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Heart Failure With Improved Ejection Fraction and Deep Learning

Sponsor
Yihui Kong (Other)
Overall Status
Completed
CT.gov ID
NCT06070506
Collaborator
(none)
422
Enrollment
1
Location
14.6
Actual Duration (Months)
28.9
Patients Per Site Per Month

Study Details

Study Description

Brief Summary

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

Condition or Disease Intervention/Treatment Phase

    Study Design

    Study Type:
    Observational
    Actual Enrollment :
    422 participants
    Observational Model:
    Other
    Time Perspective:
    Retrospective
    Official Title:
    Deep Learning-Based Prediction Model of Heart Failure With Improved Ejection Fraction
    Actual Study Start Date :
    Mar 1, 2022
    Actual Primary Completion Date :
    Dec 31, 2022
    Actual Study Completion Date :
    May 20, 2023

    Arms and Interventions

    Arm Intervention/Treatment
    HFrEF group

    Heart failure patients with LVEF persistently ≤40%.
    HFimpEF group

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

    Outcome Measures

    Primary Outcome Measures

    1. change of left ventricular ejection fraction [3 months]

      left ventricular ejection fraction value in millimeters

    Secondary Outcome Measures

    1. change of clinical predictor of EF improvement [3 months]

      weight in kilograms, height in meters(weight and height will be combined to report BMI in kg/m^2)

    2. the independent clinical predictor of HFimpEF [3 months]

      prealbumin in mg/L

    Eligibility Criteria

    Criteria

    Ages Eligible for Study:
    18 Years and Older
    Sexes Eligible for Study:
    All
    Accepts Healthy Volunteers:
    No
    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.

    Contacts and Locations

    Locations

    Site City State Country Postal Code
    1 Yihui Kong Harbin Heilongjiang China 150000

    Sponsors and Collaborators

    • Yihui Kong

    Investigators

    None specified.

    Study Documents (Full-Text)

    None provided.

    More Information

    Publications

    None provided.
    Responsible Party:
    Yihui Kong, Professor, First Affiliated Hospital of Harbin Medical University
    ClinicalTrials.gov Identifier:
    NCT06070506
    Other Study ID Numbers:
    • 2020020668
    First Posted:
    Oct 6, 2023
    Last Update Posted:
    Oct 11, 2023
    Last Verified:
    Oct 1, 2023
    Studies a U.S. FDA-regulated Drug Product:
    No
    Studies a U.S. FDA-regulated Device Product:
    No
    Keywords provided by Yihui Kong, Professor, First Affiliated Hospital of Harbin Medical University
    Heart failure
    Left ventricular ejection fraction
    Heart failure with improved ejection fraction
    Deep learning
    Additional relevant MeSH terms:
    Heart Failure

    Study Results

    No Results Posted as of Oct 11, 2023