Improvement of a Digital Health Platform for Remote Monitoring of Patients With Heart Failure (DHEART)

March 26, 2024 updated by: humanITcare

Observational Study for the Improvement of a Digital Health Platform for Remote Monitoring of Patients With Heart Failure

In the present project, we propose to run an observational study in order to create a huge dataset with telemonitoring data from heart failure (HF) patients. The dataset will contain physiological measurements, socio-demographic data, risk factor information, medication tracking, symptomatology, clinical events and health-related questionnaire answers from each patient. Furthermore, health-related alarms will be delivered to the medical professionals whenever a measure from a patient is out of a predefined clinical range. These alarms and its defined level of relevance (indicated by the medical professionals) will also be Included in the dataset. With the annotated dataset we will be able to implement and train Machine Learning (ML) models that will improve the alarm-based system by making it more robust, trustworthy and reliable.

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

Detailed Description

Heart Failure (HF) is a prevalent and fatal clinical syndrome that affects the quality of life of millions of people worldwide. Between 17% and 45% of patients suffering from HF die within the first year and the remaining die within 5 years. Furthermore, those patients have a high risk of rehospitalization, their associated healthcare costs are huge, and the higher the life expectancy, the higher the disease's prevalence. HF symptoms commonly include shortness of breath, excessive tiredness, and leg swelling which may be worsened with decompensation, and thus displacement to medical centers represents a handicap for such individuals. Remote monitoring technologies provide a feasible solution that allows earlier decompensation identification and better adherence to lifestyle changes and medication. Although telemonitoring by smartphones showed the potential to reduce both the frequency and the duration of HF hospitalizations, there was no association with the reduction of all-cause mortality. Thus, it indicates there is a need to look for more effective and precise methodologies. In recent years, the use of wearable devices that allow daily monitoring of patient's physiological data combined with Artificial Intelligence (AI) has shown immense potential in predicting cardiovascular-related diseases, their adverse events and patient's health status, including that of patients with HF.

HumanITcare has implemented a cloud platform and an alarm-based system for remote monitoring of patients that delivers health alarms when a patient's biomedical measurement is out of a predefined range. The platform relieves clinicians and caretakers of going through each patient's data to check for anomalies, accelerating the decision-making process and reducing hospital consultations. However, the system is creating many straightforward alarms that are finally being discarded after evaluation by the medical professional. In the present project, we propose to run an observational study in order to create a huge dataset with patients' clinical data that will contain annotations regarding the relevance of each alarm. With the annotated dataset we will be able to implement and train Machine Learning (ML) models that will improve the remote monitoring system and its alarm-based system by making it more robust, trustworthy and reliable.

This study is being conducted in the framework of a European project promoted by the European Innovation Council (EIC). An earlier version of the platform was validated in a study conducted in 2020 at Hospital de Torrevieja focused on HF. The rationale for this study is in line with HumanITcare's goal of incorporating artificial intelligence tools to optimize the digital platform. While this study is focused on the creation of a diverse and labeled dataset and on the development of artificial intelligence event-prediction algorithms, a forthcoming second study will focus on the validation of the algorithms to assess their clinical effectiveness.

This is an observational study involving a European network of hospitals. The study consists of continuous remote patient monitoring using HumanITcare's digital platform and the supplied devices (tensiometer, wearable, scale and oximeter). For 6 months, a total of 500 patients suffering from HF will have their physiological constants monitored.

Patients will be included in the study based on the eligibility criteria and must complete the informed consent provided. Each hospital will decide when to include their patients according to their particular clinical practice (either in the process of discharge planning or during the first follow-up visit, i.e.. 1 or 2 weeks after discharge). The recruitment period is defined as 6 months. That means patients will be incorporated into the study from its start until the sixth month. The last subject included in the study will then finish the study after one year from the first day of the study. Medical professionals from each hospital will be in charge of recruiting the participants. The recruitment rate is specific for each hospital, and it may vary depending on the month.

There is no power calculation associated with the study since the main objective of the study is to gather a dataset in order to train ML models. Once the algorithms are developed, model performance in terms of accuracy will be evaluated by means of C statistic, the area under the receiver operating characteristic curve, and creation of a calibration plot. Furthermore, the models will be evaluated in terms of fairness and potential bias using metrics including statistical parity, group fairness, equalized odds and predictive equality.

Study Type

Observational

Enrollment (Estimated)

500

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Contact Backup

Study Locations

  • Romania
      • Bucharest, Romania, 014461
        • Recruiting
        • Hospital Floreasca
      • Bucharest, Romania, 020125
        • Recruiting
        • Colentina Hospital
    • Galati
      • Galaţi, Galati, Romania, 800225
        • Recruiting
        • Hospital of Galati
  • Spain
      • Girona, Spain, 17007
        • Recruiting
        • Hospital Universitari de Girona Doctor Josep Trueta
    • Alicante
      • Torrevieja, Alicante, Spain, 03186
        • Recruiting
        • Hospital Universitario de Torrevieja
        • Contact:
          • Julio César MD Blazquez Encinar
    • Girona
      • Figueres, Girona, Spain, 17600
        • Recruiting
        • Hospital de Figueres
    • Toledo
      • Talavera De La Reina, Toledo, Spain, 45600
        • Recruiting
        • Hospital General Universitario Nuestra Señora del Prado

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

18 years and older (Adult, Older Adult)

Accepts Healthy Volunteers

No

Sampling Method

Non-Probability Sample

Study Population

Heart Failure patients will be recruited from a diversity of hospitals mainly from Spain but also from countries in the south of Europe and Eastern Europe.

Description

Inclusion Criteria:

  • Heart failure (HF) patients with NYHA Functional Class >= II (according to 2021 EU guidelines).
  • Patients older than 18 years old.
  • Patients who have suffered an acute decompensation of HF (first and recurrent) in the 30 days prior to enrollment in the study.
  • NT-pro BNP ≥300 pg/ml at the moment of hospitalization for patients without ongoing atrial fibrillation/flutter. If ongoing atrial fibrillation/flutter, NT-pro BNP must be ≥600 pg/mL
  • Patients must have had an echocardiogram during their HF hospitalization or in the previous 12 months.
  • Prior to initiating any procedures, the hospital will ensure that the patient obtains an informed consent document, if applicable.
  • All patients will be eligible regardless of the level of LVEF: HFrEF, HFmrEF, and HFpEF.

Exclusion Criteria:

  • Oncology patients with metastasis or with chemotherapy treatment ongoing
  • Patients participating in other studies or trials.
  • Patients not willing to participate.
  • Patients over 150 kg
  • Patients who do not use Catalan, Spanish, English, Portuguese, Italian, Dutch, German, Swedish, Hungarian, Romanian or French.
  • Patients without a mobile phone
  • Patients without internet connexion
  • Patients with moderate or severe cognitive impairment without a competent caregiver
  • Patients with serious psychiatric illness
  • Patients with planned cardiac surgery
  • Patients with planned heart transplantation or LVAD implant

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
Heart Failure patients telemonitored
Patients will be monitored with the HumanITcare app and platform
Other: Telemonitoring

All patients will be telemonitored in order to create a labeled and diverse dataset that will include the following data:

Physiological parameters (measured periodically), socio-demographic data, risk factors, medication tracking, symptomatology questionnaire for patients, NYHA-class, clinical interventions, health questionnaire answers, classified alarms with their respective timestamp and annotation by the MD, and measurement ranges for each personalized alarm and their changes

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Create a labeled and diverse dataset
Time Frame: 6 months
The dataset will contain the data from HF patients being telemonitored.
6 months
Implement ML models to improve the current alarm-based system using the dataset created
Time Frame: 6 months

The models should:

Provide a relevance level for each new alarm by reducing the number of irrelevant alarms and thus fostering personalized follow-up.

Be robust across different new hospitals and reliable and fair across different target populations, considering the diverse sociodemographic data that will be available in the dataset.
6 months

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Track all clinical interventions and events to be included in the database
Time Frame: 6 months

With the registered information, develop and implement ML event prediction algorithms that will add new self-generated alarms to the system.

These alarms should forecast:

Untracked hospital interventions, such as UCI visits or hospital readmissions. Changes of medication with their particular estimated dose. Clinical events, such as mortality.
6 months
Assess patient and medical professional satisfaction with the digital platform
Time Frame: 6 months
Assess patient and medical professional satisfaction with the digital platform at the study's end by using the "Post-Study Usability Questionnaire" (PSSUQ).
6 months
Assess the usability of the digital platform
Time Frame: 6 months
Assess the usability of the digital platform at the end of the study by means of the "System Usability Scale" (SUS). The questionnaire will be delivered to patients and medical professionals
6 months

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

humanITcare

Collaborators

University of Barcelona
Hospital Universitario de Torrevieja
European Innovation Council

Investigators

  • Principal Investigator: Julio César MD Blázquez, Hospital Universitario de Torrevieja

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

May 18, 2023

Primary Completion (Estimated)

December 31, 2024

Study Completion (Estimated)

December 31, 2024

Study Registration Dates

First Submitted

January 13, 2023

First Submitted That Met QC Criteria

January 24, 2023

First Posted (Actual)

February 1, 2023

Study Record Updates

Last Update Posted (Actual)

March 27, 2024

Last Update Submitted That Met QC Criteria

March 26, 2024

Last Verified

March 1, 2024

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • FOLLOWHEALTH-2023-01

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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