Torsades de Pointes (TdP) are potentially fatal ventricular arrhythmias that are promoted by prolonged ventricular repolarization (Long QT, LQT). The different forms of LQT result from inhibition of cardiac potassium currents (IKr and IKs) or activation of a late sodium current (INaL). These alterations may be either congenital (3 types: cLQT-1: IKs, cLQT-2: IKr, cLQT-3: INaL) or drug-induced (diLQT, via inhibition of IKr). More than 100 medications have received marketing authorization despite a known risk of TdP, due to a favorable benefit-risk ratio (e.g., hydroxychloroquine). QTc, which represents the duration of ventricular repolarization (in milliseconds) - defined as the time from the beginning of the QRS complex to the end of the T wave, corrected for heart rate - is prolonged in all forms of LQT. Specific T-wave abnormalities, depending on the altered ion currents, have been described and can help differentiate the various types of congenital or drug-induced LQT. However, screening for LQT and TdP risk, both at the individual and population levels, currently relies mainly on isolated QTc evaluation and genetic testing, which often takes considerable time to return. Thus, limiting ECG analysis to QTc measurement alone offers low predictive value, as the ECG contains a wealth of additional information beyond a single interval. The investigator recently demonstrated that artificial intelligence (AI)-based ECG analysis using deep-learning convolutional neural networks can detect more discriminative features of the ECG for predicting the type of LQT and the risk of TdP, going beyond QTc alone. Using these techniques, the investigator developed a model with probabilistic modules capable of: predicting TdP risk, identifying LQT subtypes (scores ranging from 0 to 100%), and quantitatively measuring ECG parameters such as QTc, heart rate, PR, and QRS duration. The objective of this project is to prospectively validate our model in real-world conditions across various departments within AP-HP, for: Automatic measurement of QTc, and Identification and classification of LQT types and TdP risk.
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Concordance of QTc measurement between the reference method and deep-learning model at 500 ms threshold
Timeframe: Day 0