QTNet: Predicting Drug-Induced QT prolongation with Deep Neural Networks

Jethani, N.; Zhang, H.; Chinitz, L.; Aphinyanaphongs, Y.; Ranganath, R.; Jankelson, L.

Background:Drug-induced QTc prolongation (diQTP) is frequent and associated with a risk of sudden cardiac death. Identifying patients at risk of diQTP can enhance monitoring and treatment plans. Objective: To develop a machine learning architecture for prediction of extreme diQTP (QTc >500ms OR {Delta}QTc >60ms) at the onset of treatment with a QTc prolonging drug. Methods: We included 4,628 adult patients who received a baseline ECG within 6 months prior to treatment onset with a QTc prolonging drug and a follow-up ECG after the fifth dose. We collected known clinical QTc prolongation risk factors (CF). We developed a novel neural network architecture (QTNet) to predict diQTP from both the CF and baseline ECG data (ECGD), composed of both the ECG waveform and measurements (i.e. QTc),by fusing a state-of-the-art convolution neural network to process raw ECG waveforms with the CF using amulti-layer perceptron. We fit a logistic regression model using the CF, replicating RISQ-PATH as Baseline. We further compared the performance of QTNet (CF+ECGD) to neural network models trained using three variable subsets: a) baseline QTc (QTC-NN), b) CF-NN, and c) ECGD-NN. Results: diQTP was present in 1030 patients (22.3%), of which baseline QTc was normal (QTc<450ms:Male/<470ms:Female) in 405 patients (39.3%). QTNet achieved the best performance (Figure 1)(AUROC, 0.802 [95% CI, 0.782-0.820]), outperforming predictions based on the Baseline (AUROC, 0.738 [95%CI, 0.716-0.757]), QTC-NN (AUROC, 0.735 [95% CI, 0.710-0.757]), CF-NN (AUROC, 0.778 [95% CI, 0.757-0.799]), and ECGD-NN (AUROC, 0.774 [95% CI, 0.750-0.794]). Conclusion: We developed QTNet, the first deep learning model for predicting extreme diQTP, outperforming models trained on known clinical risk factors.