- Record: found
- Abstract: found
- Article: found
Is Open Access
research-article
Author(s):
R. R. van de Leur 1 , 2 ,
H. Bleijendaal 3 , 4 ,
K. Taha 1 , 2 ,
T. Mast 5 ,
J. M. I. H. Gho 1 , 6 ,
M. Linschoten 1 ,
B. van Rees 7 ,
M. T. H. M. Henkens 7 ,
S. Heymans 7 , 8 ,
N. Sturkenboom 5 ,
R. A. Tio 5 ,
J. A. Offerhaus 3 ,
W. L. Bor 9 ,
M. Maarse 3 , 9 ,
H. E. Haerkens-Arends 6 ,
M. Z. H. Kolk 3 ,
A. C. J. van der Lingen 10 ,
J. J. Selder 10 ,
E. E. Wierda 11 ,
P. F. M. M. van Bergen 11 ,
M. M. Winter 3 ,
A. H. Zwinderman 4 ,
P. A. Doevendans 1 , 2 , 12 ,
P. van der Harst 1 ,
Y. M. Pinto 3 ,
F. W. Asselbergs 1 , 13 , 14 ,
R. van Es 1 ,
F. V. Y. Tjong 3 , ,
the CAPACITY-COVID collaborative consortium
Publication date (Electronic): 17 March 2022
Journal: Netherlands Heart Journal
Publisher: Bohn Stafleu van Loghum
Keywords: COVID-19, Electrocardiogram, Machine learning, Deep learning, Arrhythmia, Mortality
Read this article at
ScienceOpenPublisherPMC
- Download PDF
- XML
- Review article
- Invite someone to review
Bookmark
There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.
The electrocardiogram (ECG) is frequently obtained in the work-up of COVID-19 patients. So far, no study has evaluated whether ECG-based machine learning models have added value to predict in-hospital mortality specifically in COVID-19 patients. Using data from the CAPACITY-COVID registry, we studied 882 patients admitted with COVID-19 across seven hospitals in the Netherlands. Raw format 12-lead ECGs recorded within 72 h of admission were studied. With data from five hospitals ( n = 634), three models were developed: (a)alogistic regression baseline model using age and sex, (b)aleast absolute shrinkage and selection operator (LASSO) model using age, sex and human annotated ECG features, and (c)apre-trained deep neural network (DNN) using age, sex and the raw ECG waveforms. Data from two hospitals ( n = 248) was used for external validation. Performances for modelsa, b andc were comparable with an area under the receiver operating curve of 0.73 (95% confidence interval [CI] 0.65–0.79), 0.76 (95% CI 0.68–0.82) and 0.77 (95% CI 0.70–0.83) respectively. Predictors of mortality in the LASSO model were age, low QRS voltage, ST depression, premature atrial complexes, sex, increased ventricular rate, and right bundle branch block. This study shows that the ECG-based prediction models could be helpful for the initial risk stratification of patients diagnosed with COVID-19, and that several ECG abnormalities are associated with in-hospital all-cause mortality of COVID-19 patients. Moreover, this proof-of-principle study shows that the use of pre-trained DNNs for ECG analysis does not underperform compared with time-consuming manual annotation of ECG features. Abstract
Background and purpose
Methods
Results
Conclusion
Related collections
Most cited references19
- Record: found
- Abstract: found
- Article: not found
Deep learning.
Yann LeCun, Yoshua Bengio, Geoffrey E Hinton (2015)
Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.
0 comments Cited 8970 times – based on 0 reviews Review now
Bookmark
- Record: found
- Abstract: found
- Article: not found
Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network
Awni Y. Hannun, Pranav Rajpurkar, Masoumeh Haghpanahi … (2019)
Computerized electrocardiogram (ECG) interpretation plays a critical role in the clinical ECG workflow1. Widely available digital ECG data and the algorithmic paradigm of deep learning2 present an opportunity to substantially improve the accuracy and scalability of automated ECG analysis. However, a comprehensive evaluation of an end-to-end deep learning approach for ECG analysis across a wide variety of diagnostic classes has not been previously reported. Here, we develop a deep neural network (DNN) to classify 12 rhythm classes using 91,232 single-lead ECGs from 53,549 patients who used a single-lead ambulatory ECG monitoring device. When validated against an independent test dataset annotated by a consensus committee of board-certified practicing cardiologists, the DNN achieved an average area under the receiver operating characteristic curve (ROC) of 0.97. The average F1 score, which is the harmonic mean of the positive predictive value and sensitivity, for the DNN (0.837) exceeded that of average cardiologists (0.780). With specificity fixed at the average specificity achieved by cardiologists, the sensitivity of the DNN exceeded the average cardiologist sensitivity for all rhythm classes. These findings demonstrate that an end-to-end deep learning approach can classify a broad range of distinct arrhythmias from single-lead ECGs with high diagnostic performance similar to that of cardiologists. If confirmed in clinical settings, this approach could reduce the rate of misdiagnosed computerized ECG interpretations and improve the efficiency of expert human ECG interpretation by accurately triaging or prioritizing the most urgent conditions.
0 comments Cited 613 times – based on 0 reviews Review now
Bookmark
- Record: found
- Abstract: found
- Article: found
Is Open Access
Risk stratification of patients admitted to hospital with covid-19 using the ISARIC WHO Clinical Characterisation Protocol: development and validation of the 4C Mortality Score
Stephen R. Knight, Antonia Ho, Riinu Pius … (2020)
Objective To develop and validate a pragmatic risk score to predict mortality in patients admitted to hospital with coronavirus disease 2019 (covid-19). Design Prospective observational cohort study. Setting International Severe Acute Respiratory and emerging Infections Consortium (ISARIC) World Health Organization (WHO) Clinical Characterisation Protocol UK (CCP-UK) study (performed by the ISARIC Coronavirus Clinical Characterisation Consortium—ISARIC-4C) in 260 hospitals across England, Scotland, and Wales. Model training was performed on a cohort of patients recruited between 6 February and 20 May 2020, with validation conducted on a second cohort of patients recruited after model development between 21 May and 29 June 2020. Participants Adults (age ≥18 years) admitted to hospital with covid-19 at least four weeks before final data extraction. Main Outcome Measure In-hospital mortality. Results 35 463 patients were included in the derivation dataset (mortality rate 32.2%) and 22 361 in the validation dataset (mortality rate 30.1%). The final 4C Mortality Score included eight variables readily available at initial hospital assessment: age, sex, number of comorbidities, respiratory rate, peripheral oxygen saturation, level of consciousness, urea level, and C reactive protein (score range 0-21 points). The 4C Score showed high discrimination for mortality (derivation cohort: area under the receiver operating characteristic curve 0.79, 95% confidence interval 0.78 to 0.79; validation cohort: 0.77, 0.76 to 0.77) with excellent calibration (validation: calibration-in-the-large=0, slope=1.0). Patients with a score of at least 15 (n=4158, 19%) had a 62% mortality (positive predictive value 62%) compared with 1% mortality for those with a score of 3 or less (n=1650, 7%; negative predictive value 99%). Discriminatory performance was higher than 15 pre-existing risk stratification scores (area under the receiver operating characteristic curve range 0.61-0.76), with scores developed in other covid-19 cohorts often performing poorly (range 0.63-0.73). Conclusions An easy-to-use risk stratification score has been developed and validated based on commonly available parameters at hospital presentation. The 4C Mortality Score outperformed existing scores, showed utility to directly inform clinical decision making, and can be used to stratify patients admitted to hospital with covid-19 into different management groups. The score should be further validated to determine its applicability in other populations. Study Registration ISRCTN66726260
0 comments Cited 522 times – based on 0 reviews Review now
Bookmark
All references
Author and article information
Contributors
F. V. Y. Tjong: f.v.tjong@amsterdamumc.nl
Journal
Journal ID (nlm-ta): Neth Heart J
Journal ID (iso-abbrev): Neth Heart J
Title: Netherlands Heart Journal
Publisher: Bohn Stafleu van Loghum (Houten )
ISSN (Print): 1568-5888
ISSN (Electronic): 1876-6250
Publication date (Electronic): 17 March 2022
Publication date PMC-release: 17 March 2022
Pages: 1-7
Affiliations
[1 ]GRID grid.5477.1, ISNI 0000000120346234, Department of Cardiology, Division of Heart and Lungs, University Medical Centre Utrecht, , Utrecht University, ; Utrecht, The Netherlands
[3 ]GRID grid.7177.6, ISNI 0000000084992262, Department of Clinical and Experimental Cardiology, Amsterdam University Medical Centres, Heart Center, Amsterdam Cardiovascular Sciences, , University of Amsterdam, ; Amsterdam, The Netherlands
[4 ]GRID grid.7177.6, ISNI 0000000084992262, Department of Clinical Epidemiology, Biostatistics & Bioinformatics, Amsterdam University Medical Centres, , University of Amsterdam, ; Amsterdam, The Netherlands
[5 ]GRID grid.413532.2, ISNI 0000 0004 0398 8384, Department of Cardiology, , Catharina Hospital Eindhoven, ; Eindhoven, The Netherlands
[6 ]GRID grid.413508.b, ISNI 0000 0004 0501 9798, Department of Cardiology, , Jeroen Bosch Hospital, ; ’s-Hertogenbosch, The Netherlands
[7 ]GRID grid.5012.6, ISNI 0000 0001 0481 6099, Department of Cardiology, CARIM School for Cardiovascular Diseases, , Maastricht University, ; Maastricht, The Netherlands
[8 ]GRID grid.5596.f, ISNI 0000 0001 0668 7884, Centre for Molecular and Vascular Biology, Department of Cardiovascular Sciences, , KU Leuven, ; Leuven, Belgium
[9 ]GRID grid.415960.f, ISNI 0000 0004 0622 1269, Department of Cardiology, , St. Antonius Hospital, ; Nieuwegein, The Netherlands
[10 ]GRID grid.12380.38, ISNI 0000 0004 1754 9227, Department of Cardiology, Amsterdam University Medical Centres, Amsterdam Cardiovascular Sciences, , Vrije Universiteit Amsterdam, ; Amsterdam, The Netherlands
[12 ]GRID grid.413762.5, ISNI 0000 0004 8514 3501, Central Military Hospital, ; Utrecht, The Netherlands
Article
Publisher ID: 1670
DOI: 10.1007/s12471-022-01670-2
PMC ID: 8929464
PubMed ID: 35301688
SO-VID: e998dc77-2728-4cf6-87c3-b0d2bc206802
Copyright © © The Author(s) 2022
License:
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
History
Date accepted : 27 January 2022
Funding
Funded by: dutch heart foundation
Award ID: 2020B006 CAPACITY
Award Recipient : F. W. Asselbergs
Funded by: national institute for health research (nihr)/british heart foundation
Award ID: COVID-19 Flagship Research programme
Award Recipient : F. W. Asselbergs
Funded by: FundRef http://dx.doi.org/10.13039/501100006419, euroqol research foundation;
Funded by: novartis global
Funded by: amgen europe
Funded by: novo nordisk nederland
Funded by: servier nederland
Funded by: daiichi sankyo nederland
Funded by: FundRef http://dx.doi.org/10.13039/501100001826, zonmw;
Award ID: 104021004
Award ID: 104021004
Award ID: 2019-3-452019308
Award Recipient : F. V. Y. Tjong
Funded by: ern guard-heart
Funded by: university of amsterdam research priority area medical integromics
Funded by: amsterdam cardiovascular sciences
Funded by: FundRef http://dx.doi.org/10.13039/501100001674, fondation leducq;
Award ID: CURE-PLaN
Award Recipient : P. A. Doevendans
Funded by: alexandre suerman stipend of the university medical center utrecht
Funded by: university college london hospitals national institute for health research biomedical research
Funded by: eu/efpia innovative medicines initiative 2 joint undertaking bigdata@heart
Award ID: 116074
Award Recipient : F. W. Asselbergs
Funded by: cardiovasculair onderzoek nederland 2015-12 edetect
Categories
Subject: Original Article
ScienceOpen disciplines: Cardiovascular Medicine
Keywords: covid-19,electrocardiogram,machine learning,deep learning,arrhythmia,mortality
Data availability:
ScienceOpen disciplines: Cardiovascular Medicine
Keywords: covid-19, electrocardiogram, machine learning, deep learning, arrhythmia, mortality
Comments
Comment on this article
Sign in to comment