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Prediction of Heart-Failure with Machine Learning

ConditionsHeart Failure with Reduced Ejection FractionHeart FailureHeart Failure，CongestiveHeart Failure AcuteHeart Failure; with Decompensation
Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age18+
SponsorUniversity Medical Center Goettingen

About this trial

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.

Eligibility criteria

This trial does not accept healthy volunteers

Qualifiers

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)

Disqualifiers

life expectancy under 6 months due to non-cardiac conditions

inability to use smartwatch

severe valvular lesions

Trial population

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

Trial design

Design model

Case-only

Time perspective

Prospective

Treatments tested in this trial

  • Monitoring with Apple Watch

    Other intervention

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

Treatment groups

32 Participants
are divided into 1 treatment group
Group A: Study Cohort1 intervention

Trial outcomes

Primary outcomes

1

Re-hospitalization

Re-hospitalization due to decompensated Heart Failure

Time frame
3 Months after discharge from hospital

Secondary outcomes

1

Adherence

Adherence to Smart Watch (wearing, charging, ECG-recording)

Time frame
From study enrollment until discharge (individual, usually from 5 to 15 days)
2

nTproBNP

Prediction of nTproBNP-Level

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

Kansas City Cardiomyopathy-12 Score

Prediction of KCCQ-12 Score (0-100)

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

Other outcomes

Locations

1

Map coordinates are unavailable for these locations. Locations are shown below instead.

University Medical Center GoettingenRecruiting37075, Goettigen, Lower SaxonyGermanyGermany

Sponsors and contacts

Click on the lead sponsor to view all of their trials.

University Medical Center Goettingen

Lead sponsor

Source ClinicalTrials.gov
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