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

Completed

Conditions: Heart Failure

Interventions: Other: Telemonitoring

Registration Number: NCT05708846

Lead Sponsor: humanITcare

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.

Recruitment & Eligibility

Status: COMPLETED

Sex: All

Target Recruitment: 154

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 & Design

Study Type: OBSERVATIONAL

Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
Heart Failure patients telemonitored	Telemonitoring	Patients will be monitored with the HumanITcare app and platform

Primary Outcome Measures

Name	Time	Method
Number of Patients Included in the Dataset	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	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.

Secondary Outcome Measures

Name	Time	Method
Assess Patient and Medical Professional Satisfaction With the Digital Platform	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).
Track All Clinical Interventions and Events to be Included in the Database	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.
Mean SUS Score to Assess the Usability of the Digital Platform App	6 months	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.

Trial Locations

Locations (7): Hospital of Galati
🇷🇴
Galaţi, Galati, Romania
Hospital Floreasca
🇷🇴
Bucharest, Romania
Colentina Hospital
🇷🇴
Bucharest, Romania
Hospital Universitario de Torrevieja
🇪🇸
Torrevieja, Alicante, Spain
Hospital General Universitario Nuestra Señora del Prado
🇪🇸
Talavera De La Reina, Toledo, Spain
Hospital de Figueres
🇪🇸
Figueres, Girona, Spain
Hospital Universitari de Girona Doctor Josep Trueta
🇪🇸
Girona, Spain