Evidence map›Paper›PMID 36169999›Full record

ArticleJMIR formative research2022

Comparison Between QT and Corrected QT Interval Assessment by an Apple Watch With the AccurBeat Platform and by a 12‑Lead Electrocardiogram With Manual Annotation: Prospective Observational Study.

Sara Chokshi, Gulzhan Tologonova, Rose Calixte, Vandana Yadav, Naveed Razvi, Jason Lazar, Stan Kachnowski

Abstract read
In one paragraph

Article in JMIR formative research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
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

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. 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

7 authors.

Sara ChokshiHealthcare Innovation and Technology Lab, New York, NY, United States.ORCID https://orcid.org/0000-0002-3952-6654
Gulzhan TologonovaDivision of Cardiovascular Medicine, State University of New York Downstate Medical Center, New York, NY, United States.ORCID https://orcid.org/0000-0002-0169-9525
Rose CalixteDepartment of Epidemiology and Biostatistics, State University of New York Downstate Health Sciences University, New York, NY, United States.ORCID https://orcid.org/0000-0002-2020-5906
Vandana YadavHealthcare Innovation and Technology Lab, New York, NY, United States.ORCID https://orcid.org/0000-0002-0031-3774
Naveed RazviDepartment of Cardiology, Ipswich Hospital, Ipswich, United Kingdom.ORCID https://orcid.org/0000-0003-4154-834X
Jason LazarDivision of Cardiovascular Medicine, State University of New York Downstate Medical Center, New York, NY, United States.ORCID https://orcid.org/0000-0002-8548-7932
Stan KachnowskiHealthcare Innovation and Technology Lab, New York, NY, United States.ORCID https://orcid.org/0000-0002-2705-0513

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAbnormal prolongation or shortening of the QT interval is associated with increased risk for ventricular arrhythmias and sudden cardiac death. For continuous monitoring, widespread use, and prevention of cardiac events, advanced wearable technologies are emerging as promising surrogates for conventional 12‑lead electrocardiogram (ECG) QT interval assessment. Previous studies have shown a good agreement between QT and corrected QT (QTc) intervals measured on a smartwatch ECG and a 12-lead ECG, but the clinical accuracy of computerized algorithms for QT and QTc interval measurement from smartwatch ECGs is unclear.

objectiveThe prospective observational study compared the smartwatch-recorded QT and QTc assessed using AccurKardia's AccurBeat platform with the conventional 12‑lead ECG annotated manually by a cardiologist.

methodsECGs were collected from healthy participants (without any known cardiovascular disease) aged >22 years. Two consecutive 30-second ECG readings followed by (within 15 minutes) a 10-second standard 12-lead ECG were recorded for each participant. Characteristics of the participants were compared by sex using a 2-sample t test and Wilcoxon rank sum test. Statistical comparisons of heart rate (HR), QT interval, and QTc interval between the platform and the 12-lead ECG, ECG lead I, and ECG lead II were done using the Wilcoxon sign rank test. Linear regression was used to predict QTc and QT intervals from the ECG based on the platform's QTc/QT intervals with adjustment for age, sex, and difference in HR measurement. The Bland-Altman method was used to check agreement between various QT and QTc interval measurements.

resultsA total of 50 participants (32 female, mean age 46 years, SD 1 year) were included in the study. The result of the regression model using the platform measurements to predict the 12-lead ECG measurements indicated that, in univariate analysis, QT/QTc intervals from the platform significantly predicted QT/QTc intervals from the 12-lead ECG, ECG lead I, and ECG lead II, and this remained significant after adjustment for sex, age, and change in HR. The Bland-Altman plot results found that 96% of the average QTc interval measurements between the platform and QTc intervals from the 12-lead ECG were within the 95% confidence limit of the average difference between the two measurements, with a mean difference of -10.5 (95% limits of agreement -71.43, 50.43). A total of 94% of the average QT interval measurements between the platform and the 12-lead ECG were within the 95% CI of the average difference between the two measurements, with a mean difference of -6.3 (95% limits of agreement -54.54, 41.94).

conclusionsQT and QTc intervals obtained by a smartwatch coupled with the platform's assessment were comparable to those from a 12-lead ECG. Accordingly, with further refinements, remote monitoring using this technology holds promise for the identification of QT interval prolongation.

Indexed as

AI ECGAI wearablesartificial intelligence ECGbig datacardiovascular medicinedigital healthmachine learning

Identifiers

PMID36169999
PMCPMC9557757

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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.