Artificial Intelligence (AI) Analysis of Synchronized Phonocardiography (PCG) and Electrocardiogram(ECG)

NCT ID: NCT06009718

Last Updated: 2025-01-16

Study Results

Results pending

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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Recruitment Status

RECRUITING

Total Enrollment

3000 participants

Study Classification

OBSERVATIONAL

Study Start Date

2023-08-25

Study Completion Date

2028-06-01

Brief Summary

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The diagnosis of depressed left ventricular ejection fraction (dLVEF) (EF\<50%) depends on golden standard ultrasound cardiography (UCG). A wearable synchronized phonocardiography (PCG) and electrocardiogram (ECG) device can assist in the diagnosis of dLVEF, which can both expedite access to life-saving therapies and reduce the need for costly testing.

Detailed Description

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The synchronized PCG and ECG is wirelessly paired with the WenXin Mobile application, allowing for simultaneous recording and visualization of PCG and ECG. These features uniquely enable this device to accumulate large sets of acoustic data on patients both with and without heart failure(HF).

This study is a Case-control study. In this study, the investigators seek to develop an artificial intelligence (AI) analysis system to identify dLVEF (EF\<50%) by PCG and ECG. All adults (aged ≥18 years) planned for UCG were eligible to participate (inpatients and outpatients). Specifically, the investigators will attempt to develop machine learning algorithms to learn synchronized PCG and ECG of patients with dLVEF. Then we use these algorithms to identify dLVEF subjects. The investigators anticipate to demonstrate the wearable cardiac patch with synchronized PCG and ECG can reliably and accurately diagnose dLVEF in the primary care setting.

Conditions

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Heart Failure

Study Design

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Observational Model Type

CASE_CONTROL

Study Time Perspective

PROSPECTIVE

Study Groups

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Model training group

Compare the results of PCG and ECG with UCG, and conduct model training analysis

No interventions assigned to this group

Model validation group

Compare the results of PCG and ECG with UCG, and conduct model validation analysis

No interventions assigned to this group

Eligibility Criteria

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Inclusion Criteria

* Attendance at RuiJin hospital for UCG
* Signed dated informed consent
* Commit to follow the research procedures and cooperate in the implementation of the whole process research
* UCG has been completed
* Age ≥ 18
* At least 8 consecutive cycles of sinus rhythm can be recorded

Exclusion Criteria

* Patients with pacemakers
* Complete left bundle branch block or block or QRS wave widening\>120ms
* Left chest skin damaged or allergic to patch
* Refusal to participate
Minimum Eligible Age

18 Years

Maximum Eligible Age

100 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Ruijin Hospital

OTHER

Sponsor Role lead

Responsible Party

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RUIYAN ZHANG

Director of Cardiology Department, Chief Physician

Responsibility Role PRINCIPAL_INVESTIGATOR

Principal Investigators

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Ruiyan Zhang, MD, PhD

Role: PRINCIPAL_INVESTIGATOR

Ruijin Hospital, Shanghai Jiaotong School of Medicine

Wenli Zhang, MD

Role: STUDY_DIRECTOR

Ruijin Hospital, Shanghai Jiaotong School of Medicine

Bei Song, MD

Role: STUDY_CHAIR

Ruijin Hospital, Shanghai Jiaotong School of Medicine

Locations

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Ruijin Hospital, Shanghai Jiaotong School of Medicine

Shanghai, , China

Site Status RECRUITING

Shanghai Chest Hospital, Shanghai Jiao Tong University School Of Medicine

Shanghai, , China

Site Status RECRUITING

Shanghai East Hospital

Shanghai, , China

Site Status RECRUITING

Countries

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China

Central Contacts

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Wenli Zhang, MD

Role: CONTACT

+86 21 13917615339

Bei Song, MD

Role: CONTACT

+86 21 15821960139

Facility Contacts

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Wenli Zhang, MD

Role: primary

+86 021 64370045

Jingjuan Huang, MD,PhD

Role: primary

+86 021 22200000

Min Chen, MD

Role: primary

+86 15900727844

Other Identifiers

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RJH-PEG

Identifier Type: -

Identifier Source: org_study_id

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