Arrhythmia, Immune Checkpoint Inhibitor-Related Myocarditis, Long QT Syndrome
Conditions
Keywords
ECG, AI, Immune Checkpoint Inhibitors-Related Myocarditis, long QT
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.
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."
Interventions
None listed
Sponsors
Groupe Hospitalier Pitie-Salpetriere
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 design
Observational model
COHORT
Time perspective
OTHER
Eligibility
Sex/Gender
ALL
Healthy volunteers
Yes
Inclusion criteria
* 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.
Exclusion criteria
* 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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Performance of AI models for ECG-based prediction/diagnosis of life-threatening arrhythmia conditions: AUC | 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. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Creation and harmonization of the ECG Insight database across participating ECG cohorts | Up to study completion (anticipated 48 months) | Consolidation, anonymization/de-identification, standardization and secure integration of ECG waveforms, annotations and clinical metadata from participating cohorts into ECGInsight. |
| Performance of ECG digitization/standardization toolkit for heterogeneous ECG data : Accuracy | Up to study completion (anticipated 48 months) | 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. |
| Performance of AI models for ECG-based prediction/diagnosis of life-threatening arrhythmia conditions: Sensitivity | Up to study completion (anticipated 48 months) | Sensitivity of the prediction models for TdP/long QT risk and ICI-myocarditis diagnosis, prognosis, and risk prediction. |
| Performance of AI models for ECG-based prediction/diagnosis of life-threatening arrhythmia conditions: Specificity | Up to study completion (anticipated 48 months | Specificity of the prediction models for TdP/long QT risk and ICI-myocarditis diagnosis, prognosis, and risk prediction. |
| Performance of AI models for ECG-based prediction/diagnosis of life-threatening arrhythmia conditions: F1 Score | Up to study completion (anticipated 48 months) | F1 score of the prediction models for TdP/long QT risk and ICI-myocarditis diagnosis, prognosis, and risk prediction. |
| Performance of AI models for ECG-based prediction/diagnosis of life-threatening arrhythmia conditions: Accuracy | Up to study completion (anticipated 48 months) | Accuracy of the prediction models for TdP/long QT risk and ICI-myocarditis diagnosis, prognosis, and risk prediction. |
| Performance of AI models for ECG-based prediction/diagnosis of life-threatening arrhythmia conditions: Regression / Agreement metrics | Up to study completion (anticipated 48 months) | Regression / Agreement metrics of the prediction models for TdP/long QT risk and ICI-myocarditis diagnosis, prognosis, and risk prediction. |
Countries
France
Contacts
CONTACTJoe-Elie Salem, MD-PhD
CONTACTEdi Prifti, PhD
Outcome results
None listed