Prediction of development and severity of heart failure by machine learning of Holter ECG test
Not Applicable
- Conditions
- Heart failure
- Registration Number
- JPRN-UMIN000046696
- Lead Sponsor
- Fujita Health University
- Brief Summary
Not available
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- Complete: follow-up continuing
- Sex
- All
- Target Recruitment
- 500
Inclusion Criteria
Not provided
Exclusion Criteria
Patients who did not give consent for this study, or patients deemed inappropriate by the principal investigator or sub-investigator
Study & Design
- Study Type
- Observational
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method cardiac death or heart failure admission
- Secondary Outcome Measures
Name Time Method decrease in left ventricular ejection fraction [LVEF] to 35% or less, all cause death, cardiac death, heart failure admission