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- Title
Detecting QT prolongation from a single-lead ECG with deep learning.
- Authors
Alam, Ridwan; Aguirre, Aaron; Stultz, Collin M.
- Abstract
For a number of antiarrhythmics, drug loading requires a 3-day hospitalization with continuous monitoring for QT-prolongation. Automated QT monitoring with wearable ECG monitors would enable out-of-hospital care. We therefore develop a deep learning model that infers QT intervals from ECG Lead-I—the lead that is often available in ambulatory ECG monitors—and use this model to detect clinically meaningful QT-prolongation episodes during Dofetilide drug loading. QTNet–a deep neural network that infers QT intervals from Lead-I ECG–was trained using over 3 million ECGs from 653 thousand patients at the Massachusetts General Hospital and tested on an internal-test set consisting of 633 thousand ECGs from 135 thousand patients. QTNet is further evaluated on an external-validation set containing 3.1 million ECGs from 667 thousand patients at another healthcare institution. On both evaluations, the model 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). Finally, 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 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%. These results show that drug-induced QT prolongation risk can be tracked from ECG Lead-I using deep learning. Author summary: Ambulatory ECG monitoring, coupled with sophisticated algorithms for identifying instances of QT prolongation, can enable outpatient Dofetilide-loading in low-risk patients. As most ambulatory ECG monitors record the equivalent of a Lead-I ECG, we developed a model, QTNet, that can estimate QT intervals using ECG Lead-I alone. QTNet estimates QT intervals that are similar to those generated from the 12-lead ECG by the clinical ECG machines, with a mean absolute error of 12ms and a Pearson correlation coefficient of 0.91. When applied in a zero-shot manner (without any fine-tuning) to an external population undergoing Dofetilide loading, QTNet identifies when clinically critical QT prolongation occurs during drug loading. QTNet is a novel regression model that can be used on Lead-I ECG streams, potentially from wearable devices at out-of-hospital settings, for health critical applications such as drug-induced QT prolongation tracking.
- Subjects
MASSACHUSETTS; LONG QT syndrome diagnosis; PUBLIC hospitals; PEARSON correlation (Statistics); RESEARCH funding; PROBABILITY theory; DESCRIPTIVE statistics; ELECTROCARDIOGRAPHY; ARRHYTHMIA; DEEP learning; DOFETILIDE; ARTIFICIAL neural networks; ELECTRONIC health records; MONITOR alarms (Medicine); ELECTRODES; SENSITIVITY &; specificity (Statistics)
- Publication
PLoS Digital Health, 2024, Vol 3, Issue 6, p1
- ISSN
2767-3170
- Publication type
Article
- DOI
10.1371/journal.pdig.0000539