Clinical trial NCT06924580: Early ECG Prediction of Multi-system Disease Cohort Establishment and Follow Up
Official title: Early ECG Prediction of Multi-system Disease Cohort Establishment and Follow Up
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Public Health
Public Health System Research
Multi-system Disease Diagnosis
Lead sponsor: RenJi Hospital
Sponsor class: OTHER
First posted: 2025-04-11
Enrollment: 500000
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: CASE_CONTROL
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: All patients undergoing ECG examinations

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.

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.

Primary outcomes:
measure: Multi-system disease predicting based on ECG
timeFrame: 1 month
Secondary outcomes:
Other outcomes:
Locations:
city: Shanghai
country: China
facility: Ren Ji Hospital Afflited to School of Medicine, Shanghai Jiao Tong University
state: Shanghai
Study officials:
References: