  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.
