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Assessing the accuracy of an automated atrial fibrillation detection algorithm using smartphone technology: The iREAD Study
Abstract
Background The Kardia Mobile Cardiac Monitor (KMCM) detects atrial fibrillation (AF) via a handheld cardiac rhythm recorder and AF detection algorithm. The algorithm operates within predefined parameters to provide a "normal" or "possible atrial fibrillation detected" interpretation; outside of these parameters, an "unclassified" rhythm is reported. The system has been increasingly used, but its performance has not been independently tested.Objective The objective of this study was to evaluate whether the KMCM system can accurately detect AF.Methods A single-center, adjudicator-blinded case series of 52 consecutive patients with AF admitted for antiarrhythmic drug initiation were enrolled. Serial 12-lead electrocardiograms (ECGs) and nearly simultaneously acquired KMCM recordings were obtained.Results There were 225 nearly simultaneously acquired KMCM and ECG recordings across 52 enrolled patients (mean age 68 years; 67% male). After exclusion of unclassified recordings, the KMCM automated algorithm interpretation had 96.6% sensitivity and 94.1% specificity for AF detection as compared with physician-interpreted ECGs, with a κ coefficient of 0.89. Physician-interpreted KMCM recordings had 100% sensitivity and 89.2% specificity for AF detection as compared with physician-interpreted ECGs, with a κ coefficient of 0.85. Sixty-two recordings (27.6%) were unclassified by the KMCM algorithm. In these instances, physician interpretation of KMCM recordings had 100% sensitivity and 79.5% specificity for AF detection as compared with 12-lead ECG interpretation, with a κ coefficient of 0.71.Conclusion The KMCM system provides sensitive and specific AF detection relative to 12-lead ECGs when an automated interpretation is provided. Direct physician review of KMCM recordings can enhance diagnostic yield, especially for unclassified recordings.
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“the automatic diagnostic algorithm has excellent sensitivity (96.6%) and specificity (94%) for correctly interpreting AF versus normal sinus rhythm [15].”
From Multi-centre Randomised Controlled Trial of a Smartphone-based Event Recorder Alongside Standard Care Versus Standard Care for Patients Presenting to the Emergency Department with Palpitations and Pre-syncope: The IPED (Investigation of Palpitations in the ED) study (2019) · cites this paper as [15]
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A Smartphone App for Real-time Heart Rate Computation from Streaming ECG/EKG data
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“[27] The REHEARSE‑AF study showed the feasibility of using the KardiaMobile ® heart monitor in AF screening for at‑risk patients above 65 years of age over 12 months, with the potential to prevent ischaemic strokes.”
Kumar, S., Turnbull, S., Garikapati, K., Bennett, R. G., Campbell, T. G., Kotake, Y., Silva, K. D., Mahajan, R., Marschner, S. L., & Chow, C. K. (2023) · doi:10.22541/au.169581253.32737151/v1
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Can eHealth programs for cardiac arrhythmias be scaled-up by using the KardiaMobile algorithm?
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What Does Atrial Fibrillation Mean in Patients With Cardiac Implantable Electronic Devices?
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Behandlung von Vorhofflimmern in der Hausarztpraxis
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Patient Driven EKG Device Performance in Adults with Fontan Palliation
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“%) in this study was higher compared to other studies with non-CHD patients [5, 8, 13, 26, 27, 29].”
Rhythm-Ready: Harnessing Smart Devices to Detect and Manage Arrhythmias
Hsieh, P. N., & Singh, J. P. (2024) · Current Cardiology Reports · doi:10.1007/s11886-024-02135-1
“The KardiaMobile, which pairs with a smartphone to generate a single lead ECG, demonstrates sensitivity and specificity of 96.6% and 94.1% for AF detection compared with physician-interpreted ECGs (κ coefficient 0.89) [22].”
InfluentialSingle-Lead Mobile ECGs in Patients With Cardiovascular Disease
Steinberg, B. A. (2025) · JACC. Clinical electrophysiology · doi:10.1016/j.jacep.2025.05.026
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“35 –37 Despite having a significant proportion of asymptomatic patients (38.6”
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От первого электрокардиографа до смарт-технологий для диагностики аритмий у детей: обзор литературы
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“These findings differ from diagnostic validation studies conducted under controlled conditions but should not be interpreted as formal estimates of sensitivity or specificity because a universal reference-standard ECG was not applied [10,17,18].”
Metadata
- Authors
- William, A. D., Kanbour, M., Callahan, T., Bhargava, M., Varma, N., Rickard, J., Saliba, W., Wolski, K., Hussein, A., Lindsay, B. D., Wazni, O. M., & Tarakji, K. G.
- Affiliations
- Cleveland Clinic (US)
- Marshall University (US)
- Corresponding author
- Tarakji, Khaldoun G.
- ORCID
- Venue
- Heart Rhythm
- Publisher
- Elsevier BV
- Published
- 2018-10
- Volume
- 15
- Issue
- 10
- Pages
- 1561-1565
- ISSN
- 1547-5271 (ISSN-L)
- OpenAlex
- W2888509599
- Cited by
- 154
- References
- 14
- Citations by year
- 2026: 5 · 2025: 7 · 2024: 10 · 2023: 32 · 2022: 19 · 2021: 30 · 2020: 25 · 2019: 23 · 2018: 2
- FWCI
- 15.07
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Heart Rhythm (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Atrial Fibrillation Management and Outcomes
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Keywords
- atrial fibrillation, diagnostic accuracy, sensitivity and specificity, 12-lead ECG
- MeSH terms
- Smartphone, Adult, Aged, Aged, 80 and over, Algorithms, Atrial Fibrillation, Electrocardiography, Female, Heart Conduction System, Humans, Male, Middle Aged, Predictive Value of Tests, Signal Processing, Computer-Assisted, Telemetry, Reproducibility of Results, Single-Blind Method, Mobile Applications
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