Chronic Heart Failure Chronic heart failure
Conditions
Interventions
None listed
Sponsors
Miyagawa Shigeru
Eligibility
Sex/Gender
All
Inclusion criteria
Inclusion criteria: 1) Chronic heart failure patients with ACCF/AHA heart failure stage classification Stage C or D 2) Patients with NT-proBNP level higher than 900 pg/ml or BNP lever higher than 200 pg/ml at screening* (*: If both are measured, prioritize NT-proBNP) 3)Patients aged 18 years or older at the time of informed consent 4) New York Heart Association (NYHA) class II or higher 5) Patients who can use equipment (wristwatch type wearable device/smartphone) (completed training)
Exclusion criteria
Exclusion criteria: 1) Patients who have difficulty being auscultated near the left nipple 2)Patients who a doctor has judged to have heart failure, mainly in the right heart failure 3) Patients whose heart sounds cannot be fully confirmed by a doctor's auscultation 4) For outpatients, patients with a weight gain of 5% or more from the weight (optimal weight) measured around the last day of hospitalization for heart failure. 5) For outpatients, patients with edema on the front of both lower legs 6) Other patients who are judged to be inappropriate by the research director (co-investigator)
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| Accuracy, sensitivity, and specificity by algorithm-derived diagnostic results when referring to positive changes ( greater than 30% increase compared to basal NT-proBNP is considered positive change) in NT-proBNP at a hospital visit. | — |
Secondary
| Measure | Time frame |
|---|---|
| 1. Accuracy, sensitivity, and specificity of the diagnostic results of the algorithm when referring to positive changes in body weight at a hospital visit (A positive change is defined as a 5% increase in body weight compared to the base NT-proBNP) 2. Correlation between positive change in weight at visit (5% weight gain compared to baseline NT-proBNP is considered a positive change) and algorithmic diagnostic outcome 3. Correlation between his NT-proBNP positive change at the visit (an increase of 30% or more compared to his NT-proBNP at baseline is considered a positive change) and the diagnostic outcome of the algorithm 4. Correlation between algorithmic diagnostic results and presence or absence of shortness of breath on exertion 5. Correlation between the presence or absence of orthopnea and algorithm diagnosis results 6. Correlation between rehospitalization due to exacerbation of heart failure and algorithmic diagnostic results 7. Correlation between estimated pulmonary artery pressure by transthoracic echocardiography and algorithmic diagnostic results (Pearson) 8. Accuracy, sensitivity, and specificity of the diagnostic results of the algorithm for the occurrence of unscheduled hospital visits (including hospitalization) due to heart failure 9. Device and App Usage Adherence Rates | — |
Contacts
Public ContactYusuke Misumi
Osaka University Hospital
Outcome results
None listed