Early ECG Prediction of Multi-system Disease Cohort Establishment and Follow Up
Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
AgeNot listed
SponsorRenJi Hospital
About this trial
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.
Eligibility criteria
This trial accepts healthy volunteersQualifiers
Patients who visited the study hospital.
Patients included should have both ECG data and discharge diagnosis codes (ICD-10) for inpatients and emergency patients.
Disqualifiers
None
Trial population
All patients undergoing ECG examinations
Trial design
Design model
Case-control
Time perspective
Prospective
Treatments tested in this trial
ECG screening
Other interventionEach subject is subjected to ECG assessment.
Treatment groups
No treatment groups listed
Trial outcomes
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.
Time frame
1 monthSponsors and contacts
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Source ClinicalTrials.gov