Clinical trial NCT05829993: Development of an Artificial Intelligence Algorithm to Detect Pathological Repolarization Disorders on the ECG and the Risk of Ventricular Arrhythmias
Official title: Development of an Artificial Intelligence Algorithm to Detect Pathological Repolarization Disorders on the ECG and the Risk of Ventricular Arrhythmias
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Cardiac Disease
Ventricular Arrythmia
Keywords: Artificial intelligence
ECG
Repolarization
QT
Ventricular arrhythmias
Lead sponsor: Assistance Publique - Hôpitaux de Paris
Sponsor class: OTHER
Collaborators: name: UMMISCO - Institute of Research for Development (IRD)
name: CoreLab Banook
First posted: 2023-04-26
Enrollment: 5000
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Hospitalized patients from various centres within the APHP (cardiology, internal medicine, rhythmology, clinical pharmacology, oncology, dermatology).

Brief 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.

Detailed 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.

Eligibility criteria: 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

Primary outcomes:
measure: Diagnostic property of an AI- deep learning model
timeFrame: Day 0
Secondary outcomes:
measure: Identification of patients with congenital long QT
timeFrame: Day 0
measure: Identification of patients with drug-induced acquired long QT
timeFrame: Day 0
measure: Measurement of ECG quantitative features
timeFrame: Day 0
Other outcomes:
Locations:
city: Paris
country: France
facility: Centre d'Investigation Clinique Paris-Est/Hôpital Pitié-Salpêtrière
Study officials:
affiliation: Assistance Publique - Hôpitaux de Paris
name: Joe-Elie SALEM, PU-PH
role: PRINCIPAL_INVESTIGATOR
References: