Performance of an Automatic Arrhythmia Classification Algorithm: Comparison to the ALTITUDE Electrophysiologist Panel Adjudications. Issue 7 (15th February 2014)
- Record Type:
- Journal Article
- Title:
- Performance of an Automatic Arrhythmia Classification Algorithm: Comparison to the ALTITUDE Electrophysiologist Panel Adjudications. Issue 7 (15th February 2014)
- Main Title:
- Performance of an Automatic Arrhythmia Classification Algorithm: Comparison to the ALTITUDE Electrophysiologist Panel Adjudications
- Authors:
- MAHAJAN, DEEPA
DONG, YANTING
SAXON, LESLIE A.
CHA, YONG‐MEI
GILLIAM, Francis ROOSEVELT
ASIRVATHAM, SAMUEL J.
CESARIO, DAVID A.
JONES, PAUL W.
SETH, MILAN
POWELL, BRIAN D. - Abstract:
- <abstract abstract-type="main"> <title> <x xml:space="preserve">Abstract</x> </title> <sec id="pace12367-sec-0010" sec-type="section"> <title>Introduction</title> <p>Adjudication of thousands of implantable cardioverter defibrillator (ICD)‐treated arrhythmia episodes is labor intensive and, as a result, is most often left undone. The objective of this study was to evaluate an automatic classification algorithm for adjudication of ICD‐treated arrhythmia episodes.</p> </sec> <sec id="pace12367-sec-0020" sec-type="section"> <title>Methods</title> <p>The algorithm uses a machine learning algorithm and was developed using 776 arrhythmia episodes. The algorithm was validated on 131 dual‐chamber ICD shock episodes from 127 patients adjudicated by seven electrophysiologists (EPs). Episodes were classified by panel consensus as ventricular tachycardia/ventricular fibrillation (VT/VF) or non‐VT/VF, with the resulting classifications used as the reference. Subsequently, each episode electrogram (EGM) data was randomly assigned to three EPs without the atrial lead information, and to three EPs with the atrial lead information. Those episodes were also classified by the automatic algorithm with and without atrial information. Agreement with the reference was compared between the three EPs consensus group and the algorithm.</p> </sec> <sec id="pace12367-sec-0030" sec-type="section"> <title>Results</title> <p>The overall agreement with the reference was similar between three‐EP consensus<abstract abstract-type="main"> <title> <x xml:space="preserve">Abstract</x> </title> <sec id="pace12367-sec-0010" sec-type="section"> <title>Introduction</title> <p>Adjudication of thousands of implantable cardioverter defibrillator (ICD)‐treated arrhythmia episodes is labor intensive and, as a result, is most often left undone. The objective of this study was to evaluate an automatic classification algorithm for adjudication of ICD‐treated arrhythmia episodes.</p> </sec> <sec id="pace12367-sec-0020" sec-type="section"> <title>Methods</title> <p>The algorithm uses a machine learning algorithm and was developed using 776 arrhythmia episodes. The algorithm was validated on 131 dual‐chamber ICD shock episodes from 127 patients adjudicated by seven electrophysiologists (EPs). Episodes were classified by panel consensus as ventricular tachycardia/ventricular fibrillation (VT/VF) or non‐VT/VF, with the resulting classifications used as the reference. Subsequently, each episode electrogram (EGM) data was randomly assigned to three EPs without the atrial lead information, and to three EPs with the atrial lead information. Those episodes were also classified by the automatic algorithm with and without atrial information. Agreement with the reference was compared between the three EPs consensus group and the algorithm.</p> </sec> <sec id="pace12367-sec-0030" sec-type="section"> <title>Results</title> <p>The overall agreement with the reference was similar between three‐EP consensus and the algorithm for both with atrial EGM (94% vs 95%, P = 0.87) and without atrial EGM (90% vs 91%, P = 0.91). The odds of accurate adjudication, after adjusting for covariates, did not significantly differ between the algorithm and EP consensus (odds ratio 1.02, 95% confidence interval: 0.97–1.06).</p> </sec> <sec id="pace12367-sec-0040" sec-type="section"> <title>Conclusions</title> <p>This algorithm performs at a level comparable to an EP panel in the adjudication of arrhythmia episodes treated by both dual‐ and single‐chamber ICDs. This type of algorithm has the potential for automated analysis of clinical ICD episodes, and adjudication of EGMs for research studies and quality analyses.</p> </sec> </abstract> … (more)
- Is Part Of:
- Pacing and clinical electrophysiology. Volume 37:Issue 7(2014)
- Journal:
- Pacing and clinical electrophysiology
- Issue:
- Volume 37:Issue 7(2014)
- Issue Display:
- Volume 37, Issue 7 (2014)
- Year:
- 2014
- Volume:
- 37
- Issue:
- 7
- Issue Sort Value:
- 2014-0037-0007-0000
- Page Start:
- 889
- Page End:
- 899
- Publication Date:
- 2014-02-15
- Subjects:
- Cardiac pacing -- Periodicals
Electrophysiology -- Periodicals
617.4120645 - Journal URLs:
- http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1540-8159 ↗
http://www.blackwell-synergy.com/rd.asp?goto=journal&code=pace ↗
http://www.futuraco.com/journalsf.htm ↗
http://onlinelibrary.wiley.com/ ↗
http://firstsearch.oclc.org ↗
http://firstsearch.oclc.org/journal=0147-8389;screen=info;ECOIP ↗ - DOI:
- 10.1111/pace.12367 ↗
- Languages:
- English
- ISSNs:
- 0147-8389
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 6328.210000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 3021.xml