Smart Monitoring and Analysis System Based on Artificial Intelligence for Patients With Chronic Heart Failure Using Advanced Mini-Invasive and Wearable Medical Devices
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 入组人数
- 205
- 试验地点
- 1
- 主要终点
- Change in Hospital Admissions with AI-Based Remote Monitoring
研究概览
简要总结
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.
详细描述
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 19 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •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
排除标准
- •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)
研究组 & 干预措施
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.
干预措施: Intervention Group (Device Group - AI-Based Remote Monitoring) (Device)
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.
干预措施: Standard Clinical Follow-Up (Other)
结局指标
主要结局
Change in Hospital Admissions with AI-Based Remote Monitoring
时间窗: 6 months from participant enrollment.
The study aims to determine whether AI-based remote monitoring using a wearable device leads to a 20% reduction in hospital admissions (including emergency department visits and hospitalizations) compared to standard clinical follow-up in patients with chronic heart failure (CHF). The intervention group will use a mini-invasive wearable device for continuous physiological monitoring, while the control group will receive standard CHF management without remote monitoring. Hospital admission rates will be analyzed to assess the effectiveness of early AI-driven detection and intervention.
次要结局
- Change in Quality of Life(Baseline, 3 months, and 6 months)
- Adverse Effects of CHF Therapy(6 months)
- Change in Biochemical Parameters(3 and 6 months from participant enrollment)
- Change in Functional ECG-Derived Parameters(3 and 6 months from participant enrollment)
- Change in Functional Echocardiographic derived Parameters(3 and 6 months from participant enrollment)
研究者
Alessia Bramanti
Associate Professor
University of Salerno