Early ECG Prediction of Multi-system Disease Cohort Establishment and Follow Up

NCT ID: NCT06924580

Last Updated: 2025-04-11

Basic Information

• Recruitment Status: RECRUITING
• Total Enrollment: 500000 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2017-01-18
• Study Completion Date: 2026-12-30

Brief Summary

This registered multicenter study aims to investigate the diagnostic efficacy of artificial intelligence-enhanced electrocardiography (AI-ECG) in detecting multi-system diseases. The research will utilize prospectively collected data from inpatient, emergency, and outpatient populations to develop ECG-based diagnostic, screening, and predictive models for multi-system diseases.

Detailed Description

Recent advances in artificial intelligence (AI) have expanded the diagnostic capabilities of electrocardiography (ECG) beyond cardiovascular diseases. Emerging evidence demonstrates that AI-enhanced ECG analysis can provide valuable insights into age, gender, mortality risk, cardiac function, and systemic conditions such as electrolyte imbalances, renal dysfunction, and thyroid disorders. These findings position ECG as a promising tool for the identification and prediction of a broad spectrum of diseases.

To further investigate the underlying mechanisms linking ECG abnormalities with multi-system diseases and to develop ECG-based diagnostic, screening, and predictive models, we initiated a multi-center, prospective, observational registry study involving patients undergoing ECG examinations. The goals of the project are as follows:

1. AI-ECG Foundation Model Development

1. Diagnosis of traditional cardiovascular diseases (e.g., arrhythmias, myocardial infarction).

2. Screening of multi-system disorders, including: Circulatory, digestive, respiratory, and nervous system diseases, Endocrine/metabolic disorders, urogenital diseases, hematologic conditions, Neoplasms and mental health disorders.

3. Prediction of new-onset conditions (e.g., atrial fibrillation, heart failure, valvular diseases, NSTEMI, ventricular tachycardia) and 1-year mortality risk.

2. Clinical Utility & Implementation

Leveraging the portability, cost-effectiveness, and non-invasiveness of ECG, our AI foundation model enables:

1. Rapid, large-scale screening in outpatient, inpatient, emergency, and community settings.

2. Early detection of multi-system diseases, guiding targeted diagnostic workups.

3. Mechanistic & Interpretability Research Elucidating the diagnostic, predictive, and risk-stratification logic of AI-ECG foundation models.

Conditions

Public Health; Public Health System Research; Multi-system Disease Diagnosis

Study Design

• Observational Model Type: CASE_CONTROL
• Study Time Perspective: PROSPECTIVE

Interventions

ECG screening

Each subject is subjected to ECG assessment.

Intervention Type: OTHER

Eligibility Criteria

Inclusion Criteria

1. Patients who visited the study hospital.

2. Patients included should have both ECG data and discharge diagnosis codes (ICD-10) for inpatients and emergency patients.

Exclusion Criteria

1. Patients who declined participation, cases with incomplete or missing clinical data, and pregnant individuals.

• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

RenJi Hospital

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Locations

Ren Ji Hospital Afflited to School of Medicine, Shanghai Jiao Tong University

Shanghai, Shanghai Municipality, China

Site Status: RECRUITING

Countries

China

Central Contacts

Jun Pu, MD,PhD

Role: CONTACT

pujun310@hotmail.com

86-21-68383477

Facility Contacts

Song ding, MD

Role: primary

dingsong1105@163.com

86-21-68383477

Other Identifiers

EARLY-ECG-PREDICTION Cohort

Identifier Type: -

Identifier Source: org_study_id