Early ECG Prediction of Multi-system Disease Cohort Establishment and Follow Up
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: ECG screening.
- Who it may be relevant to
- Registry conditions: Public Health, Public Health System Research, Multi-system Disease Diagnosis. Basic parameters: No limits · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- China
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Overview
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.
Interventions
- Other ECG screening
Each subject is subjected to ECG assessment.
Primary outcome measures
- Multi-system disease predicting based on ECG [Time frame: 1 month]
Eligibility criteria
Inclusion criteria
- Patients who visited the study hospital.
- 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.
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: Yes
Study design
- Observational model
- Case-control
Study locations
China · 1 center
- Ren Ji Hospital Afflited to School of Medicine, Shanghai Jiao Tong University — Shanghai
Identifiers
NCT: NCT06924580 · EARLY-ECG-PREDICTION Cohort