Health Status(Death, Stroke Etc)NCT06179849
Artificial Intelligence-enabled Large-scale Electrocardiogram Feature Extraction and Exploring Association Between the Extracted Features and Mortality, Stroke or Various Health Outcome of Interest
An observational study of Health Status(Death, Stroke Etc), run by Yonsei University at 1 site in South Korea.
Trial summary
In this study, large-scale ECG data (Electrocardiogram data of all patients stored in the MUSE system by measuring standard 12-guided ECG at Severance Health Checkup at Severance Hospital from November 1, 2005 to October 31, 2022) are combined with electronic medical records, National Health Insurance Corporation… Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Health Status(Death, Stroke Etc)
- How many people
- 3,000,000 participants
- Where
- 1 siteSouth Korea
- Study dates
- Dec 2023to Dec 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 Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: All patients stored in the MUSE system after measuring a standard 12-guided electrocardiogram at Severance Health Checkup at Severance Hospital from November 1, 2005 to October 31, 2022
Who may be able to join
- All patients stored in the MUSE system after measuring a standard 12-guided electrocardiogram at Severance Health Checkup at Severance Hospital from November 1, 2005 to October 31, 2022
Who cannot join
None listed.
Trial locations and contacts
1 site in 1 country.
South Korea1 site
Yonsei University Health System
Seoul
Hui-Nam Pak, 82-2-2228-8459, hnpak@yuhs.ac
About this study
- In this study, large-scale ECG data (Electrocardiogram data of all patients stored in the MUSE system by measuring standard 12-guided ECG at Severance Health Checkup at Severance Hospital from November 1, 2005 to October 31, 2022) are combined with electronic medical records, National Health Insurance Corporation data, and National Statistical Office death cause data, and the artificial intelligence algorithm is used to extract ECG features to analyze the association between death, stroke, and various health conditions, and to conduct external verification or transfer learning using public databases (e.g., UK Biobank data).
- Intended to use a web-based artificial intelligence platform to distribute computational loads generated during large-scale data processing and improve analysis accuracy and efficiency.
Read the detailed description
- All patient IDs obtained from the main office are replaced by research IDs (de-identified IDs), so the actual ID is not exposed and other personal identification information (name, resident registration number) is not collected.
- Research Methods:
- Electrocardiogram extraction based on the criteria of subjects.
- Combined with extracted ECG data and National Insurance Corporation data (+ National Statistical Office cause of death data).
- Health out of interest (HOI) definition. Includes death, stroke, etc.
- The defined HOI can be extracted from Yonsei Medical Center data or from National Insurance Service data or Statistics Korea's cause of death data.
- Artificial intelligence model training with electrocardiogram (and clinical information diagram if necessary) as input, utilizing supervised deep learning algorithms if there is a label and unsupervised learning algorithms if there is no label.
- Performance evaluation for supervised learning artificial intelligence models.
- In the case of unsupervised learning artificial intelligence models, the association/correlation between extracted features and HOI or predictability/detectability analysis.
- Transfer learning can be performed by adding external verification or dielectric data to the learned model using public databases.
- External verification can be performed using external additional data by mounting the learned model on a web-based artificial intelligence platform.
- Considering large-scale data, computing workloads can be distributed using web-based artificial intelligence platforms.
- The analysis results can be anonymized and the analysis results can be provided to researchers through a web-based artificial intelligence platform.
- Conditions
- Health Status(Death, Stroke Etc)
What is being measured
The primary outcome is the main question the study is designed to answer.
Number of patients with Mortality
Investigating the reproducibility of mortality prediction (number of patients who died regardless of any cause) from ECG data measured within 1 year before death using artificial intelligence algorithms
Measured: 2 years
Number of patients with Stroke, Atrial Fibrillation, Dementia
Investigating the correlation of certain patterns of ECG with the possibility of stroke/Atrial Fibrillation, Dementia using artificial intelligence algorithms.
Measured: 2 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted22 Dec 2023
- Study startDec 2023 (estimated)
- Primary completionDec 2025 (estimated)
- Study completionDec 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Health Status(Death, Stroke Etc)Studies of the same condition, open and under way.
- More trials run by Yonsei UniversityThe 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
- Retrospective
- Enrolment
- 3,000,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Yonsei University
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Hui-Nam Pak, Principal investigator, Yonsei University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06179849
- Sponsor's protocol ID
- 4-2022-1506
- Status last verified
- Dec 2023
- First submitted
- 22 Nov 2023
- Last updated
- 2 Jan 2024
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 2 Jan 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligence-enabled Large-scale Electrocardiogram Feature Extraction and Exploring Association Between the Extracted Features and Mortality, Stroke or Various Health Outcome of Interest
- ClinicalTrials.gov ID
- NCT06179849
- Acronym / sponsor ID
- 4-2022-1506
- Conditions
- Health Status(Death, Stroke Etc)
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 3,000,000 participants (estimated)
- Lead sponsor
- Yonsei UniversityUniversity, hospital or other
- Study start
- Dec 2023 (estimated)
- Primary completion
- Dec 2025 (estimated)
- Study completion
- Dec 2025 (estimated)
- First posted
- 22 Dec 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.
- In this study, large-scale ECG data (Electrocardiogram data of all patients stored in the MUSE system by measuring standard 12-guided ECG at Severance Health Checkup at Severance Hospital from November 1, 2005 to October 31, 2022) are combined with electronic medical records, National Health Insurance Corporation data, and National Statistical Office death cause data, and the artificial intelligence algorithm is used to extract ECG features to analyze the association between death, stroke, and various health conditions, and to conduct external verification or transfer learning using public databases (e.g., UK Biobank data).
- Intended to use a web-based artificial intelligence platform to distribute computational loads generated during large-scale data processing and improve analysis accuracy and efficiency.
Detailed description
- All patient IDs obtained from the main office are replaced by research IDs (de-identified IDs), so the actual ID is not exposed and other personal identification information (name, resident registration number) is not collected.
- Research Methods:
- Electrocardiogram extraction based on the criteria of subjects.
- Combined with extracted ECG data and National Insurance Corporation data (+ National Statistical Office cause of death data).
- Health out of interest (HOI) definition. Includes death, stroke, etc.
- The defined HOI can be extracted from Yonsei Medical Center data or from National Insurance Service data or Statistics Korea's cause of death data.
- Artificial intelligence model training with electrocardiogram (and clinical information diagram if necessary) as input, utilizing supervised deep learning algorithms if there is a label and unsupervised learning algorithms if there is no label.
- Performance evaluation for supervised learning artificial intelligence models.
- In the case of unsupervised learning artificial intelligence models, the association/correlation between extracted features and HOI or predictability/detectability analysis.
- Transfer learning can be performed by adding external verification or dielectric data to the learned model using public databases.
- External verification can be performed using external additional data by mounting the learned model on a web-based artificial intelligence platform.
- Considering large-scale data, computing workloads can be distributed using web-based artificial intelligence platforms.
- The analysis results can be anonymized and the analysis results can be provided to researchers through a web-based artificial intelligence platform.
Study design
- Observational model
- Other
- Time perspective
- Retrospective
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- All patients stored in the MUSE system after measuring a standard 12-guided electrocardiogram at Severance Health Checkup at Severance Hospital from November 1, 2005 to October 31, 2022
Key inclusion criteria (1 listed)
- All patients stored in the MUSE system after measuring a standard 12-guided electrocardiogram at Severance Health Checkup at Severance Hospital from November 1, 2005 to October 31, 2022
Key exclusion criteria (0 listed)
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
Number of patients with Mortality
Time frame: 2 years
Investigating the reproducibility of mortality prediction (number of patients who died regardless of any cause) from ECG data measured within 1 year before death using artificial intelligence algorithms
Number of patients with Stroke, Atrial Fibrillation, Dementia
Time frame: 2 years
Investigating the correlation of certain patterns of ECG with the possibility of stroke/Atrial Fibrillation, Dementia using artificial intelligence algorithms.
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 2 Jan 2024.
- Individual participant dataSharing planned: No
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.
Hui-Nam Pak principal investigator
Yonsei University
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 Health Status(Death, Stroke Etc), run by Yonsei University at 1 site in South Korea.
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 1 inclusion and 0 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 2 Jan 2024. 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 South Korea. 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?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Hui-Nam Pak (82-2-2228-8459, hnpak@yuhs.ac), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Number of patients with Mortality (time frame: 2 years). The study lists 2 primary outcomes in all. 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 2 Jan 2024.