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

NCT05829993 | Recruiting

• 2 other identifiers: APHP2114412022-A01502-41
• Study Type: observational
• Target: 5,000
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Cardiac Disease; Ventricular Arrythmia

Keywords

Artificial intelligence; ECG; Repolarization; QT; Ventricular arrhythmias

Outcome Measures

Primary Outcomes (1)

• Concordance of QTc measurement between the reference method and deep-learning model at 500 ms threshold

  Evaluate the concordance (Kappa coefficient, Κ) of QTc measurement between the reference method (triplicated averaged 10-second ECG complexes, "threshold" technique, Fridericia correction) and the deep-learning model in patients classified as having QTc ≥500 ms versus \<500 ms.

  Day 0

Secondary Outcomes (4)

• Diagnostic performance of AI-generated scores for congenital long QT types 1, 2, and 3

  Day 0

• Diagnostic performance of AI score for drug-induced long QT

  Day 0

• Accuracy of AI-derived quantitative ECG measurements

  Day 0

• Evaluation of standardized feature importance profile (FIP) for ECG segment discrimination

  Day 0

Study Arms (1)

Cohort

patients with a clinical indication to perform an ECG

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Hospitalized patients from various centres within the APHP (cardiology, internal medicine, rhythmology, clinical pharmacology, oncology, dermatology).

You may qualify if:

• Age ≥ 18
• Patients or subjects taken care in recruiting centres for which an ECG is indicated
• No opposition to participation in the study

You may not qualify if:

• Medical contraindication for ECG
• Subjects with pacemaker-driven QRS

Sponsors & Collaborators

• Assistance Publique - Hôpitaux de Paris: lead
• UMMISCO - Institute of Research for Development (IRD): collaborator

Study Sites (1)

Centre d'Investigation Clinique Paris-Est/Hôpital Pitié-Salpêtrière

Paris, 75013, France

RECRUITING

MeSH Terms

Conditions

Heart Diseases

Condition Hierarchy (Ancestors)

Cardiovascular Diseases

Study Officials

• Joe-Elie SALEM, PU-PH

  Assistance Publique - Hôpitaux de Paris

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Joe-Elie SALEM, PU-PH

CONTACT

0033 1 42 17 85 35; joe-elie.salem@aphp.fr

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: April 13, 2023
• First Posted: April 26, 2023
• Study Start: November 28, 2023
• Primary Completion (Estimated): May 28, 2027
• Study Completion (Estimated): June 28, 2027
• Last Updated: July 31, 2025

Record last verified: 2025-07

Data Sharing

• IPD Sharing: Will share

Locations

FR (1)