; Aditya M Sharma; Sanjay Saxena; Gavino Faa; John R Laird; Amer M Johri; Manudeep K Kalra; Kosmas I Paraskevas; Luca Saba
Journal:  Diagnostics (Basel)       Date:  2022-03-16
  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.