Clinical trial NCT05708846: Improvement of a Digital Health Platform for Remote Monitoring of Patients With Heart Failure
Official title: Observational Study for the Improvement of a Digital Health Platform for Remote Monitoring of Patients With Heart Failure
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Heart Failure
Keywords: Heart Failure
Telemonitoring
Remote monitoring
Remote care
Healthcare
Home care
Artificial Intelligence
Chronic patients
Lead sponsor: humanITcare
Sponsor class: NETWORK
Collaborators: name: European Innovation Council
name: Hospital Universitario de Torrevieja
name: University of Barcelona
First posted: 2023-02-01
Enrollment: 154
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
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.

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.

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

Primary outcomes:
measure: Number of Patients Included in the Dataset
timeFrame: 6 months
measure: Implement ML Models to Improve the Current Alarm-based System Using the Dataset Created
timeFrame: 6 months
Secondary outcomes:
measure: Track All Clinical Interventions and Events to be Included in the Database
timeFrame: 6 months
measure: Assess Patient and Medical Professional Satisfaction With the Digital Platform
timeFrame: 6 months
measure: Mean SUS Score to Assess the Usability of the Digital Platform App
timeFrame: 6 months
Other outcomes:
Locations:
city: Galaţi
country: Romania
facility: Hospital of Galati
state: Galati
city: Bucharest
country: Romania
facility: Hospital Floreasca
city: Bucharest
country: Romania
facility: Colentina Hospital
city: Torrevieja
country: Spain
facility: Hospital Universitario de Torrevieja
state: Alicante
city: Figueres
country: Spain
facility: Hospital de Figueres
state: Girona
city: Talavera De La Reina
country: Spain
facility: Hospital General Universitario Nuestra Señora del Prado
state: Toledo
city: Girona
country: Spain
facility: Hospital Universitari de Girona Doctor Josep Trueta
Study officials:
affiliation: Hospital Universitario de Torrevieja
name: Julio César MD Blázquez
role: PRINCIPAL_INVESTIGATOR
References:
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.