Cardiac DiseaseDEEPECG4UNCT05829993
Development of an Artificial Intelligence Algorithm to Detect Pathological Repolarization Disorders on the ECG and the Risk of Ventricular Arrhythmias
An observational study of Cardiac Disease and Ventricular Arrythmia, run by Assistance Publique - Hôpitaux de Paris at 1 site in France.
Trial summary
Torsades de Pointes (TdP) are potentially fatal ventricular arrhythmias that are promoted by prolonged ventricular repolarization (Long QT, LQT). The different forms of LQT result from inhibition of cardiac potassium currents (IKr and IKs) or activation of a late sodium current (INaL). Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Cardiac Disease, Ventricular Arrythmia
- How many people
- 5,000 participants
- Where
- 1 siteFrance
- Study dates
- 28 Nov 2023to 28 Jun 2027
- 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 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: Hospitalized patients from various centres within the APHP (cardiology, internal medicine, rhythmology, clinical pharmacology, oncology, dermatology).
Who may be able to join
- Age ≥ 18
- Patients or subjects taken care in recruiting centres for which an ECG is indicated
- No opposition to participation in the study
Who cannot join
- Medical contraindication for ECG
- Subjects with pacemaker-driven QRS
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
France1 site, 1 recruiting
- Recruiting
Centre d'Investigation Clinique Paris-Est/Hôpital Pitié-Salpêtrière
Paris, 75013
Joe-Elie SALEM, PU-PH, 00 33 1 42 17 85 35, joe-elie.salem@aphp.fr
About this study
Torsades de Pointes (TdP) are potentially fatal ventricular arrhythmias that are promoted by prolonged ventricular repolarization (Long QT, LQT). The different forms of LQT result from inhibition of cardiac potassium currents (IKr and IKs) or activation of a late sodium current (INaL). These alterations may be either congenital (3 types: cLQT-1: IKs, cLQT-2: IKr, cLQT-3: INaL) or drug-induced (diLQT, via inhibition of IKr).
More than 100 medications have received marketing authorization despite a known risk of TdP, due to a favorable benefit-risk ratio (e.g., hydroxychloroquine).
Read more
QTc, which represents the duration of ventricular repolarization (in milliseconds) - defined as the time from the beginning of the QRS complex to the end of the T wave, corrected for heart rate - is prolonged in all forms of LQT.
Specific T-wave abnormalities, depending on the altered ion currents, have been described and can help differentiate the various types of congenital or drug-induced LQT.
However, screening for LQT and TdP risk, both at the individual and population levels, currently relies mainly on isolated QTc evaluation and genetic testing, which often takes considerable time to return.
Thus, limiting ECG analysis to QTc measurement alone offers low predictive value, as the ECG contains a wealth of additional information beyond a single interval.
The investigator recently demonstrated that artificial intelligence (AI)-based ECG analysis using deep-learning convolutional neural networks can detect more discriminative features of the ECG for predicting the type of LQT and the risk of TdP, going beyond QTc alone.
Using these techniques, the investigator developed a model with probabilistic modules capable of: predicting TdP risk, identifying LQT subtypes (scores ranging from 0 to 100%), and quantitatively measuring ECG parameters such as QTc, heart rate, PR, and QRS duration.
The objective of this project is to prospectively validate our model in real-world conditions across various departments within AP-HP, for:
Automatic measurement of QTc, and Identification and classification of LQT types and TdP risk.
Read the detailed description
Background Torsades de Pointes (TdP) are rare but potentially fatal ventricular arrhythmias promoted by a prolongation of ventricular repolarization, observed on electrocardiogram (ECG) as Long QT (LQT). This prolongation may result from either genetic or acquired alterations in cardiac ion channels.
The congenital forms of LQT (cLQT) have an estimated prevalence of 1 in 2,000 to 3,000 and are caused by mutations affecting specific ionic currents: cLQT-1, associated with decreased IKs current (KCNQ1 gene), cLQT-2, with decreased IKr current (KCNH2 gene), and cLQT-3, with increased late sodium current INaL (SCN5A gene).
Acquired or drug-induced LQT (diLQT) typically results from the inhibition of IKr by medications. Over 100 drugs currently on the market, including hydroxychloroquine and azithromycin, are known or suspected to prolong the QT interval, with some associated with up to 7% incidence of TdP.
QTc, measured from the beginning of the QRS complex to the end of the T wave and corrected for heart rate, is the principal biomarker used to assess repolarization duration and risk of TdP. A QTc ≥500 ms is associated with a significantly increased risk.
However, QTc measurement is subject to high inter- and intra-observer variability and lacks sufficient predictive performance to differentiate cLQT types or detect TdP risk in routine clinical settings.
Rationale Current clinical practices rely primarily on QTc measurement and genetic testing, which are not optimal in real-world conditions. Given the volume of digital ECGs now available, manual interpretation is increasingly limited.
Artificial Intelligence (AI), and particularly deep-learning models, have demonstrated promising results for ECG analysis beyond QTc alone.
The project team has developed and patented (AP-HP patent: WO/2020/245322) an AI model capable of:
automatically quantifying ECG parameters (QTc, PR, QRS, heart rate), generating probabilistic scores (0-100%) for congenital (cLQT-1, -2, -3) or drug-induced LQT, and estimating the risk of TdP.
This model showed strong retrospective performance. A prospective validation in clinical practice is now necessary.
Study Design Multicenter, cross-sectional, non-interventional study across 14 AP-HP hospitals over 42 months, enrolling 5,000 participants undergoing ECG as part of care or research. Data collected include digital ECGs (.xml), clinical history, medications, and genetic data if available.
Workflow Digital ECGs are anonymized and stored centrally. QTc values are determined using triplicate ECGs, threshold method, and Fridericia correction. AI analysis is then compared with reference. Associated clinical and genetic data enrich model evaluation.
Expected Impact If validated, this AI model could be integrated into ECG acquisition devices to enable real-time QTc measurement, risk classification, and better triage. This would improve detection and management of arrhythmogenic risk, especially in settings lacking rhythmology expertise.
- Conditions
- Cardiac Disease, Ventricular Arrythmia
- Keywords
- Artificial intelligence, ECG, Repolarization, QT, Ventricular arrhythmias
- Medical subject headings
- Heart Diseases
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 |
|---|---|---|
| Cohort | patients with a clinical indication to perform an ECG |
What is being measured
The primary outcome is the main question the study is designed to answer.
Concordance of QTc measurement between the reference method and deep-learning model at 500 ms threshold
Evaluate the concordance (Kappa coefficient, Κ) of QTc measurement between the reference method (triplicated averaged 10-second ECG complexes, "threshold" technique, Fridericia correction) and the deep-learning model in patients classified as having QTc ≥500 ms versus <500 ms.
Measured: Day 0
Secondary outcomes (4)
Diagnostic performance of AI-generated scores for congenital long QT types 1, 2, and 3
Evaluate sensitivity, specificity, positive predictive value, and negative predictive value (with 95% confidence intervals) for three AI-generated scores (0-100%) estimating the probability of congenital long QT types 1, 2, and 3 (cLQT-1: IKs KCNQ1, cLQT-2: IKr KCNH2, cLQT-3: INaL SCN5A). Scores will be compared between genetically confirmed patients and control subjects without cardiovascular disease, accounting for QT type, ongoing treatments, and time relative to major clinical events (e.g., Torsades de Pointes). Patients with AI scores >50% without prior diagnosis will undergo genetic testing, which serves as the reference standard to confirm congenital long QT.
Measured: Day 0
Diagnostic performance of AI score for drug-induced long QT
Evaluate sensitivity, specificity, positive predictive value, and negative predictive value with 95% confidence intervals for an AI-generated score (0 to 100%) estimating the probability of drug-induced long QT, defined by a QTc increase of more than 60 ms from baseline or drug-induced Torsades de Pointes, compared to control subjects without cardiovascular disease.
Measured: Day 0
Accuracy of AI-derived quantitative ECG measurements
Compare AI-generated measurements of QT, QTc, heart rate, PR interval, and QRS duration with values obtained by reference standard methods at CIC-Paris Est.
Measured: Day 0
Evaluation of standardized feature importance profile (FIP) for ECG segment discrimination
Evaluate the standardized feature importance profile (FIP) derived from the deep-learning model to identify ECG segments most discriminative among different patient groups.
Measured: Day 0
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted26 Apr 2023
- Study start28 Nov 2023
- Primary completion28 May 2027 (estimated)
- Study completion28 Jun 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Cardiac DiseaseStudies of the same condition, open and under way.
- More trials run by Assistance Publique - Hôpitaux de ParisThe sponsor's other registered studies.
- 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
- Prospective
- Enrolment
- 5,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Assistance Publique - Hôpitaux de Paris
- Sponsor type
- University, hospital or other
- Collaborators
- UMMISCO - Institute of Research for Development (IRD)
- Responsible party
- Sponsor
- Investigators
- Joe-Elie SALEM, PU-PH, Principal investigator, Assistance Publique - Hôpitaux de Paris
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05829993
- Sponsor's protocol ID
- APHP211441
- Other IDs
- 2022-A01502-41
- Acronym
- DEEPECG4U
- Status last verified
- Jul 2025
- First submitted
- 13 Apr 2023
- Last updated
- 31 Jul 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: Individual participant data (IPD), including digital ECG files (.xml), pseudonymized clinical and demographic data, treatments, ECG parameters (QTc, PR, QRS, heart rate), as well as genetic and PMSI-coded data when relevant, will be shared. Data will be available after database lock and completion of the primary analysis. IPD will be transferred via a secure online platform managed by CIC-Paris Est and shared with the artificial intelligence research team at UMISCO (Dr. Edi Prifti) for scientific analysis. All data will be pseudonymized prior to transfer. Any use or transmission to third parties requires prior approval from the sponsor (AP-HP).
Study at a glance
From the registry record, last updated by the sponsor on 31 Jul 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development of an Artificial Intelligence Algorithm to Detect Pathological Repolarization Disorders on the ECG and the Risk of Ventricular Arrhythmias
- ClinicalTrials.gov ID
- NCT05829993
- Acronym / sponsor ID
- DEEPECG4U / APHP211441
- Conditions
- Cardiac Disease, Ventricular Arrythmia
- Keywords
- Artificial intelligence, ECG, Repolarization, QT, Ventricular arrhythmias
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 5,000 participants (estimated)
- Lead sponsor
- Assistance Publique - Hôpitaux de ParisUniversity, hospital or other
- Collaborators
- UMMISCO - Institute of Research for Development (IRD)
- Study start
- 28 Nov 2023 (actual)
- Primary completion
- 28 May 2027 (estimated)
- Study completion
- 28 Jun 2027 (estimated)
- First posted
- 26 Apr 2023 (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.
Torsades de Pointes (TdP) are potentially fatal ventricular arrhythmias that are promoted by prolonged ventricular repolarization (Long QT, LQT). The different forms of LQT result from inhibition of cardiac potassium currents (IKr and IKs) or activation of a late sodium current (INaL). These alterations may be either congenital (3 types: cLQT-1: IKs, cLQT-2: IKr, cLQT-3: INaL) or drug-induced (diLQT, via inhibition of IKr).
More than 100 medications have received marketing authorization despite a known risk of TdP, due to a favorable benefit-risk ratio (e.g., hydroxychloroquine).
QTc, which represents the duration of ventricular repolarization (in milliseconds) - defined as the time from the beginning of the QRS complex to the end of the T wave, corrected for heart rate - is prolonged in all forms of LQT.
Specific T-wave abnormalities, depending on the altered ion currents, have been described and can help differentiate the various types of congenital or drug-induced LQT.
However, screening for LQT and TdP risk, both at the individual and population levels, currently relies mainly on isolated QTc evaluation and genetic testing, which often takes considerable time to return.
Thus, limiting ECG analysis to QTc measurement alone offers low predictive value, as the ECG contains a wealth of additional information beyond a single interval.
The investigator recently demonstrated that artificial intelligence (AI)-based ECG analysis using deep-learning convolutional neural networks can detect more discriminative features of the ECG for predicting the type of LQT and the risk of TdP, going beyond QTc alone.
Using these techniques, the investigator developed a model with probabilistic modules capable of: predicting TdP risk, identifying LQT subtypes (scores ranging from 0 to 100%), and quantitatively measuring ECG parameters such as QTc, heart rate, PR, and QRS duration.
The objective of this project is to prospectively validate our model in real-world conditions across various departments within AP-HP, for:
Automatic measurement of QTc, and Identification and classification of LQT types and TdP risk.
Detailed description
Background Torsades de Pointes (TdP) are rare but potentially fatal ventricular arrhythmias promoted by a prolongation of ventricular repolarization, observed on electrocardiogram (ECG) as Long QT (LQT). This prolongation may result from either genetic or acquired alterations in cardiac ion channels.
The congenital forms of LQT (cLQT) have an estimated prevalence of 1 in 2,000 to 3,000 and are caused by mutations affecting specific ionic currents: cLQT-1, associated with decreased IKs current (KCNQ1 gene), cLQT-2, with decreased IKr current (KCNH2 gene), and cLQT-3, with increased late sodium current INaL (SCN5A gene).
Acquired or drug-induced LQT (diLQT) typically results from the inhibition of IKr by medications. Over 100 drugs currently on the market, including hydroxychloroquine and azithromycin, are known or suspected to prolong the QT interval, with some associated with up to 7% incidence of TdP.
QTc, measured from the beginning of the QRS complex to the end of the T wave and corrected for heart rate, is the principal biomarker used to assess repolarization duration and risk of TdP. A QTc ≥500 ms is associated with a significantly increased risk.
However, QTc measurement is subject to high inter- and intra-observer variability and lacks sufficient predictive performance to differentiate cLQT types or detect TdP risk in routine clinical settings.
Rationale Current clinical practices rely primarily on QTc measurement and genetic testing, which are not optimal in real-world conditions. Given the volume of digital ECGs now available, manual interpretation is increasingly limited.
Artificial Intelligence (AI), and particularly deep-learning models, have demonstrated promising results for ECG analysis beyond QTc alone.
The project team has developed and patented (AP-HP patent: WO/2020/245322) an AI model capable of:
automatically quantifying ECG parameters (QTc, PR, QRS, heart rate), generating probabilistic scores (0-100%) for congenital (cLQT-1, -2, -3) or drug-induced LQT, and estimating the risk of TdP.
This model showed strong retrospective performance. A prospective validation in clinical practice is now necessary.
Study Design Multicenter, cross-sectional, non-interventional study across 14 AP-HP hospitals over 42 months, enrolling 5,000 participants undergoing ECG as part of care or research. Data collected include digital ECGs (.xml), clinical history, medications, and genetic data if available.
Workflow Digital ECGs are anonymized and stored centrally. QTc values are determined using triplicate ECGs, threshold method, and Fridericia correction. AI analysis is then compared with reference. Associated clinical and genetic data enrich model evaluation.
Expected Impact If validated, this AI model could be integrated into ECG acquisition devices to enable real-time QTc measurement, risk classification, and better triage. This would improve detection and management of arrhythmogenic risk, especially in settings lacking rhythmology expertise.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Cohort | – | –patients with a clinical indication to perform an ECG |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- Hospitalized patients from various centres within the APHP (cardiology, internal medicine, rhythmology, clinical pharmacology, oncology, dermatology).
Key inclusion criteria (3 listed)
- Age ≥ 18
- Patients or subjects taken care in recruiting centres for which an ECG is indicated
- No opposition to participation in the study
Key exclusion criteria (2 listed)
- Medical contraindication for ECG
- Subjects with pacemaker-driven QRS
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
Concordance of QTc measurement between the reference method and deep-learning model at 500 ms threshold
Time frame: Day 0
Evaluate the concordance (Kappa coefficient, Κ) of QTc measurement between the reference method (triplicated averaged 10-second ECG complexes, "threshold" technique, Fridericia correction) and the deep-learning model in patients classified as having QTc ≥500 ms versus <500 ms.
4 secondary outcomes
Diagnostic performance of AI-generated scores for congenital long QT types 1, 2, and 3
Time frame: Day 0
Diagnostic performance of AI score for drug-induced long QT
Time frame: Day 0
Accuracy of AI-derived quantitative ECG measurements
Time frame: Day 0
Evaluation of standardized feature importance profile (FIP) for ECG segment discrimination
Time frame: Day 0
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 31 Jul 2025.
- Individual participant dataSharing planned: Yes. Individual participant data (IPD), including digital ECG files (.xml), pseudonymized clinical and demographic data, treatments, ECG parameters (QTc, PR, QRS, heart rate), as well as genetic and PMSI-coded data when relevant, will be shared. Data will be available after database lock and completion of the primary analysis. IPD will be transferred via a secure online platform managed by CIC-Paris Est and shared with the artificial intelligence research team at UMISCO (Dr. Edi Prifti) for scientific analysis. All data will be pseudonymized prior to transfer. Any use or transmission to third parties requires prior approval from the sponsor (AP-HP).
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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.
Joe-Elie SALEM, PU-PH principal investigator
Assistance Publique - Hôpitaux de Paris
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 Cardiac Disease and Ventricular Arrythmia, run by Assistance Publique - Hôpitaux de Paris 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 2 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 31 Jul 2025. 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, PU-PH (0033 1 42 17 85 35, 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?
Concordance of QTc measurement between the reference method and deep-learning model at 500 ms threshold (time frame: Day 0). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
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 31 Jul 2025.