ArrhythmiaELDORANCT07644715
AI-powered ECG Analysis for Deadly Arrhythmias and ICI Myocarditis
An observational study of Arrhythmia and Long QT Syndrome, run by Groupe Hospitalier Pitie-Salpetriere at 1 site in France.
Trial 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. Read more about this study
- Who is it for?
- Any ageHealthy volunteers can take part
- Conditions the study is for
- Arrhythmia, Long QT Syndrome, Immune Checkpoint Inhibitor-Related Myocarditis
- How many people
- 127,000 participants
- Where
- 1 siteFrance
- Study dates
- 1 Jan 2026to 31 Dec 2029
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- Healthy volunteers accepted
- Child, Adult, Older adult
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.
Who may be able to join
- 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.
Who cannot join
- 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 locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
About this study
Official title: Efficient Deep Learning Approaches for the Rapid and Interpretable Detection of Deadly Arrhythmias in ECG Data
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.
Read the detailed description
"ELDORA (Efficient Deep Learning Approaches for the Rapid and Interpretable Detection of Deadly Arrhythmias in ECG Data) is an observational, non-interventional project focused on ECG-based artificial intelligence. Its overarching objective is to develop and optimize clinical-grade AI-powered tools for: (1) digitizing, standardizing and analyzing heterogeneous ECG signals, including real-life analog/paper-derived and digital recordings; and (2) supporting clinical decision research for two sudden-cardiac-arrest-prone conditions: Torsades-de-Pointes (TdP) risk prediction in established long QT syndrome, whether congenital or drug-induced, and diagnosis, prognosis and risk prediction for immune checkpoint inhibitor-induced myocarditis.
The project will consolidate diverse ECG and clinical datasets into ECGInsight, a harmonized database planned to include approximately 49 national and international ECG cohorts, around 127,000 subjects and up to about 10 million 10-second ECG equivalents. Cohorts cover a broad spectrum of health states and cardiovascular conditions, including healthy volunteers, congenital and drug-induced long QT/TdP populations, cancer patients treated with immune checkpoint inhibitors with or without myocarditis, heart transplant, diabetes, obesity and hormonal phenotyping cohorts. Data include raw ECG waveforms, automatic and expert annotations, scanned paper ECGs where applicable, demographics, clinical characteristics, laboratory results, drug exposure and hormono-metabolic assessments near the time of ECG acquisition.
Data curation will include mapping of cohort variables and clinical concepts into an ELDORA glossary, using controlled terminologies where appropriate, including ICD-10, MedDRA, OMOP and ATC for drug exposure. ECGs will be standardized using the project toolkit and integrated in a secure, GDPR-compliant infrastructure. Access is intended to be controlled and limited to approved researchers/clinicians under the project governance. The study involves no treatment allocation, no investigational medicinal product and no direct AI-driven change to patient care. Model performance will be evaluated using standard classification and regression metrics, including AUC, sensitivity, specificity, F1 score, accuracy, MAE, RMSE, R2 and Bland-Altman analyses, as appropriate to each task."
- Keywords
- ECG, AI, Immune Checkpoint Inhibitors-Related Myocarditis, long QT
- Medical subject headings
- Arrhythmias, Cardiac, Long QT Syndrome
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| 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 |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: Up to study completion (anticipated 48 months)
Secondary outcomes (7)
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.
Measured: Up to study completion (anticipated 48 months)
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.
Measured: Up to study completion (anticipated 48 months)
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.
Measured: Up to study completion (anticipated 48 months)
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.
Measured: Up to study completion (anticipated 48 months
Performance of AI models for ECG-based prediction/diagnosis of life-threatening arrhythmia conditions: F1 Score
F1 score of the prediction models for TdP/long QT risk and ICI-myocarditis diagnosis, prognosis, and risk prediction.
Measured: Up to study completion (anticipated 48 months)
Performance of AI models for ECG-based prediction/diagnosis of life-threatening arrhythmia conditions: Accuracy
Accuracy of the prediction models for TdP/long QT risk and ICI-myocarditis diagnosis, prognosis, and risk prediction.
Measured: Up to study completion (anticipated 48 months)
Performance of AI models for ECG-based prediction/diagnosis of life-threatening arrhythmia conditions: Regression / Agreement metrics
Regression / Agreement metrics of the prediction models for TdP/long QT risk and ICI-myocarditis diagnosis, prognosis, and risk prediction.
Measured: Up to study completion (anticipated 48 months)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted12 Jun 2026
- Study start1 Jan 2026
- Primary completion31 Dec 2029 (estimated)
- Study completion31 Dec 2029 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for ArrhythmiaStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Other
- Enrolment
- 127,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Groupe Hospitalier Pitie-Salpetriere
- Sponsor type
- University, hospital or other
- Collaborators
- Institut de Recherche pour le Developpement, Institut National de la Santé Et de la Recherche Médicale, France, Vanderbilt University Medical Center, University of California, San Francisco, University Hospital, Bordeaux, Assistance Publique - Hôpitaux de Paris, Banook Group
- Responsible party
- Principal investigator, Joe Elie Salem, Groupe Hospitalier Pitie-Salpetriere
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07644715
- Sponsor's protocol ID
- CIC2503-26-05
- Acronym
- ELDORA
- Status last verified
- May 2026
- First submitted
- 2 Jun 2026
- Last updated
- 12 Jun 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
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.
PubMed 34468739
Study at a glance
From the registry record, last updated by the sponsor on 12 Jun 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Efficient Deep Learning Approaches for the Rapid and Interpretable Detection of Deadly Arrhythmias in ECG Data
- ClinicalTrials.gov ID
- NCT07644715
- Acronym / sponsor ID
- ELDORA / CIC2503-26-05
- Keywords
- ECG, AI, Immune Checkpoint Inhibitors-Related Myocarditis, long QT
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 127,000 participants (estimated)
- Lead sponsor
- Groupe Hospitalier Pitie-SalpetriereUniversity, hospital or other
- Collaborators
- Institut de Recherche pour le Developpement, Institut National de la Santé Et de la Recherche Médicale, France, Vanderbilt University Medical Center, University of California, San Francisco, University Hospital, Bordeaux, Assistance Publique - Hôpitaux de Paris, Banook Group
- Study start
- 1 Jan 2026 (actual)
- Primary completion
- 31 Dec 2029 (estimated)
- Study completion
- 31 Dec 2029 (estimated)
- First posted
- 12 Jun 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
"ELDORA (Efficient Deep Learning Approaches for the Rapid and Interpretable Detection of Deadly Arrhythmias in ECG Data) is an observational, non-interventional project focused on ECG-based artificial intelligence. Its overarching objective is to develop and optimize clinical-grade AI-powered tools for: (1) digitizing, standardizing and analyzing heterogeneous ECG signals, including real-life analog/paper-derived and digital recordings; and (2) supporting clinical decision research for two sudden-cardiac-arrest-prone conditions: Torsades-de-Pointes (TdP) risk prediction in established long QT syndrome, whether congenital or drug-induced, and diagnosis, prognosis and risk prediction for immune checkpoint inhibitor-induced myocarditis.
The project will consolidate diverse ECG and clinical datasets into ECGInsight, a harmonized database planned to include approximately 49 national and international ECG cohorts, around 127,000 subjects and up to about 10 million 10-second ECG equivalents. Cohorts cover a broad spectrum of health states and cardiovascular conditions, including healthy volunteers, congenital and drug-induced long QT/TdP populations, cancer patients treated with immune checkpoint inhibitors with or without myocarditis, heart transplant, diabetes, obesity and hormonal phenotyping cohorts. Data include raw ECG waveforms, automatic and expert annotations, scanned paper ECGs where applicable, demographics, clinical characteristics, laboratory results, drug exposure and hormono-metabolic assessments near the time of ECG acquisition.
Data curation will include mapping of cohort variables and clinical concepts into an ELDORA glossary, using controlled terminologies where appropriate, including ICD-10, MedDRA, OMOP and ATC for drug exposure. ECGs will be standardized using the project toolkit and integrated in a secure, GDPR-compliant infrastructure. Access is intended to be controlled and limited to approved researchers/clinicians under the project governance. The study involves no treatment allocation, no investigational medicinal product and no direct AI-driven change to patient care. Model performance will be evaluated using standard classification and regression metrics, including AUC, sensitivity, specificity, F1 score, accuracy, MAE, RMSE, R2 and Bland-Altman analyses, as appropriate to each task."
Study design
- Observational model
- Cohort
- Time perspective
- Other
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| 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 |
Who may qualify
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
- 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.
Key inclusion criteria (3 listed)
- 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.
Key exclusion criteria (1 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Performance of AI models for ECG-based prediction/diagnosis of life-threatening arrhythmia conditions: AUC
Time frame: Up to study completion (anticipated 48 months)
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.
7 secondary outcomes
Creation and harmonization of the ECG Insight database across participating ECG cohorts
Time frame: Up to study completion (anticipated 48 months)
Performance of ECG digitization/standardization toolkit for heterogeneous ECG data : Accuracy
Time frame: Up to study completion (anticipated 48 months)
Performance of AI models for ECG-based prediction/diagnosis of life-threatening arrhythmia conditions: Sensitivity
Time frame: Up to study completion (anticipated 48 months)
Performance of AI models for ECG-based prediction/diagnosis of life-threatening arrhythmia conditions: Specificity
Time frame: Up to study completion (anticipated 48 months
Performance of AI models for ECG-based prediction/diagnosis of life-threatening arrhythmia conditions: F1 Score
Time frame: Up to study completion (anticipated 48 months)
Performance of AI models for ECG-based prediction/diagnosis of life-threatening arrhythmia conditions: Accuracy
Time frame: Up to study completion (anticipated 48 months)
Performance of AI models for ECG-based prediction/diagnosis of life-threatening arrhythmia conditions: Regression / Agreement metrics
Time frame: Up to study completion (anticipated 48 months)
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 12 Jun 2026.
- Background: PubMed 34468739Prifti 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.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Arrhythmia and Long QT Syndrome, run by Groupe Hospitalier Pitie-Salpetriere at 1 site in France.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 12 Jun 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in France. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Joe-Elie Salem, MD-PhD (0033142178535, joe-elie.salem@aphp.fr), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Performance of AI models for ECG-based prediction/diagnosis of life-threatening arrhythmia conditions: AUC (time frame: Up to study completion (anticipated 48 months)). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07644715, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 12 Jun 2026.