Prediction of development and severity of heart failure by machine learning of Holter ECG test

Prediction of development and severity of heart failure by machine learning of heart rate variability analysis index - HFHRV

Status

Active, not recruiting

Phases

Unknown

Study type

Observational

Source

JPRN

Registry ID

JPRN-UMIN000046696

Enrollment

500

Registered

2022-01-22

Start date

2021-03-01

Completion date

Unknown

Last updated

2026-06-29

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Heart failure

Interventions

None listed

Eligibility

Sex/Gender

All

Inclusion criteria

Inclusion criteria: 1. Outpatients or stable inpatients at our hospital with left ventricular systolic dysfunction 2. Outpatients or stable inpatients at our hospital with chronic heart failure in sinus rhythm 3. Outpatients or stable inpatients at our hospital with chronic heart failure in atrial fibrillation

Exclusion criteria

Exclusion criteria: Patients who did not give consent for this study, or patients deemed inappropriate by the principal investigator or sub-investigator

Design outcomes

Primary

Measure	Time frame
cardiac death or heart failure admission	—

Secondary

Measure	Time frame
decrease in left ventricular ejection fraction [LVEF] to 35% or less, all cause death, cardiac death, heart failure admission	—

Countries

Japan

Contacts

Public ContactHideo Izawa

Fujita Health University Department of Cardiology

izawa@fujita-hu.ac.jp0562-93-2312

Outcome results