作者
Edi Prifti, Ahmad Fall, Giovanni Davogustto, Alfredo Pulini, Isabelle Denjoy, Christian Funck-Brentano, Yasmin Khan, Alexandre Durand-Salmon, Fabio Badilini, Quinn S Wells, Antoine Leenhardt, Jean-Daniel Zucker, Dan M Roden, Fabrice Extramiana, Joe-Elie Salem
发表日期
2021/10/7
期刊
European Heart Journal
卷号
42
期号
38
页码范围
3948-3961
出版商
Oxford University Press
简介
Aims
Congenital long-QT syndromes (cLQTS) or drug-induced long-QT syndromes (diLQTS) can cause torsade de pointes (TdP), a life-threatening ventricular arrhythmia. The current strategy for the identification of drugs at the high risk of TdP relies on measuring the QT interval corrected for heart rate (QTc) on the electrocardiogram (ECG). However, QTc has a low positive predictive value.
Methods and results
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 the prediction of drug-induced TdP (diTdP) and diagnosis of cLQTS. Tested CNN models used single or multiple 10-s recordings/patient using 8 leads or single leads in various cohorts: 1029 healthy subjects before and after sotalol intake (n = 14 135 ECGs); 487 cLQTS patients …
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E Prifti, A Fall, G Davogustto, A Pulini, I Denjoy… - European Heart Journal, 2021