Detecting QT prolongation From a Single-lead ECG With Deep Learning Article Swipe
Ridwan Alam
,
Aaron D. Aguirre
,
Collin M. Stultz
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2401.05378
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2401.05378
For a number of antiarrhythmics, drug loading requires a 3 day hospitalization with monitoring for QT prolongation. Automated QT monitoring with wearable ECG monitors would facilitate out-of-hospital care. We develop a deep learning model that infers QT intervals from ECG lead-I - the lead most often acquired from ambulatory ECG monitors - and to use this model to detect clinically meaningful QT-prolongation episodes during Dofetilide drug loading. Using 4.22 million 12-lead ECG recordings from 903.6 thousand patients at the Massachusetts General Hospital, we develop a deep learning model, QTNet, that infers QT intervals from lead-I. Over 3 million ECGs from 653 thousand patients are used to train the model and an internal-test set containing 633 thousand ECGs from 135 thousand patients was used for testing. QTNet is further evaluated on an external-validation set containing 3.1 million ECGs from 667 thousand patients at another institution. QTNet was used to detect Dofetilide-induced QT prolongation in a publicly available database (ECGRDVQ-dataset) containing ECGs from subjects enrolled in a clinical trial evaluating the effects of antiarrhythmic drugs. QTNet achieves mean absolute errors of 12.63ms (internal-test) and 12.30ms (external-validation) for estimating absolute QT intervals. The associated Pearson correlation coefficients are 0.91 (internal-test) and 0.92 (external-validation). For the ECGRDVQ-dataset, QTNet detects Dofetilide-induced QTc prolongation with 87% sensitivity and 77% specificity. The negative predictive value of the model is greater than 95% when the pre-test probability of drug-induced QTc prolongation is below 25%. Drug-induced QT prolongation risk can be tracked from ECG lead-I using deep learning.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2401.05378
- https://arxiv.org/pdf/2401.05378
- OA Status
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- References
- 19
- Related Works
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- OpenAlex ID
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https://doi.org/10.48550/arxiv.2401.05378Digital Object Identifier
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Detecting QT prolongation From a Single-lead ECG With Deep LearningWork title
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preprintOpenAlex work type
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enPrimary language
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2023Year of publication
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2023-12-17Full publication date if available
- Authors
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Ridwan Alam, Aaron D. Aguirre, Collin M. StultzList of authors in order
- Landing page
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https://arxiv.org/abs/2401.05378Publisher landing page
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https://arxiv.org/pdf/2401.05378Direct link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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QT interval, Dofetilide, Medicine, Prolongation, Internal medicine, Ambulatory ECG, Lead (geology), Torsades de pointes, Electrocardiography, Cardiology, Geology, GeomorphologyTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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