Cardiovascular Digital Health Journal · 2022 · 36 citations · 5 references
MedicineBiomedical EngineeringCardiologyClinical CardiologyElectrophysiologyAtrial FibrillationCardiac ArrhythmiasCardiac Signal ProcessingCardiovascular MonitoringElectrocardiogram InterpretationElectrocardiographyAtrial Fibrillation AssessmentAtrial Fibrillation DetectionAtrial Fibrillation InterpretationAtrial Fibrillation ScreeningClinical Relevance AssessmentHeart Rhythm AssessmentHeart Rhythm Evaluation
BackgroundThe Apple Watch (AW) is the first commercially available wearable device with built-in electrocardiogram (ECG) electrodes to perform a single-lead ECG to detect atrial fibrillation (AF).MethodsPatients with AF who were scheduled for electrical cardioversion (ECV) were included in this study. The AW ECGs were obtained pre-ECV and post-ECV. In case of an unclassified recording, the AW ECG was obtained up to 3 times. The 12-lead ECG was used as the reference standard. Sensitivity, specificity, and kappa coefficient were calculated.ResultsIn total, 74 patients were included. Mean age was 67.1 ± 12.3 years and 20.3% were female. In total 65 AF and 64 sinus rhythm measurements were obtained. The first measurement with the AW showed a sensitivity of 93.5% and specificity of 100% (κ = 0.94). A second measurement resulted in a sensitivity of 94.6% and specificity of 100% (κ = 0.95). A third measurement resulted in a sensitivity of 93% and a specificity of 96.5% (κ = 0.90). Adjudication of unclassified recordings by a physician reduced the total unclassified recordings from 27.9% to 1.6%, but also reduced the accuracy. The kappa coefficient for unclassified single-lead ECGs was 0.58.ConclusionThe single-lead ECG of the AW shows a high accuracy for identifying AF in a clinical setting. Repeating the recording once decreases the total of unclassified recordings; however, a third recording resulted in a lower accuracy and the occurrence of false-positive measurements. Unclassified results of the AW can be reduced by physicians’ interpretation of the single-lead ECG; however, the interrater agreement is only moderate. The Apple Watch (AW) is the first commercially available wearable device with built-in electrocardiogram (ECG) electrodes to perform a single-lead ECG to detect atrial fibrillation (AF). Patients with AF who were scheduled for electrical cardioversion (ECV) were included in this study. The AW ECGs were obtained pre-ECV and post-ECV. In case of an unclassified recording, the AW ECG was obtained up to 3 times. The 12-lead ECG was used as the reference standard. Sensitivity, specificity, and kappa coefficient were calculated. In total, 74 patients were included. Mean age was 67.1 ± 12.3 years and 20.3% were female. In total 65 AF and 64 sinus rhythm measurements were obtained. The first measurement with the AW showed a sensitivity of 93.5% and specificity of 100% (κ = 0.94). A second measurement resulted in a sensitivity of 94.6% and specificity of 100% (κ = 0.95). A third measurement resulted in a sensitivity of 93% and a specificity of 96.5% (κ = 0.90). Adjudication of unclassified recordings by a physician reduced the total unclassified recordings from 27.9% to 1.6%, but also reduced the accuracy. The kappa coefficient for unclassified single-lead ECGs was 0.58. The single-lead ECG of the AW shows a high accuracy for identifying AF in a clinical setting. Repeating the recording once decreases the total of unclassified recordings; however, a third recording resulted in a lower accuracy and the occurrence of false-positive measurements. Unclassified results of the AW can be reduced by physicians’ interpretation of the single-lead ECG; however, the interrater agreement is only moderate.
5
The Measurement of Observer Agreement for Categorical Data
J. Richard Landis, Gary G. Koch · Biometrics · 1977 · 78.9K citations · Full text
Subclinical Atrial Fibrillation and the Risk of Stroke
Jeff S. Healey, Stuart J. Connolly, Michael R. Gold et al. · New England Journal of Medicine · 2012 · 2.1K citations · Full text
C. Ranganathan, Vipanchi Katthula, Evangelos Moustakas · Journal of Medical Internet Research · 2020 · 273 citations · Full text