Development of an Artificial Intelligence Algorithm to Detect Pathological Repolarization Disorders on the ECG and the Risk of Ventricular Arrhythmias
Development of an Artificial Intelligence Algorithm to Detect Pathological Repolarization Disorders on the ECG and the Risk of Ventricular Arrhythmias
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.
Background Torsades de Pointes (TdP) are rare but potentially fatal ventricular arrhythmias promoted by a prolongation of ventricular repolarization, observed on electrocardiogram (ECG) as Long QT (LQT). This prolongation may result from either genetic or acquired alterations in cardiac ion channels.
The congenital forms of LQT (cLQT) have an estimated prevalence of 1 in 2,000 to 3,000 and are caused by mutations affecting specific ionic currents: cLQT-1, associated with decreased IKs current (KCNQ1 gene), cLQT-2, with decreased IKr current (KCNH2 gene), and cLQT-3, with increased late sodium current INaL (SCN5A gene).
Acquired or drug-induced LQT (diLQT) typically results from the inhibition of IKr by medications. Over 100 drugs currently on the market, including hydroxychloroquine and azithromycin, are known or suspected to prolong the QT interval, with some associated with up to 7% incidence of TdP.
QTc, measured from the beginning of the QRS complex to the end of the T wave and corrected for heart rate, is the principal biomarker used to assess repolarization duration and risk of TdP. A QTc ≥500 ms is associated with a significantly increased risk.
However, QTc measurement is subject to high inter- and intra-observer variability and lacks sufficient predictive performance to differentiate cLQT types or detect TdP risk in routine clinical settings.
Rationale Current clinical practices rely primarily on QTc measurement and genetic testing, which are not optimal in real-world conditions. Given the volume of digital ECGs now available, manual interpretation is increasingly limited.
Artificial Intelligence (AI), and particularly deep-learning models, have demonstrated promising results for ECG analysis beyond QTc alone.
The project team has developed and patented (AP-HP patent: WO/2020/245322) an AI model capable of:
automatically quantifying ECG parameters (QTc, PR, QRS, heart rate), generating probabilistic scores (0-100%) for congenital (cLQT-1, -2, -3) or drug-induced LQT, and estimating the risk of TdP.
This model showed strong retrospective performance. A prospective validation in clinical practice is now necessary.
Study Design Multicenter, cross-sectional, non-interventional study across 14 AP-HP hospitals over 42 months, enrolling 5,000 participants undergoing ECG as part of care or research. Data collected include digital ECGs (.xml), clinical history, medications, and genetic data if available.
Workflow Digital ECGs are anonymized and stored centrally. QTc values are determined using triplicate ECGs, threshold method, and Fridericia correction. AI analysis is then compared with reference. Associated clinical and genetic data enrich model evaluation.
Expected Impact If validated, this AI model could be integrated into ECG acquisition devices to enable real-time QTc measurement, risk classification, and better triage. This would improve detection and management of arrhythmogenic risk, especially in settings lacking rhythmology expertise.
Inclusion Criteria:
Exclusion Criteria:
joe-elie.salem@aphp.fr0033 1 42 17 85 35