Advance Chronic Liver Disease (ACLD)
Conditions
Brief summary
The research project studies the possibility of using an artificial intelligence-based system in patients with advanced chronic liver disease (liver cirrhosis) to record variations in a patient's health status, with the aim of early identification of clinical improvement or deterioration. The system is based on the collection and processing of various clinical parameters through an Apple Watch. The study aims to evaluate whether the data generated by this system correlate with patients' clinical evolution and whether its use may ultimately contribute to improved care management and quality of life.
Interventions
DEVICEApple Watch
Apple Watch has been used to specifically monitor patients with liver cirrhosis, who worn it for the period specified by the study
Sponsors
Antonio Galante
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 75 Years
Healthy volunteers
No
Inclusion criteria
* Adults aged 18-75 years * Diagnosis of advanced chronic liver disease (ACLD). * Either: * hospitalized for hepatic decompensation or acute-on-chronic liver failure (ACLF), including ascites, hepatorenal syndrome, hepatic encephalopathy, bacterial infection, gastrointestinal bleeding, or jaundice; or * outpatient with Child-Pugh B cirrhosis and no evidence of hepatic decompensation or ACLF at enrolment. * Willing and able to provide written informed consent.
Exclusion criteria
* Inability or refusal to provide written informed consent * Inability to wear or correctly use the Apple Watch * Patients with hepatocellular carcinoma beyond the Milan Criteria (one lesion up to 5 cm or 3 lesions up to 3 cm in diameter) * Presence of hepatic decompensation or ACLF
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Feasibility and accuracy of machine learning analysis of individual health data collected by wearable device. | From enrollment to the end of the study (6 months) | The primary outcome is to assess the feasibility and accuracy of machine learning analysis of individual health data collected by wearable device and to describe their patterns during hospitalization due to symptoms of decompensation or ACLF and in outpatients until hospitalization due to decompensation or ACLF in patients with liver cirrhosis. Health data continuously collected through a dedicated wearable device application comprise: heart rate and heart rate variability (HR; HRV), oxygen saturation (SpO₂), ECG (QRS, PQ, PT Tpe interval), sleep quality and duration, daily step count, tremor intensity (Hz), typing speed (taps/time). The single unit of measure used to assess the feasibility of the wearable device is the usable data acquisition rate (%), defined as the percentage of monitoring data successfully collected and suitable for analysis. |
Countries
Switzerland
Outcome results
None listed