SmartWATCHes for Detection of Atrial Fibrillation
Condition: Atrial Fibrillation
MeSH terms: Atrial Fibrillation
Study type: Observational

Summary: 
      In this trial the Preventives Heartbeats algorithm will be tested in two wearable devices for
      its specificity and sensitivity to distinguish between AF and SR.
    

Detailed description: 
      Introduction

      Particularly in stroke prevention, the detection of atrial fibrillation is of pivotal
      importance. Recent studies have impressively confirmed the merits of long-term monitoring.
      Nevertheless, and despite very mature technology, the currently available methods like
      long-term ECG with conductive adhesive electrodes or implantable loop recorders are burdened
      with disadvantages in that they are inconvenient, costly and/or invasive. A predecessor study
      gave us the possibility to test an app that employs photoplethysmographic signals on a
      smartphone camera-similar to pulse oximetry technology-to distinguish between atrial
      fibrillation and sinus rhythm. That retrospective study achieved a sensitivity and
      specificity of 95% (Krivoshei et al., Europace 2016). The prospective follow-up study DETECT
      AF pro is currently being conducted in cooperation with Prof. Dörr in Greifswald, Germany.

      Our research group has been involved with testing the app's quality in clinical settings
      before implementing it for use. This procedure differs markedly from the otherwise common
      practice of marketing qualitatively inferior "health apps" without any clinical testing.
      There is no app equipped with comparable technology worldwide. The WATCH AF study is the
      first study on sophisticated rhythm analysis using a smartwatch worldwide.

      Study design

      Prospective, blinded, multicenter study.

      The blinded data will be evaluated and monitored externally.

      An interim analysis to assess signal quality will be performed after inclusion of 50% of the
      planned subjects. If there are <10% non-evaluable signals, the number of subjects will be
      adjusted accordingly. The aim is to enroll 600 evaluable subjects.

      Methods

      The subjects will be asked to place a smartwatch on each arm to allow their pulse curve to be
      recorded for five minutes. At the same time, an ambulatory ECG system will take a synchronous
      ECG for reference. The ECG system employed is equipped with a validated automatic atrial
      fibrillation detector, used as a reference for assignment to groups. Data will be collected
      on person-related information, comorbidities and medication (estimated overall
      duration/patient <20 min). No follow-up is planned. Analogous to predecessor studies, the
      pulse wave curve data are coded with the patient's identification number (ID) and externally
      analyzed at Preventicus; based on the results, the patients will be assigned to SR/AF groups.
      After evaluation of all files, the grouped results will be aggregated, unblinded and
      evaluated under monitoring.

      Primary target parameters are the app's sensitivity and specificity in correctly detecting
      atrial fibrillation compared to an automatically interpreted ECG.

      Secondary target parameters include the proportion of non-evaluable recordings in the overall
      study, differences between the two smartwatches and the app's sensitivity and specificity in
      patients with >2% premature beats.
    

Intervention name(s): Five minutes pulse wave recording
Intervention type(s): Device
Intervention description(s): Patients will wear a smart watch and a wristband each at one arm. A standard egg (lead I) and pig signals from the wearables will be recorded for five minutes.
Observational model: Case-Control
Time perspective: Prospective
Sampling method: Probability Sample
Study population: 
        Patients at the University Hospital Basel
      
Keywords: Atrial fibrillation; cardiovascular App; Rhythm monitoring; Stroke prevention