Key result
Updated smartwatch algorithm improves AF detection vs the existing version, reaching 90% sensitivity.
Why the study?
Given known limitations of the Apple Watch in correctly diagnosing AF, the authors aimed to apply a data science approach to develop an improved algorithm.
Does a new 'irregularly irregular' algorithm improve the accuracy of atrial fibrillation detection in patients undergoing smartwatch ECG recording compared to the existing Apple Watch algorithm?
Cross-Sectional (n=723)
Does a new 'irregularly irregular' algorithm improve the accuracy of atrial fibrillation detection in patients undergoing smartwatch ECG recording compared to the existing Apple Watch algorithm?
p-value: p=<0.01
A novel algorithm identifying regularity within irregular rhythms significantly improves the sensitivity and specificity of smartwatch ECGs for detecting atrial fibrillation.
Suggests algorithmic refinement may reduce false positives in wearable AF screening; leaves open whether this translates.
Smartwatches equipped with automatic atrial fibrillation (AF) detection through electrocardiogram (ECG) recording are increasingly prevalent. We have recently reported the limitations of the Apple Watch (AW) in correctly diagnosing AF. In this study, we aim to apply a data science approach to a large dataset of smartwatch ECGs in order to deliver an improved algorithm. We included 723 patients (579 patients for algorithm development and 144 patients for validation) who underwent ECG recording with an AW and a 12-lead ECG (21% had AF and 24% had no ECG abnormalities). Similar to the existing algorithm, we first screened for AF by detecting irregularities in ventricular intervals. However, as opposed to the existing algorithm, we included all ECGs (not applying quality or heart rate exclusion criteria) but we excluded ECGs in which we identified regular patterns within the irregular rhythms by screening for interval clusters. This “irregularly irregular” approach resulted in a significant improvement in accuracy compared to the existing AW algorithm (sensitivity of 90% versus 83%, specificity of 92% versus 79%, p < 0.01). Identifying regularity within irregular rhythms is an accurate yet inclusive method to detect AF using a smartwatch ECG.
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Velraeds et al. (2023) conducted a cross-sectional in Atrial fibrillation (n=723). Novel irregularly irregular algorithm vs. Existing Apple Watch algorithm was evaluated on Diagnostic accuracy (sensitivity and specificity) for atrial fibrillation (p=<0.01). An updated smartwatch algorithm identifying regularity within irregular rhythms improved AF detection versus the existing algorithm (sensitivity 90% vs 83%, specificity 92% vs 79%, P<0.01).
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