Clinical trial NCT06009718: Artificial Intelligence (AI) Analysis of Synchronized Phonocardiography (PCG) and Electrocardiogram(ECG)
Official title: A Deep-learning-based Multi-modal Phonocardiogram(PCG) and Electrocardiogram(ECG) Processing Framework for Screening Depressed Left Ventricular Ejection Fraction (dLVEF) Using a Wearable Cardiac Patch
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Heart Failure
Keywords: Phonocardiography (PCG)
Electrocardiogram (ECG)
Heart Failure (HF)
depressed left ventricular ejection fraction (dLVEF)
Lead sponsor: Ruijin Hospital
Sponsor class: OTHER
First posted: 2023-08-24
Enrollment: 3000
Sex: ALL
Minimum age (years): 18
Maximum age (years): 100
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: CASE_CONTROL
Time perspective: PROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: This is one-center cohort study. Patients undergo echocardiogram at Ruijin Hospital.

Brief summary: 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: 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.

Eligibility criteria: 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

Primary outcomes:
measure: Determination of Heart Failure Disease
timeFrame: one time assessment at baseline (approx. 5 minutes)
Secondary outcomes:
Other outcomes:
Locations:
city: Shanghai
country: China
facility: Ruijin Hospital, Shanghai Jiaotong School of Medicine
city: Shanghai
country: China
facility: Shanghai Chest Hospital, Shanghai Jiao Tong University School Of Medicine
city: Shanghai
country: China
facility: Shanghai East Hospital
Study officials:
affiliation: Ruijin Hospital, Shanghai Jiaotong School of Medicine
name: Ruiyan Zhang, MD, PhD
role: PRINCIPAL_INVESTIGATOR
affiliation: Ruijin Hospital, Shanghai Jiaotong School of Medicine
name: Wenli Zhang, MD
role: STUDY_DIRECTOR
affiliation: Ruijin Hospital, Shanghai Jiaotong School of Medicine
name: Bei Song, MD
role: STUDY_CHAIR
References: