Observational studyInternational journal of cardiology2021
Smartwatch Electrocardiogram and Artificial Intelligence for Assessing Cardiac-Rhythm Safety of Drug Therapy in the COVID-19 Pandemic. The QT-logs study.
Observational study in International journal of cardiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
38 citing papers in PubMed, 2 syntheses or guidelines pooled it, 63 citations in OpenAlex.
- Artificial intelligence in cardiovascular diagnostics: a systematic review and descriptive analysis of clinical applications and diagnostic performance.BMC cardiovascular disorders · 2025Pooled it
- Applicability of Machine Learning Algorithms in Diagnosis of Atrial Fibrillation and LQTS by Electrocardiogram Interpretation: A Systematic Review.Arquivos brasileiros de cardiologia · 2025Pooled it
- Feasibility of Wearable Devices for Motivating Post-Stroke Patients.Sensors (Basel, Switzerland) · 2025Trial
- Contemporary wearable and handheld technology for the diagnosis of cardiac arrhythmias in Singapore.Singapore medical journal · 2026Review
- [Artificial intelligence and cardiology in Latin America: a 5-year bibliometric analysis of scientific research].Archivos de cardiologia de Mexico · 2025Article
- Artificial Intelligence and ECG: A New Frontier in Cardiac Diagnostics and Prevention.Biomedicines · 2025Review
- Congenital Long QT Syndrome: A Focus on Risk Stratification and Management.Reviews in cardiovascular medicine · 2025Review
- Are Wearable ECG Devices Ready for Hospital at Home Application?Sensors (Basel, Switzerland) · 2025Review
- Role of Artificial Intelligence in Nanomedicine and Organ-specific Therapy: An Updated Review.Current drug targets · 2025Review
- Applicability of Machine Learning Algorithms in the Diagnosis of Arrhythmias - How Long Until The Machine Starts Teaching Us?Arquivos brasileiros de cardiologia · 2025Article
- Rhythm-Ready: Harnessing Smart Devices to Detect and Manage Arrhythmias.Current cardiology reports · 2024Review
- Can wearables outscore general practitioners? Congenital long QT syndrome diagnosis initiated by a smartwatch.HeartRhythm case reports · 2024Article
- Artificial intelligence-enhanced patient evaluation: bridging art and science.European heart journal · 2024Review
- Validation of smartwatch electrocardiogram intervals in children compared to standard 12 lead electrocardiograms.European journal of pediatrics · 2024Article
- Artificial intelligence-based electrocardiogram analysis improves atrial arrhythmia detection from a smartwatch electrocardiogram.European heart journal. Digital health · 2024Article
- Prospective Human Validation of Artificial Intelligence Interventions in Cardiology: A Scoping Review.JACC. Advances · 2024Article
- Transforming Cardiovascular Care With Artificial Intelligence: From Discovery to Practice: JACC State-of-the-Art Review.Journal of the American College of Cardiology · 2024Review
- Data Interoperability for Ambulatory Monitoring of Cardiovascular Disease: A Scientific Statement From the American Heart Association.Circulation. Genomic and precision medicine · 2024Article
- Detecting QT prolongation from a single-lead ECG with deep learning.PLOS digital health · 2024Article
- Use of Artificial Intelligence in Improving Outcomes in Heart Disease: A Scientific Statement From the American Heart Association.Circulation · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors at 6 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundQTc interval monitoring, for the prevention of drug-induced arrhythmias is necessary, especially in the context of coronavirus disease 2019 (COVID-19). For the provision of widespread use, surrogates for 12‑lead ECG QTc assessment may be useful. This prospective observational study compared QTc duration assessed by artificial intelligence (AI-QTc) (Cardiologs®, Paris, France) on smartwatch single‑lead electrocardiograms (SW-ECGs) with those measured on 12‑lead ECGs, in patients with early stage COVID-19 treated with a hydroxychloroquine-azithromycin regimen.
methodsConsecutive patients with COVID-19 who needed hydroxychloroquine-azithromycin therapy, received a smartwatch (Withings Move ECG®, Withings, France). At baseline, day-6 and day-10, a 12‑lead ECG was recorded, and a SW-ECG was transmitted thereafter. Throughout the drug regimen, a SW-ECG was transmitted every morning at rest. Agreement between manual QTc measurement on a 12‑lead ECG and AI-QTc on the corresponding SW-ECG was assessed by the Bland-Altman method.
results85 patients (30 men, mean age 38.3 ± 12.2 years) were included in the study. Fair agreement between manual and AI-QTc values was observed, particularly at day-10, where the delay between the 12‑lead ECG and the SW-ECG was the shortest (-2.6 ± 64.7 min): 407 ± 26 ms on the 12‑lead ECG vs 407 ± 22 ms on SW-ECG, bias -1 ms, limits of agreement -46 ms to +45 ms; the difference between the two measures was <50 ms in 98.2% of patients.
conclusionIn real-world epidemic conditions, AI-QTc duration measured by SW-ECG is in fair agreement with manual measurements on 12‑lead ECGs. Following further validation, AI-assisted SW-ECGs may be suitable for QTc interval monitoring. REGISTRATION: ClinicalTrial.govNCT04371744.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.