An observational study in Arrhythmia, Long QT Syndrome and Immune Checkpoint Inhibitor-Related Myocarditis, sponsored by Groupe Hospitalier Pitie-Salpetriere. Recruiting at 1 site in France. Per ClinicalTrials.gov, last updated 2026-06-12.
Sponsored by Groupe Hospitalier Pitie-Salpetriere · Observational
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.
"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."
101 studies on the registry are indexed under Long QT Syndrome; 20 are open to participants now.
This study's planned enrollment of 127,000 is above the median of 150 across 53 observational studies indexed under Long QT Syndrome.
Browse Long QT Syndrome studies →Groupe Hospitalier Pitie-Salpetriere is the lead sponsor of 66 studies on the registry; 4 are open to participants now.
Compared within Long QT Syndrome, this study's primary condition as ClinicalTrials.gg classifies it; the registry's first condition term is Arrhythmias, Cardiac. The counts are of registry records indexed under each term, refreshed daily. How we classify →
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.
Go through the criteria one by one — yes, no or not sure — and take the list to your doctor or the study team. Kept in this browser only.
Exclusion Criteria:
Underlined terms in the criteria open these too. Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
A recording of the heart’s electrical activity, taken with stickers on the chest, arms and legs. It takes a few minutes and does not hurt.
ECG (electrocardiogram)
A recording of the heart’s electrical activity, taken with stickers on the chest, arms and legs. It takes a few minutes and does not hurt.
Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
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
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)
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)
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)
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)
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 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.
Time frame: 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.
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
Regression / Agreement metrics 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)
Plan to share: No
Started Jan 2026 (actual). Primary completion is estimated for Dec 2029, 47 months after the start.
Of 21 observational studies under Long QT Syndrome, half reached primary completion within 56 months of starting (the median), counting those still running; a quarter within 26 and three quarters within 81. This study's planned 47 months is below that.
How these figures are worked out, and which studies are left out of them.
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Groupe Hospitalier Pitie-Salpetriere