A mobile one-lead ECG device incorporated in a symptom-driven remote arrhythmia monitoring program. The first 5,982 Hartwacht ECGs

J. L. Selder, L. Breukel, S. Blok, A. C. van Rossum, I. I. Tulevski, C. P. Allaart

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Background: In recent years many mobile devices able to record health-related data in ambulatory patients have emerged. However, well-organised programs to incorporate these devices are sparse. Hartwacht Arrhythmia (HA) is such a program, focusing on remote arrhythmia detection using the AliveCor Kardia Mobile (KM) and its algorithm. Objectives: The aim of this study was to assess the benefit of the KM device and its algorithm in detecting cardiac arrhythmias in a real-world cohort of ambulatory patients. Methods: All KM ECGs recorded in the HA program between January 2017 and March 2018 were included. Classification by the KM algorithm was compared with that of the Hartwacht team led by a cardiologist. Statistical analyses were performed with respect to detection of sinus rhythm (SR), atrial fibrillation (AF) and other arrhythmias. Results: 5,982 KM ECGs were received from 233 patients (mean age 58 years, 52% male). The KM algorithm categorised 59% as SR, 22% as possible AF, 17% as unclassified and 2% as unreadable. According to the Hartwacht team, 498 (8%) ECGs were uninterpretable. Negative predictive value for detection of AF was 98%. However, positive predictive value as well as detection of other arrhythmias was poor. In 81% of the unclassified ECGs, the Hartwacht team was able to provide a diagnosis. Conclusions: This study reports on the first symptom-driven remote arrhythmia monitoring program in the Netherlands. Less than 10% of the ECGs were uninterpretable. However, the current performance of the KM algorithm makes the device inadequate as a stand-alone application, supporting the need for manual ECG analysis in HA and similar programs.
Original languageEnglish
Pages (from-to)38-45
JournalNetherlands Heart Journal
Volume27
Issue number1
DOIs
Publication statusPublished - 2019

Cite this

@article{c9c5b714d70d407f862d6b94ccbe7490,
title = "A mobile one-lead ECG device incorporated in a symptom-driven remote arrhythmia monitoring program. The first 5,982 Hartwacht ECGs",
abstract = "Background: In recent years many mobile devices able to record health-related data in ambulatory patients have emerged. However, well-organised programs to incorporate these devices are sparse. Hartwacht Arrhythmia (HA) is such a program, focusing on remote arrhythmia detection using the AliveCor Kardia Mobile (KM) and its algorithm. Objectives: The aim of this study was to assess the benefit of the KM device and its algorithm in detecting cardiac arrhythmias in a real-world cohort of ambulatory patients. Methods: All KM ECGs recorded in the HA program between January 2017 and March 2018 were included. Classification by the KM algorithm was compared with that of the Hartwacht team led by a cardiologist. Statistical analyses were performed with respect to detection of sinus rhythm (SR), atrial fibrillation (AF) and other arrhythmias. Results: 5,982 KM ECGs were received from 233 patients (mean age 58 years, 52{\%} male). The KM algorithm categorised 59{\%} as SR, 22{\%} as possible AF, 17{\%} as unclassified and 2{\%} as unreadable. According to the Hartwacht team, 498 (8{\%}) ECGs were uninterpretable. Negative predictive value for detection of AF was 98{\%}. However, positive predictive value as well as detection of other arrhythmias was poor. In 81{\%} of the unclassified ECGs, the Hartwacht team was able to provide a diagnosis. Conclusions: This study reports on the first symptom-driven remote arrhythmia monitoring program in the Netherlands. Less than 10{\%} of the ECGs were uninterpretable. However, the current performance of the KM algorithm makes the device inadequate as a stand-alone application, supporting the need for manual ECG analysis in HA and similar programs.",
author = "Selder, {J. L.} and L. Breukel and S. Blok and {van Rossum}, {A. C.} and Tulevski, {I. I.} and Allaart, {C. P.}",
year = "2019",
doi = "10.1007/s12471-018-1203-4",
language = "English",
volume = "27",
pages = "38--45",
journal = "Netherlands Heart Journal",
issn = "1568-5888",
publisher = "Bohn Stafleu van Loghum",
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}

A mobile one-lead ECG device incorporated in a symptom-driven remote arrhythmia monitoring program. The first 5,982 Hartwacht ECGs. / Selder, J. L.; Breukel, L.; Blok, S.; van Rossum, A. C.; Tulevski, I. I.; Allaart, C. P.

In: Netherlands Heart Journal, Vol. 27, No. 1, 2019, p. 38-45.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - A mobile one-lead ECG device incorporated in a symptom-driven remote arrhythmia monitoring program. The first 5,982 Hartwacht ECGs

AU - Selder, J. L.

AU - Breukel, L.

AU - Blok, S.

AU - van Rossum, A. C.

AU - Tulevski, I. I.

AU - Allaart, C. P.

PY - 2019

Y1 - 2019

N2 - Background: In recent years many mobile devices able to record health-related data in ambulatory patients have emerged. However, well-organised programs to incorporate these devices are sparse. Hartwacht Arrhythmia (HA) is such a program, focusing on remote arrhythmia detection using the AliveCor Kardia Mobile (KM) and its algorithm. Objectives: The aim of this study was to assess the benefit of the KM device and its algorithm in detecting cardiac arrhythmias in a real-world cohort of ambulatory patients. Methods: All KM ECGs recorded in the HA program between January 2017 and March 2018 were included. Classification by the KM algorithm was compared with that of the Hartwacht team led by a cardiologist. Statistical analyses were performed with respect to detection of sinus rhythm (SR), atrial fibrillation (AF) and other arrhythmias. Results: 5,982 KM ECGs were received from 233 patients (mean age 58 years, 52% male). The KM algorithm categorised 59% as SR, 22% as possible AF, 17% as unclassified and 2% as unreadable. According to the Hartwacht team, 498 (8%) ECGs were uninterpretable. Negative predictive value for detection of AF was 98%. However, positive predictive value as well as detection of other arrhythmias was poor. In 81% of the unclassified ECGs, the Hartwacht team was able to provide a diagnosis. Conclusions: This study reports on the first symptom-driven remote arrhythmia monitoring program in the Netherlands. Less than 10% of the ECGs were uninterpretable. However, the current performance of the KM algorithm makes the device inadequate as a stand-alone application, supporting the need for manual ECG analysis in HA and similar programs.

AB - Background: In recent years many mobile devices able to record health-related data in ambulatory patients have emerged. However, well-organised programs to incorporate these devices are sparse. Hartwacht Arrhythmia (HA) is such a program, focusing on remote arrhythmia detection using the AliveCor Kardia Mobile (KM) and its algorithm. Objectives: The aim of this study was to assess the benefit of the KM device and its algorithm in detecting cardiac arrhythmias in a real-world cohort of ambulatory patients. Methods: All KM ECGs recorded in the HA program between January 2017 and March 2018 were included. Classification by the KM algorithm was compared with that of the Hartwacht team led by a cardiologist. Statistical analyses were performed with respect to detection of sinus rhythm (SR), atrial fibrillation (AF) and other arrhythmias. Results: 5,982 KM ECGs were received from 233 patients (mean age 58 years, 52% male). The KM algorithm categorised 59% as SR, 22% as possible AF, 17% as unclassified and 2% as unreadable. According to the Hartwacht team, 498 (8%) ECGs were uninterpretable. Negative predictive value for detection of AF was 98%. However, positive predictive value as well as detection of other arrhythmias was poor. In 81% of the unclassified ECGs, the Hartwacht team was able to provide a diagnosis. Conclusions: This study reports on the first symptom-driven remote arrhythmia monitoring program in the Netherlands. Less than 10% of the ECGs were uninterpretable. However, the current performance of the KM algorithm makes the device inadequate as a stand-alone application, supporting the need for manual ECG analysis in HA and similar programs.

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UR - https://www.ncbi.nlm.nih.gov/pubmed/30523617

U2 - 10.1007/s12471-018-1203-4

DO - 10.1007/s12471-018-1203-4

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SP - 38

EP - 45

JO - Netherlands Heart Journal

JF - Netherlands Heart Journal

SN - 1568-5888

IS - 1

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