Key result
The external loop recorder's automated atrial fibrillation detection algorithm demonstrated high sensitivity (93%) but low specificity (51%) and positive predictive value (5%), requiring extensive manual verification.
Why the study?
Does an external transtelephonic loop recorder with automated detection accurately identify paroxysmal atrial fibrillation in patients with ischemic cerebrovascular accident?
Cohort (n=153)
Single-blind
No
Does an external transtelephonic loop recorder with automated detection accurately identify paroxysmal atrial fibrillation in patients with ischemic cerebrovascular accident?
While an external loop recorder with automated AF detection is highly sensitive for identifying atrial fibrillation in stroke patients, its low specificity and positive predictive value necessitate substantial manual verification of recordings.
Requires manual verification due to low specificity in post-stroke screening; leaves open whether refined algorithms can improve efficiency.
BACKGROUND: Although atrial fibrillation (AF) is the most commonly encountered arrhythmia, some of the properties make its detection challenging. In daily practice, underdiagnosis can lead to less effective treatment in prevention of stroke. Based on data from studies on treatment of AF, more intensive follow-up strategies, including 7-day Holter recording, 30-day event recording, and even implantable cardiac monitoring devices, are suggested. The study purpose is to evaluate the performance of a continuous single-channel loop recorder with automatic AF detection and transtelephonic electrocardiogram (ECG) transmission capabilities. METHODS AND RESULTS: A consecutive cohort of 153 patients admitted to the stroke unit with a presumptive diagnosis of ischemic cerebrovascular accident was screened for AF. Twenty-four-hour rhythm observation was performed using a single-channel external loop recorder (ELR) configured for automated AF detection. A total of 45 patients with a known history of AF, AF on the admission ECG, or incomplete registrations were excluded. Extensive additional frequency-based settings were used to establish a reference registration. In total, 2923 recordings were transmitted. We evaluated all events, of which 1190 were designated by the device as AF. The sensitivity, specificity, PPV, and NPV for identifying AF using the ELR were, respectively, 93%, 51%, 5%, and 99%. CONCLUSIONS: In this ELR validation study, the dedicated AF detection algorithm showed to be highly sensitive but not specific for AF. Applicability of an ELR might be limited for efficacious detection of AF, as manual verification is mandatory for a vast amount of recordings.
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Velthuis et al. (2013) conducted a cohort in Acute ischemic stroke (screening for atrial fibrillation) (n=153). External loop recorder (Vitaphone 3100 BT) vs. Manual verification was evaluated on Diagnostic accuracy (sensitivity, specificity, PPV, NPV) for automated detection of atrial fibrillation. The external loop recorder's automated atrial fibrillation detection algorithm demonstrated high sensitivity (93%) but low specificity (51%) and positive predictive value (5%), requiring extensive manual verification.
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