Heart Failure With Improved Ejection Fraction and Deep Learning

NCT ID: NCT06070506

Last Updated: 2023-10-11

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

COMPLETED

Total Enrollment

422 participants

Study Classification

OBSERVATIONAL

Study Start Date

2022-03-01

Study Completion Date

2023-05-20

Brief Summary

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The aim of this study was to design a deep learning-based trained model to assist in HFimpEF diagnosis.

Detailed Description

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Conditions

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Heart Failure Diagnosis

Study Design

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Observational Model Type

OTHER

Study Time Perspective

RETROSPECTIVE

Study Groups

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HFrEF group

Heart failure patients with LVEF persistently ≤40%.

No interventions assigned to this group

HFimpEF group

Heart failure patients with previous LVEF ≤40% and a follow-up LVEF of more than 40%.

No interventions assigned to this group

Eligibility Criteria

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Inclusion Criteria

1. Age \>18 years.
2. The diagnostic criteria of HF follows the 2018 Chinese Guidelines for the Diagnosis and Treatment of Heart Failure, having symptoms of dyspnea, fatigue or decreased activity tolerance, having signs of fluid retention (such as pulmonary congestion and peripheral edema), having echocardiogram abnormalities in cardiac structure and/or function, showing elevated natriuretic peptide levels (BNP\>35 ng/L or/and N-terminal pro-BNP \>125 ng/L).
3. Have reviewing echocardiography after discharge.

Exclusion Criteria

1. Patients with hypertrophic, restrictive, or invasive cardiomyopathy and congenital or rheumatic heart disease.
2. Patients with heart transplantation during follow-up.
Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Yihui Kong

OTHER

Sponsor Role lead

Responsible Party

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Yihui Kong

Professor

Responsibility Role SPONSOR_INVESTIGATOR

Locations

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Yihui Kong

Harbin, Heilongjiang, China

Site Status

Countries

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China

Other Identifiers

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2020020668

Identifier Type: -

Identifier Source: org_study_id

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