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

NCT ID: NCT05708846

Last Updated: 2025-04-09

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 154 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2023-05-18
• Study Completion Date: 2024-12-31

Brief Summary

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.

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.

Vitalera 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 vitalera'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 vitalera'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.

Conditions

Heart Failure

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: PROSPECTIVE

Study Groups

Heart Failure patients telemonitored

Patients will be monitored with the vitalera app and platform

Telemonitoring

Intervention Type: OTHER

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

Interventions

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

Intervention Type: OTHER

Eligibility Criteria

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

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

European Innovation Council

OTHER

Sponsor Role: collaborator

Hospital Universitario de Torrevieja

UNKNOWN

Sponsor Role: collaborator

University of Barcelona

OTHER

Sponsor Role: collaborator

humanITcare

NETWORK

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Julio César MD Blázquez

Role: PRINCIPAL_INVESTIGATOR

Hospital Universitario de Torrevieja

Locations

Hospital of Galati

Galati, Galați County, Romania

Hospital Floreasca

Bucharest, , Romania

Colentina Hospital

Bucharest, , Romania

Hospital Universitario de Torrevieja

Torrevieja, Alicante, Spain

Hospital de Figueres

Figueres, Girona, Spain

Hospital General Universitario Nuestra Señora del Prado

Talavera de la Reina, Toledo, Spain

Hospital Universitari de Girona Doctor Josep Trueta

Girona, , Spain

Countries

Romania; Spain

References

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)

Authors/Task Force Members:; McDonagh TA, Metra M, Adamo M, Gardner RS, Baumbach A, Bohm M, Burri H, Butler J, Celutkiene J, Chioncel O, Cleland JGF, Coats AJS, Crespo-Leiro MG, Farmakis D, Gilard M, Heymans S, Hoes AW, Jaarsma T, Jankowska EA, Lainscak M, Lam CSP, Lyon AR, McMurray JJV, Mebazaa A, Mindham R, Muneretto C, Francesco Piepoli M, Price S, Rosano GMC, Ruschitzka F, Kathrine Skibelund A; ESC Scientific Document Group. 2021 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure: Developed by the Task Force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC). With the special contribution of the Heart Failure Association (HFA) of the ESC. Eur J Heart Fail. 2022 Jan;24(1):4-131. doi: 10.1002/ejhf.2333.

Reference Type: BACKGROUND

PMID: 35083827 (View on PubMed)

Schiff GD, Fung S, Speroff T, McNutt RA. Decompensated heart failure: symptoms, patterns of onset, and contributing factors. Am J Med. 2003 Jun 1;114(8):625-30. doi: 10.1016/s0002-9343(03)00132-3.

Reference Type: BACKGROUND

PMID: 12798449 (View on PubMed)

Brahmbhatt DH, Cowie MR. Remote Management of Heart Failure: An Overview of Telemonitoring Technologies. Card Fail Rev. 2019 May 24;5(2):86-92. doi: 10.15420/cfr.2019.5.3. eCollection 2019 May.

Reference Type: BACKGROUND

PMID: 31179018 (View on PubMed)

Scherr D, Kastner P, Kollmann A, Hallas A, Auer J, Krappinger H, Schuchlenz H, Stark G, Grander W, Jakl G, Schreier G, Fruhwald FM; MOBITEL Investigators. Effect of home-based telemonitoring using mobile phone technology on the outcome of heart failure patients after an episode of acute decompensation: randomized controlled trial. J Med Internet Res. 2009 Aug 17;11(3):e34. doi: 10.2196/jmir.1252.

Reference Type: BACKGROUND

PMID: 19687005 (View on PubMed)

Koulaouzidis G, Iakovidis DK, Clark AL. Telemonitoring predicts in advance heart failure admissions. Int J Cardiol. 2016 Aug 1;216:78-84. doi: 10.1016/j.ijcard.2016.04.149. Epub 2016 Apr 21.

Reference Type: BACKGROUND

PMID: 27140340 (View on PubMed)

Koehler F, Winkler S, Schieber M, Sechtem U, Stangl K, Bohm M, Boll H, Baumann G, Honold M, Koehler K, Gelbrich G, Kirwan BA, Anker SD; Telemedical Interventional Monitoring in Heart Failure Investigators. Impact of remote telemedical management on mortality and hospitalizations in ambulatory patients with chronic heart failure: the telemedical interventional monitoring in heart failure study. Circulation. 2011 May 3;123(17):1873-80. doi: 10.1161/CIRCULATIONAHA.111.018473. Epub 2011 Mar 28.

Reference Type: BACKGROUND

PMID: 21444883 (View on PubMed)

Lee S, Chu Y, Ryu J, Park YJ, Yang S, Koh SB. Artificial Intelligence for Detection of Cardiovascular-Related Diseases from Wearable Devices: A Systematic Review and Meta-Analysis. Yonsei Med J. 2022 Jan;63(Suppl):S93-S107. doi: 10.3349/ymj.2022.63.S93.

Reference Type: BACKGROUND

PMID: 35040610 (View on PubMed)

Guidi G, Pollonini L, Dacso CC, Iadanza E. A multi-layer monitoring system for clinical management of Congestive Heart Failure. BMC Med Inform Decis Mak. 2015;15 Suppl 3(Suppl 3):S5. doi: 10.1186/1472-6947-15-S3-S5. Epub 2015 Sep 4.

Reference Type: BACKGROUND

PMID: 26391638 (View on PubMed)

Muller-Nordhorn J, Roll S, Willich SN. Comparison of the short form (SF)-12 health status instrument with the SF-36 in patients with coronary heart disease. Heart. 2004 May;90(5):523-7. doi: 10.1136/hrt.2003.013995.

Reference Type: BACKGROUND

PMID: 15084550 (View on PubMed)

Jaarsma T, Arestedt KF, Martensson J, Dracup K, Stromberg A. The European Heart Failure Self-care Behaviour scale revised into a nine-item scale (EHFScB-9): a reliable and valid international instrument. Eur J Heart Fail. 2009 Jan;11(1):99-105. doi: 10.1093/eurjhf/hfn007.

Reference Type: BACKGROUND

PMID: 19147463 (View on PubMed)

Roque NA, Boot WR. A New Tool for Assessing Mobile Device Proficiency in Older Adults: The Mobile Device Proficiency Questionnaire. J Appl Gerontol. 2018 Feb;37(2):131-156. doi: 10.1177/0733464816642582. Epub 2016 Apr 11.

Reference Type: BACKGROUND

PMID: 27255686 (View on PubMed)

Provided Documents

Document Type: Study Protocol and Statistical Analysis Plan

View Document

Document Type: Informed Consent Form

View Document

Other Identifiers

FOLLOWHEALTH-2023-01

Identifier Type: -

Identifier Source: org_study_id