Interethnic Validation of an ECG Image Analysis Software for Detecting Left Ventricular Dysfunction in Emergency Department Population Article Swipe
Haemin Lee
,
Woon Yong Kwon
,
Kyoung Jun Song
,
You Hwan Jo
,
Joonghee Kim
,
Youngjin Cho
,
Ji Eun Hwang
,
Young Ho Choi
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/2024.10.15.24315559
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/2024.10.15.24315559
Background We previously developed and validated an AI-based ECG analysis tool (ECG Buddy) in a Korean population. This study aims to validate its performance in a U.S. population, specifically assessing its LV Dysfunction Score and LVEF-ECG feature for predicting LVEF <40%, using NT-ProBNP as a comparator. Methods We identified emergency department (ED) visits from the MIMIC-IV dataset with information on LVEF <40% or ≥40%, along with matched 12-lead ECG data recorded within 48 hours of the ED visit. The performance of ECG Buddy’s LV Dysfunction Score and LVEF-ECG feature was compared with NT-ProBNP using Receiver Operating Characteristic - Area Under the Curve (ROC-AUC) analysis. Results A total of 22,599 ED visits were analyzed. The LV Dysfunction Score had an AUC of 0.905 (95% CI: 0.899 - 0.910), with a sensitivity of 85.4% and specificity of 80.8%. The LVEF-ECG feature had an AUC of 0.908 (95% CI: 0.902 - 0.913), sensitivity 83.5%, and specificity 83.0%. NT-ProBNP had an AUC of 0.740 (95% CI: 0.727 - 0.752), with a sensitivity of 74.8% and specificity of 62.0%. The ECG-based predictors demonstrated superior diagnostic performance compared to NT-ProBNP (all p<0.001). In the Sinus Rhythm subgroup, the LV Dysfunction Score achieved an AUC of 0.913, and LVEF-ECG had an AUC of 0.917, both outperforming NT-ProBNP (0.748, 95% CI: 0.732 - 0.763, all p<0.001). Conclusion ECG Buddy demonstrated superior accuracy compared to NT-ProBNP in predicting LV systolic dysfunction, validating its utility in a U.S. ED population.
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Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2024.10.15.24315559
- OA Status
- green
- References
- 12
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4403454291
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4403454291Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2024.10.15.24315559Digital Object Identifier
- Title
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Interethnic Validation of an ECG Image Analysis Software for Detecting Left Ventricular Dysfunction in Emergency Department PopulationWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-10-16Full publication date if available
- Authors
-
Haemin Lee, Woon Yong Kwon, Kyoung Jun Song, You Hwan Jo, Joonghee Kim, Youngjin Cho, Ji Eun Hwang, Young Ho ChoiList of authors in order
- Landing page
-
https://doi.org/10.1101/2024.10.15.24315559Publisher landing page
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1101/2024.10.15.24315559Direct OA link when available
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Medicine, Ejection fraction, Emergency department, Receiver operating characteristic, Internal medicine, Cardiology, Area under the curve, Population, Sinus rhythm, Atrial fibrillation, Heart failure, Psychiatry, Environmental healthTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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