Validating QT-Interval Measurement Using the Apple Watch ECG to Enable Remote Monitoring During the COVID-19 Pandemic
Notice bibliographique
Résumé
◼ electrocardiography ◼ long QT syndrome ◼ remote consultation ◼ telemedicine S creening and monitoring for QT prolongation when certain medications are initiated are routinely performed to avoid arrhythmic complications.However, the novel coronavirus disease 2019 (COVID-19) pandemic and its proposed treatments-including hydroxychloroquine and azithromycin, which are known to prolong the QT interval 1 -raise logistical and safety concerns with established QT monitoring strategies.Recently, the American College of Cardiology made recommendations for QT monitoring in outpatients with COVID-19 on hydroxychloroquine/azithromycin, suggesting that the use of direct-to-consumer mobile devices such as the Apple Watch 1-lead ECG could be considered in cases of resource constraints or quarantines. 2The Apple Watch ECG is cleared by the US Food and Drug Administration for detecting atrial fibrillation but has not been studied for QT monitoring.Lead I (the lead recorded by the Apple Watch) may be suboptimal for measuring this interval; however, other leads can be reproduced by placing the smartwatch on the left ankle or chest. 3We therefore sought to validate the use of the Apple Watch for QT measurement, including using nonstandard smartwatch positions, in an unselected outpatient population.Between December 2019 and January 2020, 100 consecutive patients in sinus rhythm were enrolled from outpatient or emergency departments.The study was approved by our institutional review committee, and the subjects gave informed consent.Standard 12-lead ECGs were performed, followed by smartwatch electrocardiographic recordings using the Apple Watch Series 4 (Apple Inc, Cupertino, CA).After a brief demonstration, patients recorded 30-second Apple Watch electrocardiographic equivalents of lead I (AW-I; watch on left wrist), lead II (AW-II; watch on left ankle), and a simulated lead V 6 (AW-LAT; watch on left lateral chest; Figure [A]).Using commercially available software (EP Calipers, EP studios Inc, Louisville, KY), a cardiologist measured 3 RR and QT intervals to calculate the corrected QT interval (QTc) using the Bazett formula (Figure [B]).A QTc >480 milliseconds was considered high risk.Agreement between the 12-lead and Apple Watch QTc measurements was calculated with the use of the median absolute error and Bland-Altman analyses.To measure interobserver variability, all AW-I QTc measurements were repeated by a second blinded cardiologist, and the intraclass correlation coefficient was calculated on the basis of a 2-way random absolute agreement model with single measurements.Agreement on whether a tracing was interpretable was assessed with the Cohen κ statistic.T-wave amplitude was measured to evaluate its association with QTc measurement accuracy.The mean age was 67±7 years; 59% were male; and 35% had diagnosed cardiac disease.Heart rates were similar on 12-lead and Apple Watch recordings (69±11 bpm versus 70±13 bpm, respectively; P=0.2, paired Student t test).QTc intervals ranged from 336 to 530 milliseconds on 12-lead ECGs.Eight patients were identified as high risk, all of whom were similarly identified by the smartwatch.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».