RESUMO
BACKGROUND: Atrial fibrillation affects approximately 4% of the world's population and is one of the major causes of stroke, heart failure, sudden death, and cardiovascular morbidity. It can be difficult to diagnose when asymptomatic or in the paroxysmal stage, and its natural history is not well understood. New wearables and connected devices offer an opportunity to improve on this situation. OBJECTIVE: We aimed to validate an algorithm for the automatic detection of atrial fibrillation from a single-lead electrocardiogram taken with a smartwatch. METHODS: Eligible patients were recruited from 4 sites in Paris, France. Electrocardiograms (12-lead reference and single lead) were captured simultaneously. The electrocardiograms were reviewed by independent, blinded board-certified cardiologists. The sensitivity and specificity of the algorithm to detect atrial fibrillation and normal sinus rhythm were calculated. The quality of single-lead electrocardiograms (visibility and polarity of waves, interval durations, heart rate) was assessed in comparison with the gold standard (12-lead electrocardiogram). RESULTS: A total of 262 patients (atrial fibrillation: n=100, age: mean 74.3 years, SD 12.3; normal sinus rhythm: n=113, age: 61.8 years, SD 14.3; other arrhythmia: n=45, 66.9 years, SD 15.2; unreadable electrocardiograms: n=4) were included in the final analysis; 6.9% (18/262) were classified as Noise by the algorithm. Excluding other arrhythmias and Noise, the sensitivity for atrial fibrillation detection was 0.963 (95% CI lower bound 0.894), and the specificity was 1.000 (95% CI lower bound 0.967). Visibility and polarity accuracies were similar (1-lead electrocardiogram: P waves: 96.9%, QRS complexes: 99.2%, T waves: 91.2%; 12-lead electrocardiogram: P waves: 100%, QRS complexes: 98.8%, T waves: 99.5%). P-wave visibility accuracy was 99% (99/100) for patients with atrial fibrillation and 95.7% (155/162) for patients with normal sinus rhythm, other arrhythmias, and unreadable electrocardiograms. The absolute values of the mean differences in PR duration and QRS width were <3 ms, and more than 97% were <40 ms. The mean difference between the heart rates from the 1-lead electrocardiogram calculated by the algorithm and those calculated by cardiologists was 0.55 bpm. CONCLUSIONS: The algorithm demonstrated great diagnostic performance for atrial fibrillation detection. The smartwatch's single-lead electrocardiogram also demonstrated good quality for physician use in daily routine care. TRIAL REGISTRATION: ClinicalTrials.gov NCT04351386; http://clinicaltrials.gov/ct2/show/NCT04351386.
RESUMO
BACKGROUND: Measurement of arterial stiffness should be more available. Our aim was to show that aortic pulse wave velocity can be reliably measured with a bathroom scale combining the principles of ballistocardiography (BCG) and impedance plethysmography on a single foot. METHOD: The calibration of the bathroom scale was conducted on a group of 106 individuals. The aortic pulse wave velocity was measured with the SphygmoCor in the supine position. Three consecutive measurements were then performed on the Withings scale in the standing position. This aorta-leg pulse transit time (alPTT) was then converted into a velocity with the additional input of the height of the person. Agreement between the SphygmoCor and the bathroom scale so calibrated is assessed on a separate group of 86 individuals, following the same protocol. RESULTS: The bias is 0.25 m·s-1 and the SE 1.39 m·s-1. This agreement with Sphygmocor is "acceptable" according to the ARTERY classification. The alPTT correlated well with cfPTT with (Spearman) R = 0.73 in pooled population (cal 0.79, val 0.66). The aorta-leg pulse wave velocity correlated with carotid-femoral pulse wave velocity with R = 0.76 (cal 0.80, val 0.70). CONCLUSION: Estimation of the aortic pulse wave velocity is feasible with a bathroom scale. Further investigations are needed to improve the repeatability of measurements and to test their accuracy in different populations and conditions.
