Use of Determine Learning-based Cardiodynamicsgram (CDG) for Rapid and Precise Stratification of Chest Pain in Emergency Department
NCT ID: NCT06669884
Last Updated: 2024-11-12
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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RECRUITING
8000 participants
OBSERVATIONAL
2021-10-28
2024-10-31
Brief Summary
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Detailed Description
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Conditions
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Study Design
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COHORT
PROSPECTIVE
Study Groups
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machine learning algorithm
machine learning algorithm based on ECG features
Cardiodynamicsgram (CDG)
Cardiodynamicsgram (CDG) technique
Interventions
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Cardiodynamicsgram (CDG)
Cardiodynamicsgram (CDG) technique
Eligibility Criteria
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Inclusion Criteria
* Those with suspected ACS who have symptoms of acute chest pain, visiting in the emergency department
Exclusion Criteria
* Those with hemodynamic instability (cardiogenic shock, cardiac arrest)
* Those with malignant arrhythmias(ventricular tachycardia, ventricular fibrillation, third-degree atrioventricular block)
* Those with aortic coarctation, or acute pulmonary embolism
* Those who has an unanalysable ECG report due to loosened leads, unstable baseline, or signal interference, etc.
18 Years
ALL
No
Sponsors
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Qilu Hospital of Shandong University
OTHER
Responsible Party
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Principal Investigators
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Yuguo Chen, Professor
Role: PRINCIPAL_INVESTIGATOR
Qliu Hospital of Shandong University
Locations
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Qilu Hospital of Shandong University
Jinan, Shandong, China
Countries
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Central Contacts
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Facility Contacts
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Other Identifiers
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QLEmer-CDG-1
Identifier Type: -
Identifier Source: org_study_id
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