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Identifying Risk of...
Identifying Risk of Adverse Outcomes in COVID-19 Patients via Artificial Intelligence-Powered Analysis of 12-Lead Intake Electrocardiogram.
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- Sridhar, Arun R. (författare)
- Division of Cardiology, University of Washington, Seattle, Washington.;
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- Chen, Zih-Hua (författare)
- Department of Bioengineering, University of Washington, Seattle, Washington.
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- Mayfield, Jacob J. (författare)
- Division of Cardiology, University of Washington, Seattle, Washington.
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- Fohner, Alison E. (författare)
- Department of Epidemiology, University of Washington, Seattle, Washington.
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- Arvanitis, Panagiotis (författare)
- Uppsala universitet,Kardiologi-arrytmi
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- Atkinson, Sarah (författare)
- Division of Cardiology, University of Washington, Seattle, Washington.
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- Braunschweig, Frieder (författare)
- Karolinska Institutet
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- Chatterjee, Neal A. (författare)
- Division of Cardiology, University of Washington, Seattle, Washington.
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- Zamponi, Alessio Falasca (författare)
- Uppsala universitet,Institutionen för medicinska vetenskaper
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- Johnson, Gregory (författare)
- Unaffiliated independent researcher, Seattle, Washington.
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- Joshi, Sanika A. (författare)
- Department of Bioengineering, University of Washington, Seattle, Washington.
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- Lassen, Mats C. H. (författare)
- Department of Cardiology, Herlev & Gentofte University Hospital, Copenhagen University, Copenhagen, Denmark.
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- Poole, Jeanne E. (författare)
- Division of Cardiology, University of Washington, Seattle, Washington.
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- Rumer, Christopher (författare)
- Division of Cardiology, University of Washington, Seattle, Washington.
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- Skaarup, Kristoffer G. (författare)
- Department of Cardiology, Herlev & Gentofte University Hospital, Copenhagen University, Copenhagen, Denmark.
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- Biering-Sørensen, Tor (författare)
- Department of Cardiology, Herlev & Gentofte University Hospital, Copenhagen University, Copenhagen, Denmark.
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- Blomström-Lundqvist, Carina (författare)
- Uppsala universitet,Kardiologi-arrytmi,Kardiologi
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- Linde, Cecilia M. (författare)
- Department of Medicine, Karolinska Institutet, Stockholm, Sweden.; Department of Cardiology, Karolinska University Hospital, Stockholm, Sweden.
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- Maleckar, Mary M. (författare)
- Department of Computational Physiology, Simula Research Laboratory, Oslo, Norway.
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- Boyle, Patrick M (författare)
- Department of Bioengineering, University of Washington, Seattle, Washington.; Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, Washington.; Center for Cardiovascular Biology, University of Washington, Seattle, Washington.
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Division of Cardiology, University of Washington, Seattle, Washington; Department of Bioengineering, University of Washington, Seattle, Washington. (creator_code:org_t)
- Elsevier, 2022
- 2022
- Engelska.
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Ingår i: Cardiovascular digital health journal. - : Elsevier. - 2666-6936. ; 3:2, s. 62-74
- Relaterad länk:
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https://doi.org/10.1...
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https://uu.diva-port... (primary) (Raw object)
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http://www.cvdigital...
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https://urn.kb.se/re...
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https://doi.org/10.1...
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http://kipublication...
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Abstract
Ämnesord
Stäng
- Background: Adverse events in COVID-19 are difficult to predict. Risk stratification is encumbered by the need to protect healthcare workers. We hypothesize that AI can help identify subtle signs of myocardial involvement in the 12-lead electrocardiogram (ECG), which could help predict complications.Objective: Use intake ECGs from COVID-19 patients to train AI models to predict risk of mortality or major adverse cardiovascular events (MACE).Methods: We studied intake ECGs from 1448 COVID-19 patients (60.5% male, 63.4±16.9 years). Records were labeled by mortality (death vs. discharge) or MACE (no events vs. arrhythmic, heart failure [HF], or thromboembolic [TE] events), then used to train AI models; these were compared to conventional regression models developed using demographic and comorbidity data.Results: 245 (17.7%) patients died (67.3% male, 74.5±14.4 years); 352 (24.4%) experienced at least one MACE (119 arrhythmic; 107 HF; 130 TE). AI models predicted mortality and MACE with area under the curve (AUC) values of 0.60±0.05 and 0.55±0.07, respectively; these were comparable to AUC values for conventional models (0.73±0.07 and 0.65±0.10). There were no prominent temporal trends in mortality rate or MACE incidence in our cohort; holdout testing with data from after a cutoff date (June 9, 2020) did not degrade model performance.Conclusion: Using intake ECGs alone, our AI models had limited ability to predict hospitalized COVID-19 patients' risk of mortality or MACE. Our models' accuracy was comparable to that of conventional models built using more in-depth information, but translation to clinical use would require higher sensitivity and positive predictive value. In the future, we hope that mixed-input AI models utilizing both ECG and clinical data may be developed to enhance predictive accuracy.
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Kardiologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Cardiac and Cardiovascular Systems (hsv//eng)
Nyckelord
- 12-lead ECG
- Artificial Intelligence
- COVID-19
- Deep Learning Arrhythmia
- Heart Failure Prognosis
- Mortality
- Risk Factors
- Cardiology
- Kardiologi
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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Till lärosätets databas
- Av författaren/redakt...
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Sridhar, Arun R.
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Chen, Zih-Hua
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Mayfield, Jacob ...
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Fohner, Alison E ...
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Arvanitis, Panag ...
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Atkinson, Sarah
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Braunschweig, Fr ...
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Chatterjee, Neal ...
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Zamponi, Alessio ...
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Johnson, Gregory
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Joshi, Sanika A.
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Lassen, Mats C. ...
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Poole, Jeanne E.
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Rumer, Christoph ...
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Skaarup, Kristof ...
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Biering-Sørensen ...
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Blomström-Lundqv ...
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Linde, Cecilia M ...
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Maleckar, Mary M ...
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Boyle, Patrick M
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- MEDICIN OCH HÄLSOVETENSKAP
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MEDICIN OCH HÄLS ...
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och Klinisk medicin
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Cardiovascular d ...
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Uppsala universitet
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Karolinska Institutet