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

Study Overview
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Brief Summary
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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
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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.

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

Conditions
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Heart Failure

Intervention / Treatment
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* Other: Telemonitoring


Participation Criteria
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Eligibility Criteria
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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

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Heart Failure patients telemonitored<br>Patients will be monitored with the HumanITcare app and platform | Other: Telemonitoring<br>* 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<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Create a labeled and diverse dataset | 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 | 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 | 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 | 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 | 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 | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Heart Failure, Telemonitoring, Remote monitoring, Remote care, Healthcare, Home care, Artificial Intelligence, Chronic patients