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.
Study Type
OBSERVATIONAL
Enrollment
8
Apple Watch has been used to specifically monitor patients with liver cirrhosis, who worn it for the period specified by the study
Ente Ospedaliero Cantonale
Lugano, Switzerland
Feasibility and accuracy of machine learning analysis of individual health data collected by wearable device.
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.
Time frame: From enrollment to the end of the study (6 months)
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