AI-powered ECG Analysis for Deadly Arrhythmias and ICI Myocarditis

NCT07644715 | Recruiting

• 1 other identifier: CIC2503-26-05
• Study Type: observational
• Target: 127,000
• Locations: 1 country
• Sites: 1

Brief Summary

ELDORA is a non-interventional observational data-science study aiming to develop and validate clinical-grade artificial intelligence tools applied to electrocardiogram (ECG) data. The project will standardize heterogeneous ECGs, create the ECGInsight harmonized database, and train interpretable models for life-threatening arrhythmia risk prediction, especially Torsades-de-Pointes/long QT syndrome and immune checkpoint inhibitor (ICI)-induced myocarditis. The project uses existing and ongoing national and international ECG cohorts with de-identified clinical metadata; AI outputs are intended for research/model development and are not used to drive patient care during the study.

Conditions

Arrhythmia; Long QT Syndrome; Immune Checkpoint Inhibitor-Related Myocarditis

Keywords

ECG; AI; Immune Checkpoint Inhibitors-Related Myocarditis; long QT

Outcome Measures

Primary Outcomes (1)

• Performance of AI models for ECG-based prediction/diagnosis of life-threatening arrhythmia conditions: AUC

  Model discrimination performance assessed using the Area Under the Receiver Operating Characteristic Curve (AUC) for prediction of torsade de pointes (TdP)/long QT risk and immune checkpoint inhibitor (ICI)-myocarditis diagnosis, prognosis, and risk.

  Up to study completion (anticipated 48 months)

Secondary Outcomes (7)

• Creation and harmonization of the ECG Insight database across participating ECG cohorts

  Up to study completion (anticipated 48 months)

• Performance of ECG digitization/standardization toolkit for heterogeneous ECG data : Accuracy

  Up to study completion (anticipated 48 months)

• Performance of AI models for ECG-based prediction/diagnosis of life-threatening arrhythmia conditions: Sensitivity

  Up to study completion (anticipated 48 months)

• Performance of AI models for ECG-based prediction/diagnosis of life-threatening arrhythmia conditions: Specificity

  Up to study completion (anticipated 48 months

• Performance of AI models for ECG-based prediction/diagnosis of life-threatening arrhythmia conditions: F1 Score

  Up to study completion (anticipated 48 months)

Study Arms (1)

A unified dataset (ECGinsight) comprising at least 10 millions ECG

A unified dataset (ECGinsight) comprising at least 10 millions ECG spanning from multiple international setting and including healthy volunteers, LQT/TdP, cancer/ICI myocarditis, heart transplant, diabetes, obesity, hormonal and patients with cardiovascular comorbidities and events

Eligibility Criteria

• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Subjects from existing and ongoing ECG cohorts contributing to ECGInsight, including healthy volunteers and patients with cardiovascular diseases, cancer/ICI exposure, LQT/TdP and ICI-myocarditis-relevant phenotypes.

You may qualify if:

• subjects included in participating existing or ongoing ECG cohorts made available to ECGInsight
• availability of ECG data (digital waveform or scanned/paper ECG suitable for digitization) and relevant clinical/demographic metadata
• data use permitted by applicable ethical, regulatory, contractual and GDPR requirements.

You may not qualify if:

• datasets or individual records for which required approvals, data-sharing agreements, de-identification/anonymization, or minimum ECG/metadata quality requirements are not met. No interventional study treatment is assigned.

Sponsors & Collaborators

• Groupe Hospitalier Pitie-Salpetriere: lead
• Institut de Recherche pour le Developpement: collaborator
• Institut National de la Santé Et de la Recherche Médicale, France: collaborator
• Vanderbilt University Medical Center: collaborator
• University of California, San Francisco: collaborator
• University Hospital, Bordeaux: collaborator
• Assistance Publique - Hôpitaux de Paris: collaborator
• Banook Group: collaborator

Study Sites (1)

CIC-2503

Paris, 75013, France

RECRUITING

Related Publications (1)

• Prifti E, Fall A, Davogustto G, Pulini A, Denjoy I, Funck-Brentano C, Khan Y, Durand-Salmon A, Badilini F, Wells QS, Leenhardt A, Zucker JD, Roden DM, Extramiana F, Salem JE. Deep learning analysis of electrocardiogram for risk prediction of drug-induced arrhythmias and diagnosis of long QT syndrome. Eur Heart J. 2021 Oct 7;42(38):3948-3961. doi: 10.1093/eurheartj/ehab588.

  PMID: 34468739 BACKGROUND

MeSH Terms

Conditions

Arrhythmias, Cardiac; Long QT Syndrome

Condition Hierarchy (Ancestors)

Heart Diseases; Cardiovascular Diseases; Pathologic Processes; Pathological Conditions, Signs and Symptoms; Cardiac Conduction System Disease; Heart Defects, Congenital; Cardiovascular Abnormalities; Congenital Abnormalities; Congenital, Hereditary, and Neonatal Diseases and Abnormalities

Central Study Contacts

Joe-Elie Salem, MD-PhD

CONTACT

0033142178535; joe-elie.salem@aphp.fr

Edi Prifti, PhD

CONTACT

+33 1 48 02 55 20; edi.prifti@ird.fr

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: OTHER
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Clinical Professor, Clinical Investigation Center Paris Est

Study Record Dates

• First Submitted: June 2, 2026
• First Posted: June 12, 2026
• Study Start: January 1, 2026
• Primary Completion (Estimated): December 31, 2029
• Study Completion (Estimated): December 31, 2029
• Last Updated: June 12, 2026

Record last verified: 2026-05

Data Sharing

• IPD Sharing: Will not share

Locations

FR (1)