Atrial Fibrillation Detection With an Analog Smartwatch: Prospective Clinical Study and Algorithm Validation.

ECG algorithm atrial fibrillation automatic detection cardiac cardiology cardiovascular diagnosis digital health electrocardiogram heart disease heart failure mHealth mobile health morbidity physician sensor smart technology smartwatch wearable

Journal

JMIR formative research
ISSN: 2561-326X
Titre abrégé: JMIR Form Res
Pays: Canada
ID NLM: 101726394

Informations de publication

Date de publication:
04 Nov 2022
Historique:
received: 14 02 2022
accepted: 28 04 2022
revised: 26 04 2022
pubmed: 29 4 2022
medline: 29 4 2022
entrez: 28 4 2022
Statut: epublish

Résumé

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. We aimed to validate an algorithm for the automatic detection of atrial fibrillation from a single-lead electrocardiogram taken with a smartwatch. 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). 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. 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. ClinicalTrials.gov NCT04351386; http://clinicaltrials.gov/ct2/show/NCT04351386.

Sections du résumé

BACKGROUND 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 OBJECTIVE
We aimed to validate an algorithm for the automatic detection of atrial fibrillation from a single-lead electrocardiogram taken with a smartwatch.
METHODS 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 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 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 BACKGROUND
ClinicalTrials.gov NCT04351386; http://clinicaltrials.gov/ct2/show/NCT04351386.

Identifiants

pubmed: 35481559
pii: v6i11e37280
doi: 10.2196/37280
pmc: PMC9675016
doi:

Banques de données

ClinicalTrials.gov
['NCT04351386']

Types de publication

Journal Article

Langues

eng

Pagination

e37280

Informations de copyright

©David Campo, Valery Elie, Tristan de Gallard, Pierre Bartet, Tristan Morichau-Beauchant, Nicolas Genain, Antoine Fayol, David Fouassier, Adrien Pasteur-Rousseau, Etienne Puymirat, Julien Nahum. Originally published in JMIR Formative Research (https://formative.jmir.org), 04.11.2022.

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Auteurs

David Campo (D)

Withings, Issy Les Moulineaux, France.

Valery Elie (V)

Withings, Issy Les Moulineaux, France.

Tristan de Gallard (T)

Withings, Issy Les Moulineaux, France.

Pierre Bartet (P)

Withings, Issy Les Moulineaux, France.

Tristan Morichau-Beauchant (T)

Intensive Care Unit, Centre Cardiologique du Nord, Sainte-Denis, France.

Nicolas Genain (N)

Withings, Issy Les Moulineaux, France.

Antoine Fayol (A)

Cardiology Intensive Care Unit, Hopital Europeen Georges Pompidou, Paris, France.

David Fouassier (D)

Institut Coeur Paris Centre Turin, Paris, France.

Adrien Pasteur-Rousseau (A)

Institut Coeur Paris Centre Floréal, Bagnolet, France.

Etienne Puymirat (E)

Cardiology Intensive Care Unit, Hopital Europeen Georges Pompidou, Paris, France.

Julien Nahum (J)

Intensive Care Unit, Centre Cardiologique du Nord, Sainte-Denis, France.

Classifications MeSH