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

NCT06924580 | Recruiting

• 1 other identifier: EARLY-ECG-PREDICTION Cohort
• Study Type: observational
• Target: 500,000
• Locations: 1 country
• Sites: 1

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.

Conditions

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

Outcome Measures

Primary Outcomes (1)

• 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

Interventions

ECG screening OTHER

Each subject is subjected to ECG assessment.

Eligibility Criteria

• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

All patients undergoing ECG examinations

You may qualify if:

• Patients who visited the study hospital.
• Patients included should have both ECG data and discharge diagnosis codes (ICD-10) for inpatients and emergency patients.

You may not qualify if:

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

Sponsors & Collaborators

• RenJi Hospital: lead

Study Sites (1)

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

Shanghai, Shanghai Municipality, 200000, China

RECRUITING

Central Study Contacts

Jun Pu, MD,PhD

CONTACT

86-21-68383477; pujun310@hotmail.com

Study Design

• Study Type: observational
• Observational Model: CASE CONTROL
• Time Perspective: PROSPECTIVE
• Target Duration: 10 Years
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: April 6, 2025
• First Posted: April 11, 2025
• Study Start: January 18, 2017
• Primary Completion (Estimated): December 30, 2026
• Study Completion (Estimated): December 30, 2026
• Last Updated: April 11, 2025

Record last verified: 2024-12

Locations

CN (1)