AI-Based Monitoring System for Chronic Heart Failure With Advanced Wearable and Mini-Invasive Devices

NCT ID: NCT06909682

Last Updated: 2025-04-03

Basic Information

• Recruitment Status: NOT_YET_RECRUITING
• Total Enrollment: 205 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2025-08-01
• Study Completion Date: 2027-02-02

Brief Summary

The goal of this observational, multicenter study is to evaluate whether AI-driven remote monitoring using a mini-invasive wearable device can improve clinical outcomes in adult patients (≥18 years) with chronic heart failure (CHF).

The main questions it aims to answer are:

• Can continuous remote monitoring reduce hospital admissions (emergency visits and hospitalizations) by 20% compared to standard care?
• Does wearable-based remote monitoring improve functional, biochemical, and instrumental parameters in CHF patients? Researchers will compare patients using the wearable device (intervention group) to those receiving standard clinical follow-up (control group) to assess whether AI-driven monitoring leads to fewer hospitalizations, better disease management, and improved quality of life.

Participants will:

• Wear the EmbracePlus (Empatica Inc.) device continuously for six months (intervention group only).
• Have their biometric data (SpO₂, HRV, EDA, respiratory rate, temperature, sleep quality) monitored remotely.
• Receive automated alerts and teleconsultations if abnormal physiological changes are detected.
• Attend scheduled follow-up visits (remote and in-person) for clinical evaluation and treatment adjustments.

The study aims to provide real-world evidence on whether integrating wearable health technology with AI analytics can enhance CHF management and improve patient outcomes.

Detailed Description

Chronic Heart Failure (CHF) is a multifactorial syndrome characterized by high rates of hospitalization, morbidity, and mortality. Despite advances in pharmacological and device-based therapies, early identification of clinical deterioration remains a major challenge. Traditional follow-up models, based primarily on intermittent in-person evaluations, are often inadequate in capturing subclinical changes that precede acute decompensation.

The SMART-CARE (System of Monitoring and Analysis based on Artificial Intelligence for Chronic Heart Failure Patients with Mini-Invasive and Wearable Medical Devices) study aims to assess whether continuous remote monitoring using a CE (Conformité Européenne)-certified wearable device (EmbracePlus by Empatica Inc.) integrated with AI (Artificial Intelligence) analytics can improve the management of CHF patients. The study adopts a prospective, multicenter, observational design with two parallel cohorts: patients managed with standard care versus patients equipped with the wearable device for six months.

The wearable device captures a range of physiological signals-including peripheral capillary oxygen saturation (SpO₂), heart rate variability (HRV), electrodermal activity (EDA), skin conductance level (SCL), respiratory rate, peripheral skin temperature, pulse rate, fatigue detection, and sleep metrics via actigraphy-and transmits them in real time to a centralized digital platform. AI algorithms analyze these data continuously, triggering alerts in the event of abnormal trends. When alerts are generated, patients undergo teleconsultation, with possible treatment adjustments or in-person follow-up as clinically indicated.

The study is designed to generate real-world evidence on whether AI-enhanced monitoring can reduce unplanned hospital admissions by at least 20% over a six-month follow-up, compared to standard care. Secondary endpoints include improvements in cardiac function (evaluated through echocardiographic parameters), neurohormonal biomarkers such as B-type Natriuretic Peptide (BNP) and Atrial Natriuretic Peptide (ANP), exercise tolerance assessed by the Six-Minute Walk Test (6MWT), quality of life measured by the Kansas City Cardiomyopathy Questionnaire (KCCQ), and incidence of therapy-related adverse events (e.g., hypotension, bradyarrhythmias).

In addition to evaluating clinical efficacy, the study supports the development of a predictive multimarker model. Data collected through the SMART-CARE platform-including clinical history, biochemical markers, imaging data, and continuous sensor-derived variables-will be used by collaborating academic centers to train AI algorithms capable of forecasting CHF progression and tailoring individualized interventions.

All data are pseudonymized in compliance with the General Data Protection Regulation (GDPR, Regulation EU 2016/679). The study does not interfere with ongoing medical treatments and adheres to Good Clinical Practice (GCP) and the ethical principles of the Declaration of Helsinki. Patients provide written informed consent prior to enrollment.

The SMART-CARE initiative reflects a broader goal: integrating telemedicine, wearable health technology, and AI-based predictive modeling into a seamless care pathway that promotes proactive CHF management and enables personalized, data-driven therapeutic decisions.

Conditions

Chronic Heart Failure; Cardiovascular Diseases; Heart Failure With Reduced Ejection Fraction (HFrEF); Heart Failure With Preserved Ejection Fraction (HFPEF); Congestive Heart Failure Chronic

Study Design

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

Study Groups

Intervention Group (Device Group - AI-Based Remote Monitoring)

Participants in this group will wear the EmbracePlus mini-invasive device for continuous remote monitoring over a six-month period. The device tracks key physiological parameters, including oxygen saturation (SpO₂), heart rate variability (HRV), electrodermal activity (EDA), temperature, respiratory rate, and sleep quality. Data is transmitted to a centralized AI-driven platform, which analyzes trends and detects early signs of heart failure worsening. If significant abnormalities are identified, the system triggers automated alerts, prompting teleconsultations or in-person evaluations as needed to ensure timely clinical intervention.

Intervention Group (Device Group - AI-Based Remote Monitoring)

Intervention Type: DEVICE

This intervention utilizes a mini-invasive wearable device for continuous remote monitoring of chronic heart failure (CHF) patients. Unlike traditional telemonitoring, it integrates AI-driven predictive analytics to track oxygen saturation (SpO₂), heart rate variability (HRV), electrodermal activity (EDA), temperature, respiratory rate, and sleep quality in real time. The system generates automated alerts for healthcare providers, enabling early detection of CHF exacerbation and proactive intervention through teleconsultations, medication adjustments, or in-person evaluations. Data is securely transmitted to a cloud-based platform, allowing continuous risk assessment and personalized care adjustments. This approach aims to reduce unnecessary hospitalizations, enhance patient monitoring, and optimize heart failure management through advanced AI-based digital health technology.

Control Group (Non-Device Group - Standard Clinical Follow-Up)

Participants in this group will receive standard chronic heart failure (CHF) management according to current clinical guidelines. Their follow-up will consist of scheduled in-person visits every three months, during which they will undergo routine laboratory tests (including BNP, NT-proBNP, renal function, and electrolytes), as well as echocardiography and ECG evaluations. Treatment adjustments will be made based on clinical assessments and reported symptoms.

Standard Clinical Follow-Up

Intervention Type: OTHER

Participants in this group will receive standard chronic heart failure (CHF) management according to current clinical guidelines. Their follow-up will consist of scheduled in-person visits every three months, during which they will undergo routine laboratory tests (including BNP, NT-proBNP, renal function, and electrolytes), as well as echocardiography and ECG evaluations. Treatment adjustments will be made based on clinical assessments and reported symptoms. Unlike the intervention group, these participants will not use a wearable device, and their condition will be monitored exclusively through traditional hospital visits and self-reported health status.

Interventions

Intervention Group (Device Group - AI-Based Remote Monitoring)

This intervention utilizes a mini-invasive wearable device for continuous remote monitoring of chronic heart failure (CHF) patients. Unlike traditional telemonitoring, it integrates AI-driven predictive analytics to track oxygen saturation (SpO₂), heart rate variability (HRV), electrodermal activity (EDA), temperature, respiratory rate, and sleep quality in real time. The system generates automated alerts for healthcare providers, enabling early detection of CHF exacerbation and proactive intervention through teleconsultations, medication adjustments, or in-person evaluations. Data is securely transmitted to a cloud-based platform, allowing continuous risk assessment and personalized care adjustments. This approach aims to reduce unnecessary hospitalizations, enhance patient monitoring, and optimize heart failure management through advanced AI-based digital health technology.

Intervention Type: DEVICE

Standard Clinical Follow-Up

Participants in this group will receive standard chronic heart failure (CHF) management according to current clinical guidelines. Their follow-up will consist of scheduled in-person visits every three months, during which they will undergo routine laboratory tests (including BNP, NT-proBNP, renal function, and electrolytes), as well as echocardiography and ECG evaluations. Treatment adjustments will be made based on clinical assessments and reported symptoms. Unlike the intervention group, these participants will not use a wearable device, and their condition will be monitored exclusively through traditional hospital visits and self-reported health status.

Intervention Type: OTHER

Eligibility Criteria

Inclusion Criteria

• Age ≥ 18 years (adults of any sex)
• Confirmed diagnosis of chronic heart failure (CHF) for at least 6 months prior to screening
• Stable on optimized heart failure therapy for at least one month before enrollment
• Any left ventricular ejection fraction (LVEF) classification, including:

• Heart Failure with Reduced Ejection Fraction (HFrEF)
• Heart Failure with Mid-Range Ejection Fraction (HFmrEF)
• Heart Failure with Preserved Ejection Fraction (HFpEF)
• NYHA Functional Class I, II, or III
• History of at least one hospital admission or outpatient visit in the past 12 months requiring intravenous (IV) diuretics, vasodilators, or inotropes for CHF exacerbation

Exclusion Criteria

• NYHA Functional Class IV or anticipated heart transplant or ventricular assist device (VAD) implantation within 6 months of screening
• Severe renal impairment (eGFR < 30 mL/min/1.73 m²) or dialysis dependence
• Terminal comorbidities (e.g., advanced cancer, end-stage pulmonary disease) significantly limiting life expectancy
• Pregnancy
• Presence of skin conditions or allergies preventing prolonged use of a wearable device
• Inability to comply with study procedures (e.g., cognitive impairment, significant psychiatric disorders)

• Minimum Eligible Age: 19 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

University of Salerno

OTHER

Sponsor Role: lead

Responsible Party

Alessia Bramanti

Associate Professor

Responsibility Role: PRINCIPAL_INVESTIGATOR

Central Contacts

Alessia Bramanti, Electronic Engineering

Role: CONTACT

abramanti@unisa.it

+393483809181

References

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Other Identifiers

D43C22002120006

Identifier Type: -

Identifier Source: org_study_id