About this trial
Heart failure is a serious condition and a major reason why older adults are admitted to the hospital. Even after going home, many patients struggle to get the follow-up care they need, leading to high rates of return to the hospital.
While remote monitoring technology exists, it is often difficult to use, expensive, or hard for doctors to interpret.
Recent research suggests that changes in a person's voice and speech can be early warning signs of worsening heart failure, but we need to test if this can work reliably in a home setting.This study tests a new, user-friendly platform called AVATAR-SC.
This system uses a a computer-generated character to interact with patients. By analyzing short voice clips and simple health questionnaires provided by the patient, the system aims to make home monitoring easier and more effective.
The study will involve 60 participants divided into two groups:half of the patients who have recently been discharged from the hospital following a heart failure episode; the other 30 patients who visit the hospital monthly for a specific heart failure treatment (Levosimendan).
Participants will interact with the digital avatar twice a week. During these sessions, they will provide brief voice recordings and answer a few questions about how they feel. It is important to note that the AI in this study is not making medical decisions or changing the patient's current treatment; it is strictly being tested to see how well the technology functions.
As a feasibility study, the primary goal is to evaluate the practical implementation of the technology rather than clinical outcomes.
The research focuses on: Determining if patients can navigate the system easily and independently, Assessing how consistently participants engage with the scheduled sessions, Verifying the technical reliability of the platform in a home environment,identifying the strengths and challenges of the system to prepare for future, larger-scale trials.
Benefits: Participants may feel more supported and more aware of their symptoms through regular check-ins.
Risks: The risks are very low. They mostly involve getting used to new technology and ensuring that personal data is kept private and secure.
Impact If successful, AVATAR-SC could lead to a new way for doctors to keep an eye on heart failure patients at home, catching problems early through the sound of their voice and preventing unnecessary hospital visits.
Eligibility criteria
This trial does not accept healthy volunteersQualifiers
Age ≥ 18 years
Diagnosis of heart failure (NYHA Class II-IV), according to the Diagnosis Related Group (DRG) classification.
Recent discharge following acute heart failure (AHF) or ongoing treatment with Levosimendan in a Day Hospital setting.
Provision of signed informed consent in Italian.
Disqualifiers
Inability or incapacity to provide informed consent.
Cognitive impairment severe enough to prevent interaction with the avatar and compromise the reliability of the collected data.
Significant phonation disorders (e.g., results of previous otorhinolaryngological surgery or post-traumatic outcomes).
Inability to manage a compatible device or the device provided by the study for interaction with the AVATAR-SC platform.
Trial design
Parallel
Treatments tested in this trial
AVATAR-SC
Other interventionPatients discharged following hospitalization for acute heart failure will be enrolled in this study arm and will utilize AVATAR\_SC for periodic monitoring
Levosimendan
DrugPatients receiving monthly Levosimendan therapy at the ASST GOM Niguarda (Cardiology 2 - Heart Failure and Transplants) will be enrolled for longitudinal voice data collection. This approach captures data across all preclinical stages of the deterioration-compensation cycle, supported by periodic monitoring via the AVATAR-SC platform. Levosimendan will be prescribed as per clinical practice. .
Treatment groups
Trial outcomes
Primary outcomes
Feasability of the AVATAR-SC System in Heart Failure patients
≥80% of patients compleating 75% of scheduled interaction
Secondary outcomes
Change in Perceived Quality of Life (QoL)
Outcome Measures. A clinical improvement defined as at least a 10% increase in perceived quality of life scores, as measured by standardized Patient-Reported Outcome Measures (KCCQ, EQ-5D).
Change in Vocal Biomarkers Score
Exploratory offline analysis to evaluate the statistical consistency between vocal patterns and clinical parameters. Quantitative metrics including accuracy, precision, recall, and F1-score will be used for descriptive purposes only. This analysis is purely exploratory, without the definition of clinical thresholds, individual classification, or operational use of the results
New York Heart Association (NYHA) Functional Class of Heart Failure
Clinical assessment performed by the physician to evaluate the patient's overall heart failure severity and functional limitations based on cardiac symptoms. The evaluation is performed using the New York Heart Association (NYHA) classification, which categorizes patients into one of four classes based on their limitations during physical activit (class I to IV) . Higher classes indicate greater severity of heart failure and worse functional status.
Change in Montreal Cognitive Assessment (MoCA) Score
The MoCA is a screening instrument for cognitive impairment. It assesses different cognitive domains: attention and concentration, executive functions, memory, language, visuoconstructional skills, conceptual thinking, calculations, and orientation. The total score is the sum of all item scores. The scale ranges from 0 to 30, where a score higher than 26 .
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