Simulation of daily snapshot rhythm monitoring to identify atrial fibrillation in continuously monitored patients with stroke risk factors

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 score1 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 over365 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.