ai-based monitoring system for chronic heart failure with advanced wearable and mini-invasive devices

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

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.

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.

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)

Keywords: wearable medical devices remote patient monitoring artificial intelligence in cardiology smart wearables for healthcare observational study in heart failure ai-based predictive modeling telemedicine in heart failure non-invasive health monitoring personalized medicine for chf quality of life improvement in heart failure heart failure readmission prevention telehealth in chronic disease management digital health solutions for heart disease