Evidence map›Paper›PMID 35811720›Full record

ArticleFrontiers in cardiovascular medicine2022

Clinical Validation of Automated Corrected QT-Interval Measurements From a Single Lead Electrocardiogram Using a Novel Smartwatch.

Diego Mannhart, Elisa Hennings, Mirko Lischer, Claudius Vernier, Jeanne Du Fay de Lavallaz, Sven Knecht, Beat Schaer, Stefan Osswald, Michael Kühne, Christian Sticherling and 1 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in cardiovascular medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
2.5field-weighted citation impact, top 10% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 16 citations in OpenAlex.

  1. Review
  2. Review
  3. Article
  4. Wearable health monitoring: wave of the future or waste of time?Global cardiology science & practice · 2024
    Review
  5. Article
  6. Smart devices to measure and monitor QT intervals.Frontiers in cardiovascular medicine · 2023
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors at 1 institution in 1 country.

Diego MannhartDepartment of Cardiology, University Hospital Basel, Basel, Switzerland.
Elisa HenningsDepartment of Cardiology, University Hospital Basel, Basel, Switzerland.
Mirko LischerDepartment of Cardiology, University Hospital Basel, Basel, Switzerland.
Claudius VernierDepartment of Cardiology, University Hospital Basel, Basel, Switzerland.
Jeanne Du Fay de LavallazDepartment of Cardiology, University Hospital Basel, Basel, Switzerland.
Sven KnechtDepartment of Cardiology, University Hospital Basel, Basel, Switzerland.
Beat SchaerDepartment of Cardiology, University Hospital Basel, Basel, Switzerland.
Stefan OsswaldDepartment of Cardiology, University Hospital Basel, Basel, Switzerland.
Michael KühneDepartment of Cardiology, University Hospital Basel, Basel, Switzerland.
Christian SticherlingDepartment of Cardiology, University Hospital Basel, Basel, Switzerland.
Patrick BadertscherDepartment of Cardiology, University Hospital Basel, Basel, Switzerland.
University Hospital of Basel · CH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: The Withings Scanwatch (Withings SA, Issy les Moulineaux, France) offers automated analysis of the QTc. We aimed to compare automated QTc-measurements using a single lead ECG of a novel smartwatch (Withings Scanwatch, SW-ECG) with manual-measured QTc from a nearly simultaneously recorded 12-lead ECG. Methods: We enrolled consecutive patients referred to a tertiary hospital for cardiac workup in a prospective, observational study. The QT-interval of the 12-lead ECG was manually interpreted by two blinded, independent cardiologists through the tangent-method. Bazett's formula was used to calculate QTc. Results were compared using the Bland-Altman method. Results: A total of 317 patients (48% female, mean age 63 ± 17 years) were enrolled. HR-, QRS-, and QT-intervals were automatically calculated by the SW in 295 (93%), 249 (79%), and 177 patients (56%), respectively. Diagnostic accuracy of SW-ECG for detection of QTc-intervals ≥ 460 ms (women) and ≥ 440 ms (men) as quantified by the area under the curve was 0.91 and 0.89. The Bland-Altman analysis resulted in a bias of 6.6 ms [95% limit of agreement (LoA) -59 to 72 ms] comparing automated QTc-measurements (SW-ECG) with manual QTc-measurement (12-lead ECG). In 12 patients (6.9%) the difference between the two measurements was greater than the LoA. Conclusion: In this clinical validation of a direct-to-consumer smartwatch we found fair to good agreement between automated-SW-ECG QTc-measurements and manual 12-lead-QTc measurements. The SW-ECG was able to automatically calculate QTc-intervals in one half of all assessed patients. Our work shows, that the automated algorithm of the SW-ECG needs improvement to be useful in a clinical setting.

Indexed as

artificial intelligencedigital healthintelligent ECGQTcremote patient monitoring (RPM)single-lead ECGsmartwatch

Identifiers

PMID35811720
PMCPMC9259864
OpenAlexW4283313575

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.