Early ECG Prediction of Multi-system Disease Cohort Estab... | NCT06924580

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

NCT06924580

Sponsor

RenJi Hospital

Status

Recruiting

Last Update Posted

Apr 11, 2025Actual

Enrollment

500,000Estimated

Phase

Not provided

Conditions

Public Health
Public Health System Research
Multi-system Disease Diagnosis

Interventions

ECG screening

Countries

China

Protocol Section

Identification Module Status Module Sponsor/Collaborators Module Oversight Module Description Module Conditions Module Design Module Arms/Interventions Module Outcomes Module Eligibility Module Contacts/Locations Module References Module IPD Sharing Statement Module

Results Section

Participant Flow Module Baseline Characteristics Module Outcome Measures Module Adverse Events Module More Info Module

Annotation Section

Annotation Module

Document Section

Large Document Module

Derived Section

Miscellaneous Info Module Condition Browse Module Intervention Browse Module

Protocol Section

Identification Module

NCT ID

Results Section

No data available

No data is available for this block.

Annotation Section

No data available

No data is available for this block.

Document Section

No data available

No data is available for this block.

Derived Section

Miscellaneous Info Module

Version Holder

Status Module

Record Verification Date

Dec 2024

Overall Recruitment Status or Expanded Access Status

Recruiting

Last Known Status

Not provided

Delayed Posting

Not provided

Why Stopped

Not provided

Expanded Access Info

Start Date

Jan 18, 2017Actual

Primary Completion Date

Dec 30, 2026Estimated

Completion Date

Dec 30, 2026Estimated

First Submitted Date

Apr 6, 2025

First Submission Date that Met QC Criteria

Apr 6, 2025

First Posted Date

Apr 11, 2025Actual

Results Waived

Not provided

Results First Submitted Date

Not provided

Results First Submitted that Met QC Criteria

Not provided

Results First Posted Date

Not provided

Certification/Extension (aka Delayed Results) First Submitted Date

Not provided

Certification/Extension First Submitted that Passed QC Review

Not provided

Certification/Extension First Posted Date

Not provided

Last Update Submitted Date

Apr 6, 2025

Last Update Posted Date

Apr 11, 2025Actual

Oversight Module

Has Data Monitoring Committee (DMC)

Is FDA Regulated Drug

Is FDA Regulated Device

Is Unapproved Device

Not provided

Pediatric Postmarket Surveillance of a Device Product

Not provided

Product Exported from US

FDAAA801 Violation

Not provided

Description Module

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

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.

2. 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.

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

Design Module

Study Type

Observational

Number of References to an Expanded Access Study

Not provided

Expanded Access Types

Not provided

Patient Registry

Yes

Target Follow-Up Duration

10 Years

Phases

Not provided

Interventional Study Design

Allocation

Not provided

Intervention Model

Biospecimen

No data available

No data is available for this block.

Enrollment

500,000Estimated

Arms/Interventions Module

Arm Groups

Not provided

Interventions

Name	Type	Description	Arm Group Labels	Other Names
ECG screening	Other	Each subject is subjected to ECG assessment.

Outcomes Module

Primary Outcomes

Measure	Description	Time Frame
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.	1 month

Secondary Outcomes

Not provided

Other Outcomes

Not provided

Eligibility Module

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.

Accepts Healthy Volunteers

Yes

Sex

All

Sex/Gender Based

Not provided

Sex/Gender Description

Not provided

Minimum Age

Not provided

Maximum Age

Not provided

Standard Ages

ChildAdultOlder Adult

Study Population

All patients undergoing ECG examinations

Sampling Method

Non-Probability Sample

Contacts/Locations Module

Central Contacts

Name	Role	Phone	Extension	Email
Jun Pu, MD,PhD	Contact	86-21-68383477		pujun310@hotmail.com

Overall Officials

Not provided

Locations

Facility	Status	City	State	ZIP	Country	Contacts
Ren Ji Hospital Afflited to School of Medicine, Shanghai Jiao Tong University	Recruiting	Shanghai	Shanghai Municipality	200000	China