Heart FailureDHEARTNCT05708846
Improvement of a Digital Health Platform for Remote Monitoring of Patients With Heart Failure
An observational study of Heart Failure, run by humanITcare at 7 sites in 2 countries.
Trial 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… Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Heart Failure
- What is being tested?
- Telemonitoring
- How many people
- 154 participants
- Where
- 7 sitesin 2 countries
- Study dates
- 18 May 2023to 31 Dec 2024
- Phase
- Observational
- Are there results?
- Yes, posted in the registrySummary in the doctors' view
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
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.
Who may be able to join
- 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.
Who cannot join
- 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
Show all 11Show fewer
- 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
Trial locations and contacts
7 sites in 2 countries.
7 sites in 2 countries
Spain4 sites
Hospital de Figueres
Figueres, Girona, 17600
Hospital Universitari de Girona Doctor Josep Trueta
Girona, 17007
Hospital General Universitario Nuestra Señora del Prado
Talavera de la Reina, Toledo, 45600
Hospital Universitario de Torrevieja
Torrevieja, Alicante, 03186
Romania3 sites
Hospital Floreasca
Bucharest, 014461
Colentina Hospital
Bucharest, 020125
Hospital of Galati
Galati, Galați County, 800225
About this study
Official title: 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.
Read the 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
- Being tested
- Telemonitoring
- Keywords
- Heart Failure, Telemonitoring, Remote monitoring, Remote care, Healthcare, Home care, Artificial Intelligence, Chronic patients
- Medical subject headings
- Heart Failure
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
TelemonitoringOther
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
| Study group | Role | What they receive |
|---|---|---|
| Heart Failure patients telemonitored | Patients will be monitored with the vitalera app and platform Receives: Other: Telemonitoring |
What is being measured
The primary outcome is the main question the study is designed to answer.
Number of Patients Included in the Dataset
The dataset will contain the data from HF patients being telemonitored. This outcome shows the number of patients from which data will be used to build a dataset to train ML models for patient health prediction.
Measured: 6 months
Implement ML Models to Improve the Current Alarm-based System Using the Dataset Created
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.
Measured: 6 months
Secondary outcomes (3)
Track All Clinical Interventions and Events to be Included in the Database
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.
Measured: 6 months
Assess Patient and Medical Professional Satisfaction With the Digital Platform
Assess patient and medical professional satisfaction with the digital platform at the study's end by using the "Post-Study Usability Questionnaire" (PSSUQ).
Measured: 6 months
Mean SUS Score to Assess the Usability of the Digital Platform App
Assess the usability of the digital platform at the end of the study by means of the "System Usability Scale" (SUS). The SUS is a standardized tool used to evaluate the usability of digital platforms through a 10-item questionnaire. Each item is rated on a 5-point Likert scale, ranging from "Strongly Disagree" (1) to "Strongly Agree" (5). Scale from 0 to 100. The higher the score the better usablity.
Measured: 6 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted1 Feb 2023
- Study start18 May 2023
- Primary completion31 Dec 2024
- Study completion31 Dec 2024
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Posted resultsParticipant flow, outcome measures and adverse events reported by the sponsor.
- Publications (12)Papers the sponsor has linked to this study.
- Other trials for Heart FailureStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 154 participants (actual)
Sponsor and investigators
- Lead sponsor
- humanITcare
- Sponsor type
- Research network
- Collaborators
- European Innovation Council, Hospital Universitario de Torrevieja, University of Barcelona
- Responsible party
- Sponsor
- Investigators
- Julio César MD Blázquez, Principal investigator, Hospital Universitario de Torrevieja
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05708846
- Sponsor's protocol ID
- FOLLOWHEALTH-2023-01
- Acronym
- DHEART
- Status last verified
- Apr 2025
- First submitted
- 13 Jan 2023
- Last updated
- 9 Apr 2025
- Results posted
- Yes, on ClinicalTrials.gov
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
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.
PubMed 27942354Authors/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.
PubMed 35083827Schiff 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.
PubMed 12798449Brahmbhatt 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.
PubMed 31179018Scherr 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.
PubMed 19687005Koulaouzidis 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.
PubMed 27140340Koehler 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.
PubMed 21444883Lee 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.
PubMed 35040610Guidi 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.
PubMed 26391638Muller-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.
PubMed 15084550Jaarsma 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.
PubMed 19147463Roque 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.
PubMed 27255686
Study at a glance
From the registry record, last updated by the sponsor on 9 Apr 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Observational Study for the Improvement of a Digital Health Platform for Remote Monitoring of Patients With Heart Failure
- ClinicalTrials.gov ID
- NCT05708846
- Acronym / sponsor ID
- DHEART / FOLLOWHEALTH-2023-01
- Conditions
- Heart Failure
- Keywords
- Heart Failure, Telemonitoring, Remote monitoring, Remote care, Healthcare, Home care, Artificial Intelligence, Chronic patients
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 154 participants (actual)
- Lead sponsor
- humanITcareResearch network
- Collaborators
- European Innovation Council, Hospital Universitario de Torrevieja, University of Barcelona
- Study start
- 18 May 2023 (actual)
- Primary completion
- 31 Dec 2024 (actual)
- Study completion
- 31 Dec 2024 (actual)
- First posted
- 1 Feb 2023 (actual)
- Results
- Posted 8 Apr 2025
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Heart Failure patients telemonitored | – | Other: TelemonitoringPatients will be monitored with the vitalera app and platform |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- 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.
Key inclusion criteria (7 listed)
- 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.
Key exclusion criteria (11 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| TelemonitoringAll 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 | Other | Heart Failure patients telemonitored |
Primary outcome
Number of Patients Included in the Dataset
Time frame: 6 months
The dataset will contain the data from HF patients being telemonitored. This outcome shows the number of patients from which data will be used to build a dataset to train ML models for patient health prediction.
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.
3 secondary outcomes
Track All Clinical Interventions and Events to be Included in the Database
Time frame: 6 months
Assess Patient and Medical Professional Satisfaction With the Digital Platform
Time frame: 6 months
Mean SUS Score to Assess the Usability of the Digital Platform App
Time frame: 6 months
Results and safety
Loading the posted results from ClinicalTrials.gov…
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 9 Apr 2025.
- Posted resultsParticipant flow, baseline, all outcome measures and adverse events.
- Background: PubMed 27942354Tripoliti 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.
- Background: PubMed 35083827Authors/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.
- Background: PubMed 12798449Schiff 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.
- Background: PubMed 31179018Brahmbhatt 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.
- Background: PubMed 19687005Scherr 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.
- Background: PubMed 27140340Koulaouzidis 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.
- Background: PubMed 21444883Koehler 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.
- Background: PubMed 35040610Lee 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.
- Background: PubMed 26391638Guidi 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.
- Background: PubMed 15084550Muller-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.
- Background: PubMed 19147463Jaarsma 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.
- Background: PubMed 27255686Roque 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.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Julio César MD Blázquez principal investigator
Hospital Universitario de Torrevieja
7 sites in 2 countries: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Heart Failure, run by humanITcare at 7 sites in 2 countries.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 7 inclusion and 11 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 9 Apr 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 7 sites in Spain, Romania. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Yes. Results are posted in the registry (first posted 8 Apr 2025). A summary is in the For doctors view, and the full results are on ClinicalTrials.gov.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Number of Patients Included in the Dataset (time frame: 6 months). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05708846, which also lists 12 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 9 Apr 2025.