Heart Failure Patient Management and Interventions Using Continuous Patient Monitoring Outside Hospitals and Real-world Data

NCT ID: NCT07008729

Last Updated: 2026-02-04

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

ACTIVE_NOT_RECRUITING

Clinical Phase

NA

Total Enrollment

390 participants

Study Classification

INTERVENTIONAL

Study Start Date

2024-04-24

Study Completion Date

2026-08-31

Brief Summary

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The goal of this study is to create a digital platform for managing patients with chronic heart failure, those with long-term ventricular assistance, or heart transplant recipients. This platform aims to help doctors make clinical decisions and change treatments based on continuous monitoring and the collection of medical, clinical, physiological, behavioral, psychosocial, and real-world data from these patients.

The ultimate goal is to reduce mortality and hospitalization rates for this group of patients while improving their quality of life, safety, and well-being.

To do this, participants will be divided into two groups:

* Intervention Group: The data collected by the platform will be available to their treating doctors.
* Control Group: Doctors will not have access to the data.

All participating patients will receive a set of devices and sensors to collect data such as vital signs, physical activity, sleep quality, psychological and nutritional status, and environmental data. All this information will be gathered through a mobile app designed for the study.

The follow-up will last for 18 months, during which there will be 4 in-person medical visits (spaced 4 months apart). Participation in the study won't affect patients' scheduled medical visits related to their illness or their usual treatment.

Detailed Description

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Specifically, the devices that will be given to the patients and their required use will be as follows:

A Smartphone: This will have the app with the platform designed for the study. The patient must interact with it daily to enter data.

A Blood Pressure Monitor: The patient will need to take their blood pressure every day and send it via Bluetooth to the mobile platform (the connection setup will be done by the study team before giving it to the patients).

An Oxygen Saturation Monitor: The patient will need to check their oxygen levels daily and send the data via Bluetooth to the mobile platform (the connection setup will be done by the study team before giving it to the patients).

A Scale: The patient will need to weigh themselves daily and transmit the data via Bluetooth to the mobile platform (the connection setup will be done by the study team before giving it to the patients).

A Smartwatch: The patient must send data on their physical activity and sleep daily via Bluetooth to the mobile platform (the connection setup will be done by the study team before giving it to the patients).

Home Humidity and Temperature Sensors and a Home Gateway Device (Raspberry Pi): The patient will need to connect this last device to their home internet network. The connection setup will be done by the study team before giving it to the patients.

There are other data and information that the patient must manually enter into the app, such as daily body temperature, symptoms they experience (like increased fatigue or shortness of breath), or data on the functioning of the long-term ventricular assistance (controller parameters or alarm presence). Additionally, the app will show the patient's current medication, which they need to confirm they have taken correctly.

The scheduled visits during the study will take place at the Baseline and every four months. These visits will include an in-person medical visit, an electrocardiogram, a blood test, and a 6-minute walk test. Also, at each visit, a series of questionnaires will be given to assess the patient's cognitive, nutritional, and psychological state, as well as their quality of life and that of their primary caregivers. Some visits will also include more specific cardiac tests, like an echocardiogram or a cardiopulmonary exercise test.

If during the study follow-up, the treating doctors unexpectedly discover any information that affects the patient's health or quality of life, the patient will be informed immediately, even if it is unrelated to the study's purpose.

All data recorded throughout the study will be stored anonymously by the study platform.

Conditions

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Chronic Heart Failure Heart Transplant Recipient Left Ventricular Assist Device

Study Design

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Allocation Method

RANDOMIZED

Intervention Model

PARALLEL

Primary Study Purpose

DIAGNOSTIC

Blinding Strategy

DOUBLE

Participants Caregivers
The care providers will be masked to the data collected by the control group

Study Groups

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Intervention group

The RETENTION platform will offer to the clinician in the experimental arm all the data gathered by the devices (smart watch, scale, oximeter, blood pressure monitor, temperature, medication adherence, weather and pollution indexes). along with artificial intelligence recommendations.

Group Type EXPERIMENTAL

Heart monitoring tools (data revision)

Intervention Type DEVICE

data collected by the devices will be reviewed by the treating physician in the intervention group

Control group

Patients in the control group will be monitored but the data will not be available to the clinicians nor the artificial intelligence recommendations.

Group Type PLACEBO_COMPARATOR

Control (Standard treatment)

Intervention Type OTHER

Patients in the control group will be monitored but the information will not be available for the clinicians nor the artificial intelligence recommendations

Interventions

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Heart monitoring tools (data revision)

data collected by the devices will be reviewed by the treating physician in the intervention group

Intervention Type DEVICE

Control (Standard treatment)

Patients in the control group will be monitored but the information will not be available for the clinicians nor the artificial intelligence recommendations

Intervention Type OTHER

Eligibility Criteria

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Inclusion Criteria

* Age of 18-75 years.
* Ability to understand and provide consent in order to participate in the study.
* Have a cognitive assessment score of \> 22 as assessed by the Montreal Cognitive Assessment (MoCA), ( adults without or with mild cognitive impairment at most).
* Have a depression score as assessed by Personal Health Questionnaire-9 (PHQ-9) score \< 10.
* Provide written informed consent.
* For Heart Failure patients: 1) have a diagnosis of symptomatic heart failure (class II or III according to the New York Heart Association Classification); 2) have echocardiographically determined left ventricular ejection fraction (LVEF) ≤40%; 3) have been hospitalized due to cardiovascular reasons or had urgent visit to the Emergency Department due to decompensated HF that required administration of intravenous diuretics within the last 12 months before randomization (Currently hospitalized patients can be included provided they do not fulfill exclusion criterion of being currently with in-hospital administration of IV diuretics/vasoactive/inotropic drugs).
* For patients with ventricular assist devices (VAD): have left ventricular assist device (LVAD) implanted either as destination therapy or as bridge-to-transplantation, within at least 90 days and no longer than 48 months before randomization
* for heart transplant recipients: have been discharged following heart transplantation within at least 30 days and no longer than 36 months before study randomization.

Exclusion Criteria

* Is clinically unstable at the time of randomization as defined by: administration of intravenous diuretics during the last 12 hours or intravenous vasodilators or inotropes during the last 48 hours before randomization.
* Has acute coronary syndrome within the last 30 days before randomization.
* Has acute inflammatory heart disease, (for instance, acute myocarditis), within 90 days prior to randomization.
* Has a planned revascularisation, cardiac resynchronization therapy (CRT) implantation or any valvular procedures within 3 months after randomization
* Has known current alcohol or illicit drug abuse.
* Shows significant impairment or unwillingness to use the telemonitoring equipment (for instance, dementia, impaired self-determination, lacking ability to communicate).
* Has any severe non-cardiovascular disease limiting life expectancy to less than 1 year.
* Is pregnant.
* Participates currently in other telemonitoring programs/studies using smart devices
Minimum Eligible Age

18 Years

Maximum Eligible Age

75 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Hannover Medical School

OTHER

Sponsor Role collaborator

Attikon Hospital

OTHER

Sponsor Role collaborator

IRCCS Azienda Ospedaliero-Universitaria di Bologna

OTHER

Sponsor Role collaborator

Onassis Cardiac Surgery Centre

OTHER

Sponsor Role collaborator

Hospital Universitario Ramon y Cajal

OTHER

Sponsor Role collaborator

Puerta de Hierro University Hospital

OTHER

Sponsor Role lead

Responsible Party

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Mercedes Rivas-Lasarte

Principal Investigator, MD PHD, Cardiology Department

Responsibility Role PRINCIPAL_INVESTIGATOR

Principal Investigators

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Maria Haritou, PhD

Role: STUDY_CHAIR

ICCS

Locations

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Medizinische Hochschule Hannover ('Mhh')

Hanover, , Germany

Site Status

National and Kapodistrian University of Athens

Athens, , Greece

Site Status

Onassis Cardiac Surgery Center

Athens, , Greece

Site Status

Alma Mater Studiorum - Universita Di Bologna

Bologna, , Italy

Site Status

Hospital Universitario Puerta de Hierro

Majadahonda, Madrid, Spain

Site Status

Hospital Universitario Ramón Y Cajal

Madrid, , Spain

Site Status

Countries

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Germany Greece Italy Spain

References

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Bennett MK, Shao M, Gorodeski EZ. Home monitoring of heart failure patients at risk for hospital readmission using a novel under-the-mattress piezoelectric sensor: A preliminary single centre experience. J Telemed Telecare. 2017 Jan;23(1):60-67. doi: 10.1177/1357633X15618810. Epub 2016 Jul 9.

Reference Type BACKGROUND
PMID: 26670209 (View on PubMed)

Scott IA, Scuffham P, Gupta D, Harch TM, Borchi J, Richards B. Going digital: a narrative overview of the effects, quality and utility of mobile apps in chronic disease self-management. Aust Health Rev. 2020 Feb;44(1):62-82. doi: 10.1071/AH18064.

Reference Type BACKGROUND
PMID: 30419185 (View on PubMed)

Ski CF, Thompson DR, Brunner-La Rocca HP. Putting AI at the centre of heart failure care. ESC Heart Fail. 2020 Oct;7(5):3257-3258. doi: 10.1002/ehf2.12813. Epub 2020 Jun 17. No abstract available.

Reference Type BACKGROUND
PMID: 32558251 (View on PubMed)

McConnell MV, Turakhia MP, Harrington RA, King AC, Ashley EA. Mobile Health Advances in Physical Activity, Fitness, and Atrial Fibrillation: Moving Hearts. J Am Coll Cardiol. 2018 Jun 12;71(23):2691-2701. doi: 10.1016/j.jacc.2018.04.030.

Reference Type BACKGROUND
PMID: 29880130 (View on PubMed)

Dorsey ER, Yvonne Chan YF, McConnell MV, Shaw SY, Trister AD, Friend SH. The Use of Smartphones for Health Research. Acad Med. 2017 Feb;92(2):157-160. doi: 10.1097/ACM.0000000000001205.

Reference Type BACKGROUND
PMID: 27119325 (View on PubMed)

Liu L, Stroulia E, Nikolaidis I, Miguel-Cruz A, Rios Rincon A. Smart homes and home health monitoring technologies for older adults: A systematic review. Int J Med Inform. 2016 Jul;91:44-59. doi: 10.1016/j.ijmedinf.2016.04.007. Epub 2016 Apr 19.

Reference Type BACKGROUND
PMID: 27185508 (View on PubMed)

Goswami R. The current state of artificial intelligence in cardiac transplantation. Curr Opin Organ Transplant. 2021 Jun 1;26(3):296-301. doi: 10.1097/MOT.0000000000000875.

Reference Type BACKGROUND
PMID: 33938466 (View on PubMed)

Shahbazi F, Asl BM. Generalized discriminant analysis for congestive heart failure risk assessment based on long-term heart rate variability. Comput Methods Programs Biomed. 2015 Nov;122(2):191-8. doi: 10.1016/j.cmpb.2015.08.007. Epub 2015 Aug 24.

Reference Type BACKGROUND
PMID: 26344584 (View on PubMed)

Tripoliti EE, Papadopoulos TG, Karanasiou GS, Naka KK, Fotiadis DI. Heart Failure: Diagnosis, Severity Estimation and Prediction of Adverse Events Through Machine Learning Techniques. Comput Struct Biotechnol J. 2016 Nov 17;15:26-47. doi: 10.1016/j.csbj.2016.11.001. eCollection 2017.

Reference Type BACKGROUND
PMID: 27942354 (View on PubMed)

Yun JE, Park JE, Park HY, Lee HY, Park DA. Comparative Effectiveness of Telemonitoring Versus Usual Care for Heart Failure: A Systematic Review and Meta-analysis. J Card Fail. 2018 Jan;24(1):19-28. doi: 10.1016/j.cardfail.2017.09.006. Epub 2017 Sep 20.

Reference Type BACKGROUND
PMID: 28939459 (View on PubMed)

Savarese G, Lund LH. Global Public Health Burden of Heart Failure. Card Fail Rev. 2017 Apr;3(1):7-11. doi: 10.15420/cfr.2016:25:2.

Reference Type BACKGROUND
PMID: 28785469 (View on PubMed)

Ong MK, Romano PS, Edgington S, Aronow HU, Auerbach AD, Black JT, De Marco T, Escarce JJ, Evangelista LS, Hanna B, Ganiats TG, Greenberg BH, Greenfield S, Kaplan SH, Kimchi A, Liu H, Lombardo D, Mangione CM, Sadeghi B, Sadeghi B, Sarrafzadeh M, Tong K, Fonarow GC; Better Effectiveness After Transition-Heart Failure (BEAT-HF) Research Group. Effectiveness of Remote Patient Monitoring After Discharge of Hospitalized Patients With Heart Failure: The Better Effectiveness After Transition -- Heart Failure (BEAT-HF) Randomized Clinical Trial. JAMA Intern Med. 2016 Mar;176(3):310-8. doi: 10.1001/jamainternmed.2015.7712.

Reference Type BACKGROUND
PMID: 26857383 (View on PubMed)

Related Links

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https://www.retention-project.eu/

Oficial project's web site. It provides information about the project to every user.

Other Identifiers

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PI 152/22

Identifier Type: OTHER

Identifier Source: secondary_id

H2020-SC1-BHC-2018-2020 GA 965

Identifier Type: -

Identifier Source: org_study_id

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