Prediction of Heart-Failure with Machine Learning

NCT06819618 | Recruiting

• 1 other identifier: 81X3300116
• Study Type: observational
• Target: 32
• Locations: 1 country
• Sites: 1

Brief Summary

In this monocentric observational study the research question is to what extent data collected via Apple Watch can predict the heart failure status of decompensated HF patients. For this purpose, physiological data from the Apple Watch (such as single-lead electrocardiogram, SpO2, respiratory rate, step count, nighttime temperature, etc.) will be extracted and used as predictor variables to forecast outcomes like risk of decompensation and rehospitalization within the follow-up period. Since this is a data-driven study, additional data collected as part of guideline-compliant treatment will also be included.

Conditions

Heart Failure with Reduced Ejection Fraction; Heart Failure; Heart Failure，Congestive; Heart Failure Acute; Heart Failure; with Decompensation

Keywords

Heart Failure; Wearables; Monitoring; Apple Watch

Outcome Measures

Primary Outcomes (1)

• Re-hospitalization

  Re-hospitalization due to decompensated Heart Failure

  3 Months after discharge from hospital

Secondary Outcomes (3)

• Adherence

  From study enrollment until discharge (individual, usually from 5 to 15 days)

• nTproBNP

  At enrollment (t1), end of hospital stay = discharge (t2) and 3 months after discharge (t3)

• Kansas City Cardiomyopathy-12 Score

  At enrollment (t1), end of hospital stay = discharge (t2) and 3 months after discharge (t3)

Study Arms (1)

Study Cohort

Other: Monitoring with Apple Watch

Interventions

Monitoring with Apple Watch OTHER

Patients will receive Apple Watch for Monitoring of Biosignals throughout the hospital stay

Study Cohort

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

All acutely decompensated patients hospitalized for heart failure with reduced ejection fraction in the UMC Goettingen

You may qualify if:

• age over 17
• HFrEF with LV-EF under 41
• hospitalized for decompensated heart failure with a) nTproBNP over 1000 AND b) willing to participate AND c) at least one out of three clinical signs (edema, pleural effusion, ascites)

You may not qualify if:

• life expectancy under 6 months due to non-cardiac conditions
• inability to use smartwatch
• severe valvular lesions

Sponsors & Collaborators

• University Medical Center Goettingen: lead

Study Sites (1)

University Medical Center Goettingen

Goettigen, Lower Saxony, 37075, Germany

RECRUITING

MeSH Terms

Conditions

Heart Failure

Condition Hierarchy (Ancestors)

Heart Diseases; Cardiovascular Diseases

Central Study Contacts

Soeren Sievers, Dr. med.

CONTACT

00495513965044; soeren.sievers@med.uni-goettingen.de

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Resident

Study Record Dates

• First Submitted: January 5, 2025
• First Posted: February 11, 2025
• Study Start: April 1, 2024
• Primary Completion: May 31, 2025
• Study Completion: May 31, 2025
• Last Updated: February 11, 2025

Record last verified: 2025-01

Locations

DE (1)