title: Simulation of Daily Snapshot Rhythm Monitoring to Identify Atrial Fibrillation in Continuously Monitored Patients with Stroke Risk Factors.
abstract: BACKGROUND
  New technologies are diffusing into medical practice swiftly. Hand-held devices such as smartphones can record short-duration (e.g., 1-minute) ECGs, but their effectiveness in identifying patients with paroxysmal atrial fibrillation (AF) is unknown.
  
  
  METHODS
  We used data from the TRENDS study, which included 370 patients (mean age 71 years, 71% men, CHADS2 score≥1 point: mean 2.3 points) who had no documentation of atrial tachycardia (AT)/AF or antiarrhythmic or anticoagulant drug use at baseline. All were subsequently newly diagnosed with AT/AF by a cardiac implantable electronic device (CIED) over one year of follow-up. Using a computer simulation approach (5,000 repetitions), we estimated the detection rate for paroxysmal AT/AF via daily snapshot ECG monitoring over various periods, with the probability of detection equal to the percent AT/AF burden on each day.
  
  
  RESULTS
  The estimated AT/AF detection rates with snapshot monitoring periods of 14, 28, 56, 112, and 365 days were 10%, 15%, 21%, 28%, and 50% respectively. The detection rate over 365 days of monitoring was higher in those with CHADS2 scores ≥2 than in those with CHADS2 scores of 1 (53% vs. 38%), and was higher in those with AT/AF burden ≥0.044 hours/day compared to those with AT/AF burden <0.044 hours/day (91% vs. 14%; both P<0.05).
  
  
  CONCLUSIONS
  Daily snapshot ECG monitoring over 365 days detects half of patients who developed AT/AF as detected by CIED, and shorter intervals of monitoring detected fewer AT/AF patients. The detection rate was associated with individual CHADS2 score and AT/AF burden.
  
  
  BACKGROUND
  ClinicalTrials.gov NCT00279981.
authors.affiliation: Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America.
authors.forename: Yuichiro
authors.initials: Y
authors.lastname: Yano
authors.affiliation: Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America.
authors.forename: Philip
authors.initials: P
authors.lastname: Greenland
authors.affiliation: Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America.
authors.forename: Donald M
authors.initials: DM
authors.lastname: Lloyd-Jones
authors.affiliation: Department of Medicine, Division of Cardiology, Richard M. Ross Heart Hospital, The Ohio State University Medical Center, Columbus, Ohio, United States of America.
authors.forename: Emile G
authors.initials: EG
authors.lastname: Daoud
authors.affiliation: Medtronic Inc., Minneapolis, Minnesota, United States of America.
authors.forename: Jodi L
authors.initials: JL
authors.lastname: Koehler
authors.affiliation: Medtronic Inc., Minneapolis, Minnesota, United States of America.
authors.forename: Paul D
authors.initials: PD
authors.lastname: Ziegler
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D001281:Atrial Fibrillation / Q000175:diagnosis* / Q000503:physiopathology; D040421:Cell Phone; D003198:Computer Simulation; D004562:Electrocardiography / Q000295:instrumentation* / Q000379:methods; D004567:Electrodes, Implanted; D005260:Female; D006333:Heart Failure / Q000503:physiopathology; D006801:Humans; D008297:Male; D008875:Middle Aged; D012307:Risk Factors; D020521:Stroke / Q000175:diagnosis* / Q000503:physiopathology; D013610:Tachycardia / Q000175:diagnosis* / Q000503:physiopathology