Key result
Single-lead ECG model QTNet estimates absolute QT intervals with ~12ms error and detects Dofetilide-induced prolongation.
Why the study?
Antiarrhythmic drug loading requires a 3-day hospitalization to monitor for QT prolongation, whereas automated QT monitoring with wearable ECG monitors could facilitate out-of-hospital care.
Does a deep learning model (QTNet) accurately estimate QT intervals from single-lead ECG and detect drug-induced QT prolongation?
Observational (n=1,570,675)
Yes
Does a deep learning model (QTNet) accurately estimate QT intervals from single-lead ECG and detect drug-induced QT prolongation?
Absolute Event Rate: 12.3% vs 90.8%
A deep learning model can accurately estimate QT intervals from a single-lead ECG and detect clinically meaningful drug-induced QT prolongation, potentially enabling outpatient monitoring for antiarrhythmic drug loading.
Supports single-lead QT monitoring feasibility for drug safety; hypothesis-generating pending prospective validation before practice change.
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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Alam et al. (2023) conducted an observational in QT prolongation (n=1,570,675). QTNet (Deep Learning Model) vs. Neurokit2 baseline algorithm was evaluated on Mean absolute error (MAE) for estimating absolute QT intervals (external validation). QTNet, a deep learning model using single-lead ECG, estimated absolute QT intervals with a mean absolute error of 12.30ms and detected Dofetilide-induced QTc prolongation with 87% sensitivity.
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