“Previous studies have validated the Apple Watch’s accuracy in diagnosing atrial fibrillation in a limited number of patients with similar clinical profiles,” said lead investigator Marc Strik, MD, PhD, LIRYC Institute. , Bordeaux University Hospital, Bordeaux, France explained. “We tested the accuracy of the Apple Watch ECG app for detecting atrial fibrillation in patients with various coexisting electrocardiogram abnormalities.”

The study included 734 consecutively hospitalized patients. Each patient received her 12-lead ECG, followed immediately by her Apple Watch recording for 30 seconds. The smartwatch’s automatic single-lead ECG AF detection was classified as ‘no signs of atrial fibrillation’, ‘atrial fibrillation’, or ‘conclusive reading’. The smartwatch recordings were handed over to an electrophysiologist, who performed blinded interpretation and assigned a diagnosis of ‘AF’, ‘absence of AF’, or ‘diagnosis unknown’ to each trace. Another blinded electrophysiologist interpreted her 100 randomly selected traces to determine how much the observers agreed.

In about 1 in 5 patients, the smartwatch ECG was unable to generate an automatic diagnosis. Patients with premature atrial and ventricular contractions (PAC/PVC), sinus node dysfunction, and second- or third-degree AV block were at increased risk of false-positive automated AF detection. Among patients with atrial fibrillation, there was a high risk of false-negative follow-up (absence of atrial fibrillation) in patients with ventricular conduction abnormalities (interventricular conduction delay) or rhythms controlled by implanted pacemakers.

Cardiac electrophysiologists had a high level of agreement in distinguishing between atrial fibrillation and non-fibrillation. The smartphone app correctly identified 78% of patients with AF and 81% of patients without AF. Electrophysiologists identified 97% of patients with AF and 89% without.

Patients using PVC were three times more likely to have false AF detections on smartwatch ECGs, making it very difficult to distinguish between patients with atrial tachycardia (AT) and atrial flutter (AFL) .

“These observations are not surprising because smartwatch auto-detection algorithms are based solely on cycle variability,” Dr. Strik points out, noting that PVC causes both short and long cycles, and cycle variability. said to increase fertility. “Ideally, algorithms would better distinguish between PVC and AF. It may improve smartwatch AF detection accuracy.”

In an accompanying editorial, Andrés F. Miranda-Arboleda (MD) and Adrian Baranchuk (MD), Department of Cardiology, Kingston Health Science Center, Kingston, ON, Canada, say that this is the first “real” study. increase. Using Apple Watch as a diagnostic tool for AF.

“This is very important because we have learned that the performance of the Apple Watch in diagnosing atrial fibrillation is greatly affected by the presence of underlying ECG abnormalities. Smartwatch algorithms for detecting fibrillation aren’t smart enough for cardiovascular conditions yet. But they may soon be.

“As the use of smartwatches in healthcare increases, we will learn which medical conditions and ECG abnormalities may affect and alter the detection of atrial fibrillation by smartwatches in order to optimize patient care. is important,” said Dr. Strik. “Smartwatch detection of atrial fibrillation has great potential, but is more difficult in patients with pre-existing heart disease.”

Story source:

Materials provided Elsevier. Note: Content may be edited for style and length.