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The aim of this study was to design a deep learning-based trained model to assist in HFimpEF diagnosis.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| 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%. |
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| Measure | Description | Time Frame |
|---|---|---|
| change of left ventricular ejection fraction | left ventricular ejection fraction value in millimeters | 3 months |
| Measure | Description | Time Frame |
|---|---|---|
| change of clinical predictor of EF improvement | weight in kilograms, height in meters(weight and height will be combined to report BMI in kg/m^2) | 3 months |
| the independent clinical predictor of HFimpEF |
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Inclusion Criteria:
Exclusion Criteria:
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The participants in the study are HF patients hospitalized in the Department of Cardiology of the First Affiliated Hospital of Harbin Medical University who had no less than two echocardiograms at baseline and during the follow-up period, between January 2014 to December 2022.
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Yihui Kong | Harbin | Heilongjiang | 150000 | China |
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| ID | Term |
|---|---|
| D006333 | Heart Failure |
| D004194 | Disease |
| ID | Term |
|---|---|
| D006331 | Heart Diseases |
| D002318 | Cardiovascular Diseases |
| D010335 | Pathologic Processes |
| D013568 | Pathological Conditions, Signs and Symptoms |
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prealbumin in mg/L
| 3 months |