Left Ventricular Systolic DysfunctionNCT06231797
AI-ECG Screening for Left Ventricular Systolic Dysfunction
An observational study of Left Ventricular Systolic Dysfunction, run by Seoul National University Hospital.
Trial summary
The purpose of the current study is to verify the effectiveness of the artificial intelligence algorithm applied to the electrocardiogram as a potential screening tool for left ventricular systolic dysfunction. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Left Ventricular Systolic Dysfunction
- What is being tested?
- AI algorithm conducted on 12-lead ECG and transthoracic echocardiography
- How many people
- 1,530 participants
- Where
- No sites listed yet
- Study dates
- 1 Feb 2024to 10 Jul 2025
- Phase
- Observational
- Are there results?
- None posted in the registry
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
- Adult, Older adult
Study population: Patients undergoing 12-lead ECG and transthoracic echocardiography in routine clinical practice
Who may be able to join
- Individuals or those whose legal representative agree to participate in the study, and sign the consent form
- Can complete both 12-lead electrocardiogram and transthoracic echocardiography
Who cannot join
- Individuals whose age is less than 18 year-old.
- Individuals who do not agree to participate in the study
- Patients who are unable to participate in clinical trials at the discretion of the investigator
Trial locations and contacts
No sites are listed yet.
About this study
Official title: AI-ECG Screening for Left Ventricular Systolic Dysfunction: A Prospective, Observational, Multicenter Study
The purpose of the current study is to verify the effectiveness of the artificial intelligence algorithm applied to the electrocardiogram as a potential screening tool for left ventricular systolic dysfunction.
Read the detailed description
The current investigators have developed an artificial intelligence (AI) algorithm based on 12-lead electrocardiogram (ECG) detecting left ventricular systolic dysfunction, through 364,845 ECGs from 148,547 patients. Then, when the model was tested retrospectively on 59,805 ECGs of 24,376 patients, the model performance expressed as an area under the receiver operating characteristic curve was 0.889 (95% CI 0.887-0.891).
The investigators are planning to prospectively validate the model's effectiveness as a potential screening tool for left ventricular systolic dysfunction.
- Conditions
- Left Ventricular Systolic Dysfunction
- Being tested
- AI algorithm conducted on 12-lead ECG and transthoracic echocardiography
- Medical subject headings
- Ventricular Dysfunction, Left
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI algorithm conducted on 12-lead ECG and transthoracic echocardiographyDiagnostic test
12-lead ECG is performed for each patient. For 12-lead ECG, AITIALVSD (AI algorithm) analysis will be performed through a separate server.
What is being measured
The primary outcome is the main question the study is designed to answer.
Area under the receiver operating characteristic curve (AUROC)
AI model performance detecting LVSD, expressed as an AUROC. As a diagnostic assistance for LVSD, an ROC curve expressed as sensitivity to (1-specificity) will be presented, and the accuracy of prediction will be confirmed by calculating the AUROC, which is the area below.
Measured: Through study completion, an average of 1 year
Secondary outcomes (4)
Sensitivity
AI model sensitivity detecting LVSD
Measured: Through study completion, an average of 1 year
Specificity
AI model sensitivity detecting patients with normal left ventricular systolic function
Measured: Through study completion, an average of 1 year
Positive predictive value
Positive predictive value in the recruited patient population
Measured: Through study completion, an average of 1 year
Negative predictive value
Negative predictive value in the recruited patient population
Measured: Through study completion, an average of 1 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted30 Jan 2024
- Study start1 Feb 2024 (estimated)
- Primary completion10 Jul 2024 (estimated)
- Study completion10 Jul 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (2)Papers the sponsor has linked to this study.
- Other trials for Left Ventricular Systolic DysfunctionStudies of the same condition, open and under way.
- More trials run by Seoul National University HospitalThe 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
- Other
- Time perspective
- Prospective
- Enrolment
- 1,530 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Seoul National University Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Seung-Pyo Lee, MD, PhD, Principal investigator, Seoul National University Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06231797
- Sponsor's protocol ID
- H-2306-083-1439
- Status last verified
- Oct 2023
- First submitted
- 2 Nov 2023
- Last updated
- 5 Feb 2024
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Kwon JM, Jo YY, Lee SY, Kang S, Lim SY, Lee MS, Kim KH. Artificial Intelligence-Enhanced Smartwatch ECG for Heart Failure-Reduced Ejection Fraction Detection by Generating 12-Lead ECG. Diagnostics (Basel). 2022 Mar 8;12(3):654. doi: 10.3390/diagnostics12030654.
PubMed 35328207Kwon JM, Kim KH, Jeon KH, Kim HM, Kim MJ, Lim SM, Song PS, Park J, Choi RK, Oh BH. Development and Validation of Deep-Learning Algorithm for Electrocardiography-Based Heart Failure Identification. Korean Circ J. 2019 Jul;49(7):629-639. doi: 10.4070/kcj.2018.0446. Epub 2019 Mar 21.
PubMed 31074221
Study at a glance
From the registry record, last updated by the sponsor on 5 Feb 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- AI-ECG Screening for Left Ventricular Systolic Dysfunction: A Prospective, Observational, Multicenter Study
- ClinicalTrials.gov ID
- NCT06231797
- Acronym / sponsor ID
- H-2306-083-1439
- Conditions
- Left Ventricular Systolic Dysfunction
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 1,530 participants (estimated)
- Lead sponsor
- Seoul National University HospitalUniversity, hospital or other
- Study start
- 1 Feb 2024 (estimated)
- Primary completion
- 10 Jul 2024 (estimated)
- Study completion
- 10 Jul 2025 (estimated)
- First posted
- 30 Jan 2024 (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.
The purpose of the current study is to verify the effectiveness of the artificial intelligence algorithm applied to the electrocardiogram as a potential screening tool for left ventricular systolic dysfunction.
Detailed description
The current investigators have developed an artificial intelligence (AI) algorithm based on 12-lead electrocardiogram (ECG) detecting left ventricular systolic dysfunction, through 364,845 ECGs from 148,547 patients. Then, when the model was tested retrospectively on 59,805 ECGs of 24,376 patients, the model performance expressed as an area under the receiver operating characteristic curve was 0.889 (95% CI 0.887-0.891).
The investigators are planning to prospectively validate the model's effectiveness as a potential screening tool for left ventricular systolic dysfunction.
Study design
- Observational model
- Other
- Time perspective
- Prospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Age groups
- Adult, Older adult
- Study population
- Patients undergoing 12-lead ECG and transthoracic echocardiography in routine clinical practice
Key inclusion criteria (2 listed)
- Individuals or those whose legal representative agree to participate in the study, and sign the consent form
- Can complete both 12-lead electrocardiogram and transthoracic echocardiography
Key exclusion criteria (3 listed)
- Individuals whose age is less than 18 year-old.
- Individuals who do not agree to participate in the study
- Patients who are unable to participate in clinical trials at the discretion of the investigator
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| AI algorithm conducted on 12-lead ECG and transthoracic echocardiography12-lead ECG is performed for each patient. For 12-lead ECG, AITIALVSD (AI algorithm) analysis will be performed through a separate server. | Diagnostic test | – |
Primary outcome
Area under the receiver operating characteristic curve (AUROC)
Time frame: Through study completion, an average of 1 year
AI model performance detecting LVSD, expressed as an AUROC. As a diagnostic assistance for LVSD, an ROC curve expressed as sensitivity to (1-specificity) will be presented, and the accuracy of prediction will be confirmed by calculating the AUROC, which is the area below.
4 secondary outcomes
Sensitivity
Time frame: Through study completion, an average of 1 year
Specificity
Time frame: Through study completion, an average of 1 year
Positive predictive value
Time frame: Through study completion, an average of 1 year
Negative predictive value
Time frame: Through study completion, an average of 1 year
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 5 Feb 2024.
- Background: PubMed 35328207Kwon JM, Jo YY, Lee SY, Kang S, Lim SY, Lee MS, Kim KH. Artificial Intelligence-Enhanced Smartwatch ECG for Heart Failure-Reduced Ejection Fraction Detection by Generating 12-Lead ECG. Diagnostics (Basel). 2022 Mar 8;12(3):654. doi: 10.3390/diagnostics12030654.
- Results publication: PubMed 31074221Kwon JM, Kim KH, Jeon KH, Kim HM, Kim MJ, Lim SM, Song PS, Park J, Choi RK, Oh BH. Development and Validation of Deep-Learning Algorithm for Electrocardiography-Based Heart Failure Identification. Korean Circ J. 2019 Jul;49(7):629-639. doi: 10.4070/kcj.2018.0446. Epub 2019 Mar 21.
- 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. This study is not recruiting at the moment.
Seung-Pyo Lee, MD, PhD principal investigator
Seoul National University Hospital
Questions about this trial
What is this clinical trial studying?
An observational study of Left Ventricular Systolic Dysfunction, run by Seoul National University Hospital.
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 2 inclusion and 3 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: status not confirmed, as of the sponsor's last update on 5 Feb 2024. Status can change between updates, so confirm with the study contact before making plans.
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?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Hak Seung Lee, MD (+1-771-216-0764, cardiolee@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Area under the receiver operating characteristic curve (AUROC) (time frame: Through study completion, an average of 1 year). 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 NCT06231797, which also lists 2 linked publications.
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 5 Feb 2024.