AI-powered ECG Analysis for Deadly Arrhythmias and ICI Myocarditis
Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
AgeNot listed
SponsorGroupe Hospitalier Pitie-Salpetriere
About this trial
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.
Eligibility criteria
This trial accepts healthy volunteersQualifiers
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.
Disqualifiers
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.
Trial 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.
Trial design
Design model
Cohort
Time perspective
Other
Treatments tested in this trial
Not listed
Trial groups
127,000 Participants
are grouped into 1 trial groupGroup A: A unified dataset (ECGinsight) comprising at least 10 millions ECG
Trial outcomes
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.
Time frame
Up to study completion (anticipated 48 months)Secondary outcomes
1
Creation and harmonization of the ECG Insight database across participating ECG cohorts
Consolidation, anonymization/de-identification, standardization and secure integration of ECG waveforms, annotations and clinical metadata from participating cohorts into ECGInsight.
Time frame
Up to study completion (anticipated 48 months)2
Performance of ECG digitization/standardization toolkit for heterogeneous ECG data : Accuracy
Accuracy of ECG digitization and standardization tools for conversion of analog/paper-derived and digital ECG data into analysis-ready formats, assessed by comparison with reference ECG signals.
Time frame
Up to study completion (anticipated 48 months)3
Performance of AI models for ECG-based prediction/diagnosis of life-threatening arrhythmia conditions: Sensitivity
Sensitivity of the prediction models for TdP/long QT risk and ICI-myocarditis diagnosis, prognosis, and risk prediction.
Time frame
Up to study completion (anticipated 48 months)4
Performance of AI models for ECG-based prediction/diagnosis of life-threatening arrhythmia conditions: Specificity
Specificity of the prediction models for TdP/long QT risk and ICI-myocarditis diagnosis, prognosis, and risk prediction.
Time frame
Up to study completion (anticipated 48 monthsSponsors and contacts
Click on the lead sponsor to view all of their trials.
Groupe Hospitalier Pitie-Salpetriere
Lead sponsor
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
Source ClinicalTrials.gov