Sammani, Arjan;
Van de Leur, Rutger R;
Henkens, Michiel THM;
Meine, Mathias;
Loh, Peter;
Hassink, Rutger J;
Oberski, Daniel L;
... Van Es, Rene; + view all
(2022)
Life-threatening ventricular arrhythmia prediction in patients with dilated cardiomyopathy using explainable electrocardiogram-based deep neural networks.
EP Europace
, 24
(10)
pp. 1645-1654.
10.1093/europace/euac054.
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Abstract
AIMS: While electrocardiogram (ECG) characteristics have been associated with life-threatening ventricular arrhythmias (LTVA) in dilated cardiomyopathy (DCM), they typically rely on human-derived parameters. Deep neural networks (DNNs) can discover complex ECG patterns, but the interpretation is hampered by their 'black-box' characteristics. We aimed to detect DCM patients at risk of LTVA using an inherently explainable DNN. METHODS AND RESULTS: In this two-phase study, we first developed a variational autoencoder DNN on more than 1 million 12-lead median beat ECGs, compressing the ECG into 21 different factors (F): FactorECG. Next, we used two cohorts with a combined total of 695 DCM patients and entered these factors in a Cox regression for the composite LTVA outcome, which was defined as sudden cardiac arrest, spontaneous sustained ventricular tachycardia, or implantable cardioverter-defibrillator treated ventricular arrhythmia. Most patients were male (n = 442, 64%) with a median age of 54 years [interquartile range (IQR) 44-62], and median left ventricular ejection fraction of 30% (IQR 23-39). A total of 115 patients (16.5%) reached the study outcome. Factors F8 (prolonged PR-interval and P-wave duration, P < 0.005), F15 (reduced P-wave height, P = 0.04), F25 (increased right bundle branch delay, P = 0.02), F27 (P-wave axis P < 0.005), and F32 (reduced QRS-T voltages P = 0.03) were significantly associated with LTVA. CONCLUSION: Inherently explainable DNNs can detect patients at risk of LTVA which is mainly driven by P-wave abnormalities.
| Type: | Article |
|---|---|
| Title: | Life-threatening ventricular arrhythmia prediction in patients with dilated cardiomyopathy using explainable electrocardiogram-based deep neural networks |
| Location: | England |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1093/europace/euac054 |
| Publisher version: | https://doi.org/10.1093/europace/euac054 |
| Language: | English |
| Additional information: | Copyright © The Author(s) 2022. Published by Oxford University Press on behalf of the European Society of Cardiology. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com. |
| Keywords: | Dilated cardiomyopathy, Deep neural network, Prognosis, Sudden cardiac death, Implantable cardioverter-defibrillator |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics > Clinical Epidemiology UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10158618 |
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