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.
Age ≥ 18
Patients or subjects taken care in recruiting centres for which an ECG is indicated
No opposition to participation in the study
Medical contraindication for ECG
Subjects with pacemaker-driven QRS
Hospitalized patients from various centres within the APHP (cardiology, internal medicine, rhythmology, clinical pharmacology, oncology, dermatology).
Cohort
Prospective
Not listed
Concordance of QTc measurement between the reference method and deep-learning model at 500 ms threshold
Evaluate the concordance (Kappa coefficient, Κ) of QTc measurement between the reference method (triplicated averaged 10-second ECG complexes, "threshold" technique, Fridericia correction) and the deep-learning model in patients classified as having QTc ≥500 ms versus \<500 ms.
Diagnostic performance of AI-generated scores for congenital long QT types 1, 2, and 3
Evaluate sensitivity, specificity, positive predictive value, and negative predictive value (with 95% confidence intervals) for three AI-generated scores (0-100%) estimating the probability of congenital long QT types 1, 2, and 3 (cLQT-1: IKs KCNQ1, cLQT-2: IKr KCNH2, cLQT-3: INaL SCN5A). Scores will be compared between genetically confirmed patients and control subjects without cardiovascular disease, accounting for QT type, ongoing treatments, and time relative to major clinical events (e.g., Torsades de Pointes). Patients with AI scores \>50% without prior diagnosis will undergo genetic testing, which serves as the reference standard to confirm congenital long QT.
Diagnostic performance of AI score for drug-induced long QT
Evaluate sensitivity, specificity, positive predictive value, and negative predictive value with 95% confidence intervals for an AI-generated score (0 to 100%) estimating the probability of drug-induced long QT, defined by a QTc increase of more than 60 ms from baseline or drug-induced Torsades de Pointes, compared to control subjects without cardiovascular disease.
Accuracy of AI-derived quantitative ECG measurements
Compare AI-generated measurements of QT, QTc, heart rate, PR interval, and QRS duration with values obtained by reference standard methods at CIC-Paris Est.
Evaluation of standardized feature importance profile (FIP) for ECG segment discrimination
Evaluate the standardized feature importance profile (FIP) derived from the deep-learning model to identify ECG segments most discriminative among different patient groups.
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Assistance Publique - Hôpitaux de Paris
Lead sponsor
UMMISCO - Institute of Research for Development (IRD)
Collaborator
