Development of an Artificial Intelligence Algorithm to Detect Pathological Repolarization Disorders on the ECG and the Risk of Ventricular Arrhythmias

Study Overview
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Brief Summary
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The objective of this study is to prospectively validate in real life cohorts from various departments of the APHP our artificial intelligence (deep-learning) models allowing for : automatic measurement of various ECG quantitative features, identification and typing of LQT and risk of TdP.

Detailed Description
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Torsade-de-Pointes (TdP) are potentially fatal ventricular arrhythmias favored by a prolongation of ventricular repolarization (Long QT, LQT). The different types of existing LQT derive from the inhibition of cardiac potassium currents (IKr ; IKs) or the activation of a late sodium current (INaL). These alterations can be of congenital origin (3 types=>cLQT-1:IKs, cLQT-2:IKr, cLQT-3: INaL) or drug-induced (diLQT, via inhibition of IKr). More than 100 drugs have marketing authorization despite a risk of TdP because they have a favorable benefit/risk ratio (e.g. hydroxychloroquine). QTc, which represents the duration of ventricular repolarization (msec) and corresponds to the time between the beginning of the QRS and the end of the T-wave, corrected by heart rate, is prolonged in all LQT. Specific T-wave abnormalities as a function of the altered currents have been described and helps to discriminate cLQT/diLQT types. Thus, limiting the analysis of the ECG to that of the QTc is not very predictive because the information contained in an ECG is much richer and is not limited to the simple measurement of an interval. We have recently shown that analysis of ECGs using artificial intelligence (convolutional neural network, deep-learning) identifies elements of the ECG that are more discriminating in the prediction of the type of LQT and the risk of TdP, beyond of QTc. With these techniques, we have developed a model with probabilistic modules that predict the risk of TdP, identify the type of LQT (score ranging from 0 to 100%) and allow for the quantitative measurements of various common ECG parameters (including QTc, heart rate, PR and QRS). The objective of the project is to prospectively validate in real life cohorts from various departments of the APHP our model allowing for : automatic QTc measurement, identification and typing of LQT and risk of TdP.

Official Title
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Development of an Artificial Intelligence Algorithm to Detect Pathological Repolarization Disorders on the ECG and the Risk of Ventricular Arrhythmias

Conditions
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Cardiac Disease, Ventricular Arrythmia

Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Age ≥ 18 Patients or subjects taken care in recruiting centres for which an ECG is indicated No opposition to participation in the study Exclusion Criteria: Medical contraindication for ECG Subjects with pacemaker-driven QRS

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Cohort<br>patients with a clinical indication to perform an ECG | |

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Diagnostic property of an AI- deep learning model | Evaluate the diagnostic properties (specificity, sensitivity, positive predictive value, negative predictive value) of a deep-learning quantitative QTc measurement model with a standardized and validated expert measurement to identify patients with very pathological QTc (≥500msec) within a population of hospitalized patients from various centres. | Day 0 | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Identification of patients with congenital long QT | Evaluate an AI-model for identification of patients with congenital long QT, and discriminate the type within a population of hospitalized patients | Day 0 | 
| Identification of patients with drug-induced acquired long QT | Evaluate an AI-model for identification of patients with drug-induced acquired long QT | Day 0 | 
| Measurement of ECG quantitative features | Evaluate an AI-model for measurements of QT, PR, QRS, heart rate and QTc. | Day 0 | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Artificial intelligence, ECG, Repolarization, QT, Ventricular arrhythmias