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Deep learning analysis of ECG for risk prediction of drug-induced arrhythmias and diagnosis of long QT syndrome
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Edité par CCSD ; Oxford University Press (OUP) -
International audience. Aims: Congenital (cLQTS) or drug-induced (diLQTS) long-QT syndromes can cause Torsadede-Pointes (TdP), a life-threatening ventricular arrhythmia. The current strategy for identification of drugs at high-risk of TdP relies on measuring the QT-interval corrected for heart rate (QTc) on ECG. However, QTc has a low positive predictive value. Methods: We used convolutional-neural-network (CNN) models to quantify ECG alterations induced by sotalol, an IKr-blocker associated with TdP, aiming to provide new tools (CNN-models) to enhance prediction of diTdP and diagnosis of cLQTS. Tested CNN-models used single or multiple 10sec recordings/patient using 8 leads or single leads in various cohorts: 1029 healthy subjects before and after sotalol intake (n=14135 ECGs); 487 cLQTS patients (n=1083 ECGs: 560 type 1, 456 type 2, 67 type 3); 48 patients with diTdP (n=1105 ECGs, with 147 obtained within 48hours of a diTdP episode). Results: CNN-models outperformed models using QTc to identify exposure to sotalol (ROC-AUC=0.98 vs. 0.72,p≤0.001). CNN-models had higher ROC-AUC using multiple vs. single 10s-ECG (p≤0.001). Performances were comparable for 8-lead vs. single lead models. CNN-models predicting sotalol exposure also accurately detected presence and type of cLQTS vs. healthy controls, particularly for cLQT2 (AUC-ROC=0.9), and were greatest shortly after a diTdP event and declining over time(p≤0.001), after controlling for QTc and intake of culprit drugs. ECG segment analysis identified the J-Tpeak interval as the best discriminator of sotalol intake. Conclusion: CNN-models applied to ECGs outperform QTc measurements to identify exposure to drugs altering the QT-interval, congenital LQTS, and are greatest shortly after a diTdP episode.
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