Artificial Intelligence-enabled Large-scale Electrocardiogram Feature Extraction and Exploring Association Between the Extracted Features and Mortality, Stroke or Various Health Outcome of Interest

NCT ID: NCT06179849

Last Updated: 2024-01-02

Basic Information

• Recruitment Status: NOT_YET_RECRUITING
• Total Enrollment: 3000000 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2023-12-31
• Study Completion Date: 2025-12-31

Brief 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 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:

1. Electrocardiogram extraction based on the criteria of subjects.

2. Combined with extracted ECG data and National Insurance Corporation data (+ National Statistical Office cause of death data).

3. Health out of interest (HOI) definition. Includes death, stroke, etc.

4. 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.

5. 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.

6. Performance evaluation for supervised learning artificial intelligence models.

7. In the case of unsupervised learning artificial intelligence models, the association/correlation between extracted features and HOI or predictability/detectability analysis.

8. Transfer learning can be performed by adding external verification or dielectric data to the learned model using public databases.

9. External verification can be performed using external additional data by mounting the learned model on a web-based artificial intelligence platform.

10. Considering large-scale data, computing workloads can be distributed using web-based artificial intelligence platforms.

11. 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)

Study Design

• Observational Model Type: OTHER
• Study Time Perspective: RETROSPECTIVE

Eligibility Criteria

Inclusion Criteria

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

• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Yonsei University

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Hui-Nam Pak

Role: PRINCIPAL_INVESTIGATOR

Yonsei University

Locations

Yonsei University Health System

Seoul, , South Korea

Countries

South Korea

Central Contacts

Hui-Nam Pak

Role: CONTACT

hnpak@yuhs.ac

82-2-2228-8459

Facility Contacts

Hui-Nam Pak

Role: primary

hnpak@yuhs.ac

82-2-2228-8459

Other Identifiers

4-2022-1506

Identifier Type: -

Identifier Source: org_study_id