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

Official title

Smart Monitoring and Analysis System Based on Artificial Intelligence for Patients With Chronic Heart Failure Using Advanced Mini-Invasive and Wearable Medical Devices

Overview

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.

Interventions

• Device 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 interv
• Other 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

Primary outcome measures

• Change in Hospital Admissions with AI-Based Remote Monitoring [Time frame: 6 months from participant enrollment.]

Secondary outcome measures (12)

• Change in Quality of Life [Time frame: Baseline, 3 months, and 6 months]
• Adverse Effects of CHF Therapy [Time frame: 6 months]
• Change in Biochemical Parameters [Time frame: 3 and 6 months from participant enrollment]
• Change in Biochemical Parameters [Time frame: 3 and 6 months from participant enrollment]
• Change in Biochemical Parameters [Time frame: 3 and 6 months from participant enrollment]
• Change in Biochemical Parameters [Time frame: 3 and 6 months from participant enrollment]
• Change in Biochemical Parameters [Time frame: 3 and 6 months from participant enrollment]
• Change in Biochemical Parameters [Time frame: 3 and 6 months from participant enrollment]
• Change in Biochemical Parameters [Time frame: 3 and 6 months from participant enrollment]
• Change in Functional ECG-Derived Parameters [Time frame: 3 and 6 months from participant enrollment]
• Change in Functional ECG-Derived Parameters [Time frame: 3 and 6 months from participant enrollment]
• Change in Functional Echocardiographic derived Parameters [Time frame: 3 and 6 months from participant enrollment]

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
• Ability to provide written informed consent or availability of a legally authorized representative 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)

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: No

Study design

Observational model

Cohort

Study locations

Italy · 1 center

• Hospital University San Giovanni di Dio and Ruggi d'Aragona — Salerno

Publications

• Tang WH, Francis GS, Morrow DA, Newby LK, Cannon CP, Jesse RL, Storrow AB, Christenson RH, Apple FS, Ravkilde J, Wu AH; National Academy of Clinical Biochemistry Laboratory Medicine. National Academy of Clinical Biochemistry Laboratory Medicine practice guidelines: Clinical utilization of cardiac biomarker testing in heart failure. Circulation. 2007 Jul 31;116(5):e99-109. doi: 10.1161/CIRCULATIONA PMID 17630410
• Cleland JG, Daubert JC, Erdmann E, Freemantle N, Gras D, Kappenberger L, Tavazzi L; Cardiac Resynchronization-Heart Failure (CARE-HF) Study Investigators. The effect of cardiac resynchronization on morbidity and mortality in heart failure. N Engl J Med. 2005 Apr 14;352(15):1539-49. doi: 10.1056/NEJMoa050496. Epub 2005 Mar 7. PMID 15753115
• Bousquet J, Anto JM, Sterk PJ, Adcock IM, Chung KF, Roca J, Agusti A, Brightling C, Cambon-Thomsen A, Cesario A, Abdelhak S, Antonarakis SE, Avignon A, Ballabio A, Baraldi E, Baranov A, Bieber T, Bockaert J, Brahmachari S, Brambilla C, Bringer J, Dauzat M, Ernberg I, Fabbri L, Froguel P, Galas D, Gojobori T, Hunter P, Jorgensen C, Kauffmann F, Kourilsky P, Kowalski ML, Lancet D, Pen CL, Mallet J, PMID 21745417
• Keijser W, de Manuel-Keenoy E, d'Angelantonio M, Stafylas P, Hobson P, Apuzzo G, Hurtado M, Oates J, Bousquet J, Senn A. DG Connect Funded Projects on Information and Communication Technologies (ICT) for Old Age People: Beyond Silos, CareWell and SmartCare. J Nutr Health Aging. 2016;20(10):1024-1033. doi: 10.1007/s12603-016-0804-0. PMID 27925142
• Ciccarelli M, Bramanti A, Carrizzo A, Garofano M, Visco V, Izzo C, Rusciano MR, Galasso G, Loria F, Bruno G, Vecchione C. Artificial intelligence-based remote monitoring for chronic heart failure: design and rationale of the SMART-CARE study. Front Digit Health. 2025 Dec 10;7:1719562. doi: 10.3389/fdgth.2025.1719562. eCollection 2025. PMID 41451381

Identifiers

NCT: NCT06909682 · D43C22002120006

Primary sources (government registries)

• ClinicalTrials.gov (US NIH)

View this study on ClinicalTrials.gov ↗