Diagnosis, Heart Failure
Conditions
Keywords
Heart failure, Left ventricular ejection fraction, Heart failure with improved ejection fraction, Deep learning
Brief summary
The aim of this study was to design a deep learning-based trained model to assist in HFimpEF diagnosis.
Interventions
None listed
Sponsors
Yihui Kong
Study design
Observational model
OTHER
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| change of left ventricular ejection fraction | 3 months | left ventricular ejection fraction value in millimeters |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| 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) |
| the independent clinical predictor of HFimpEF | 3 months | prealbumin in mg/L |
Countries
China
Outcome results
None listed