Public HealthEARLY-ECG-PREDNCT06924580
Early ECG Prediction of Multi-system Disease Cohort Establishment and Follow Up
An observational study of Public Health and Public Health System Research, run by RenJi Hospital at 1 site in China.
Trial summary
This registered multicenter study aims to investigate the diagnostic efficacy of artificial intelligence-enhanced electrocardiography (AI-ECG) in detecting multi-system diseases. Read more about this study
- Who is it for?
- Any ageHealthy volunteers can take part
- Conditions the study is for
- Public Health, Public Health System Research, Multi-system Disease Diagnosis
- What is being tested?
- ECG screening
- How many people
- 500,000 participants
- Where
- 1 siteChina
- Study dates
- 18 Jan 2017to 30 Dec 2026
- How long will I take part?
- 10 Years
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
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
- Healthy volunteers accepted
- Child, Adult, Older adult
Study population: All patients undergoing ECG examinations
Who may be able to join
- Patients who visited the study hospital.
- Patients included should have both ECG data and discharge diagnosis codes (ICD-10) for inpatients and emergency patients.
Who cannot join
- 1. Patients who declined participation, cases with incomplete or missing clinical data, and pregnant individuals.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site, 1 recruiting
- Recruiting
Ren Ji Hospital Afflited to School of Medicine, Shanghai Jiao Tong University
Shanghai, Shanghai Municipality, 200000
Song ding, MD, 86-21-68383477, dingsong1105@163.com
About this study
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.
Read the 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:
- AI-ECG Foundation Model Development
- Diagnosis of traditional cardiovascular diseases (e.g., arrhythmias, myocardial infarction).
- 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.
- Prediction of new-onset conditions (e.g., atrial fibrillation, heart failure, valvular diseases, NSTEMI, ventricular tachycardia) and 1-year mortality risk.
- Clinical Utility \& Implementation
Leveraging the portability, cost-effectiveness, and non-invasiveness of ECG, our AI foundation model enables:
- Rapid, large-scale screening in outpatient, inpatient, emergency, and community settings.
- Early detection of multi-system diseases, guiding targeted diagnostic workups.
- Mechanistic \& Interpretability Research Elucidating the diagnostic, predictive, and risk-stratification logic of AI-ECG foundation models.
- Being tested
- ECG screening
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
ECG screeningOther
Each subject is subjected to ECG assessment.
What is being measured
The primary outcome is the main question the study is designed to answer.
Multi-system disease predicting based on ECG
Evaluating the effectiveness of ECG in predicting diseases across various systems, such as circulatory system diseases, respiratory system diseases, digestive system diseases, nervous system diseases, urogenital system diseases, endocrine and nutritional/metabolic system diseases, hematological diseases, infectious and parasitic diseases, tumors, and mental and behavioral disorders. This study initially uses the ICD-10 coding system for preliminary screening of target diseases. Subsequently, a committee of multidisciplinary clinical experts conducts a systematic review of candidate diseases based on the ICD-10 coding system framework, including the applicability of diagnostic criteria, the accuracy of ICD-10 classification, the reasonableness of exclusion criteria, and the assessment of the level of evidence.
Measured: 1 month
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted11 Apr 2025
- Study start18 Jan 2017
- Primary completion30 Dec 2026 (estimated)
- Study completion30 Dec 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Public HealthStudies of the same condition, open and under way.
- More trials run by RenJi HospitalThe 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
- Case-control
- Time perspective
- Prospective
- Enrolment
- 500,000 participants (estimated)
- Target duration
- 10 Years
Sponsor and investigators
- Lead sponsor
- RenJi Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06924580
- Sponsor's protocol ID
- EARLY-ECG-PREDICTION Cohort
- Acronym
- EARLY-ECG-PRED
- Status last verified
- Dec 2024
- First submitted
- 6 Apr 2025
- Last updated
- 11 Apr 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 11 Apr 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Early ECG Prediction of Multi-system Disease Cohort Establishment and Follow Up
- ClinicalTrials.gov ID
- NCT06924580
- Acronym / sponsor ID
- EARLY-ECG-PRED / EARLY-ECG-PREDICTION Cohort
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 500,000 participants (estimated)
- Lead sponsor
- RenJi HospitalUniversity, hospital or other
- Study start
- 18 Jan 2017 (actual)
- Primary completion
- 30 Dec 2026 (estimated)
- Study completion
- 30 Dec 2026 (estimated)
- First posted
- 11 Apr 2025 (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.
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:
- AI-ECG Foundation Model Development
- Diagnosis of traditional cardiovascular diseases (e.g., arrhythmias, myocardial infarction).
- 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.
- Prediction of new-onset conditions (e.g., atrial fibrillation, heart failure, valvular diseases, NSTEMI, ventricular tachycardia) and 1-year mortality risk.
- Clinical Utility \& Implementation
Leveraging the portability, cost-effectiveness, and non-invasiveness of ECG, our AI foundation model enables:
- Rapid, large-scale screening in outpatient, inpatient, emergency, and community settings.
- Early detection of multi-system diseases, guiding targeted diagnostic workups.
- Mechanistic \& Interpretability Research Elucidating the diagnostic, predictive, and risk-stratification logic of AI-ECG foundation models.
Study design
- Observational model
- Case-control
- Time perspective
- Prospective
- Target follow-up
- 10 Years
Who may qualify
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
- Study population
- All patients undergoing ECG examinations
Key inclusion criteria (2 listed)
- Patients who visited the study hospital.
- Patients included should have both ECG data and discharge diagnosis codes (ICD-10) for inpatients and emergency patients.
Key exclusion criteria (1 listed)
- 1. Patients who declined participation, cases with incomplete or missing clinical data, and pregnant individuals.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| ECG screeningEach subject is subjected to ECG assessment. | Other | – |
Primary outcome
Multi-system disease predicting based on ECG
Time frame: 1 month
Evaluating the effectiveness of ECG in predicting diseases across various systems, such as circulatory system diseases, respiratory system diseases, digestive system diseases, nervous system diseases, urogenital system diseases, endocrine and nutritional/metabolic system diseases, hematological diseases, infectious and parasitic diseases, tumors, and mental and behavioral disorders. This study initially uses the ICD-10 coding system for preliminary screening of target diseases. Subsequently, a committee of multidisciplinary clinical experts conducts a systematic review of candidate diseases based on the ICD-10 coding system framework, including the applicability of diagnostic criteria, the accuracy of ICD-10 classification, the reasonableness of exclusion criteria, and the assessment of the level of evidence.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 11 Apr 2025.
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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.
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 Public Health and Public Health System Research, run by RenJi Hospital at 1 site in China.
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 2 inclusion and 1 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: looking for participants now, as of the sponsor's last update on 11 Apr 2025. 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 China. 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?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Jun Pu, MD,PhD (86-21-68383477, pujun310@hotmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Multi-system disease predicting based on ECG (time frame: 1 month). 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 11 Apr 2025.