DEEPECG4U: Development of an Artificial Intelligence Algorithm to Detect Pathological Repolarization Disorders on the ECG and the Risk of Ventricular Arrhythmias
Study Details
Study Description
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 :
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automatic measurement of various ECG quantitative features,
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identification and typing of LQT and risk of TdP.
| Condition or Disease | Intervention/Treatment | Phase |
|---|---|---|
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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 :
-
automatic QTc measurement,
-
identification and typing of LQT and risk of TdP.
Study Design
Arms and Interventions
| Arm | Intervention/Treatment |
|---|---|
| Cohort patients with a clinical indication to perform an ECG |
Outcome Measures
Primary Outcome Measures
- Diagnostic property of an AI- deep learning model [Day 0]
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.
Secondary Outcome Measures
- Identification of patients with congenital long QT [Day 0]
Evaluate an AI-model for identification of patients with congenital long QT, and discriminate the type within a population of hospitalized patients
- Identification of patients with drug-induced acquired long QT [Day 0]
Evaluate an AI-model for identification of patients with drug-induced acquired long QT
- Measurement of ECG quantitative features [Day 0]
Evaluate an AI-model for measurements of QT, PR, QRS, heart rate and QTc.
Eligibility Criteria
Criteria
Inclusion Criteria:
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Age ≥ 18
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Patients or subjects taken care in recruiting centres for which an ECG is indicated
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No opposition to participation in the study
Exclusion Criteria:
- Medical contraindication for ECG
Contacts and Locations
Locations
| Site | City | State | Country | Postal Code | |
|---|---|---|---|---|---|
| 1 | Centre d'Investigation Clinique Paris-Est/Hôpital Pitié-Salpêtrière | Paris | France | 75013 |
Sponsors and Collaborators
- Assistance Publique - Hôpitaux de Paris
- UMMISCO - Institute of Research for Development (IRD)
- CoreLab Banook
Investigators
- Principal Investigator: Joe-Elie SALEM, PU-PH, Assistance Publique - Hôpitaux de Paris
Study Documents (Full-Text)
None provided.More Information
Publications
None provided.- APHP211441
- 2022-A01502-41