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

Cardiac Disease Clinical Trial

— DEEPECG4U  

Official title:

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

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05829993
• Other study ID #: APHP211441
• Secondary ID: 2022-A01502-41
• Status: Recruiting
• Start date: November 28, 2023
• Est. completion date: June 2024

Study information

• Verified date: December 2023
• Source: Assistance Publique - Hôpitaux de Paris
• Contact: Joe-Elie SALEM, PU-PH
• Phone: 0033 1 42 17 85 35
• Email: joe-elie.salem[@]aphp.fr
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

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 :

1. automatic measurement of various ECG quantitative features,

2. identification and typing of LQT and risk of TdP.

Description:

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 :

1. automatic QTc measurement,

2. identification and typing of LQT and risk of TdP.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 5000
• Est. completion date: June 2024
• Est. primary completion date: May 2024
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 18 Years and older

Eligibility

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

Related Conditions & MeSH terms

• Arrhythmias, Cardiac
• Cardiac Disease
• Heart Diseases
• Ventricular Arrythmia

Locations

Country	Name	City	State
France	Centre d'Investigation Clinique Paris-Est/Hôpital Pitié-Salpêtrière	Paris

Sponsors (3)

Lead Sponsor	Collaborator
Assistance Publique - Hôpitaux de Paris	CoreLab Banook, UMMISCO - Institute of Research for Development (IRD)

Country where clinical trial is conducted

France, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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	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
Secondary	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
Secondary	Measurement of ECG quantitative features	Evaluate an AI-model for measurements of QT, PR, QRS, heart rate and QTc.	Day 0