Update on the Detection of Frailty in Older Adults: A Multicenter Cohort Machine Learning-Based Study

What this Trial Is About

Researchers are conducting a cohort study involving 500 older adults diagnosed with frailty to improve the diagnosis and management of this condition. The study aims to update the diagnostic assessment of frailty by linking various demographic, physical, and psychological variables with ultrasound images of frail patients. The use of Machine Learning will help identify different profiles and subtypes of frail older adults, supporting personalized therapeutic plans. Participants will undergo instrumental and functional tests evaluating their functional capacity alongside ultrasound imaging to assess sarcopenia and nutritional status. Additional assessments include psychological evaluations and anthropometric measurements such as weight, height, and BMI. Inflammation markers like CRP and albumin will also be analyzed to support diagnosis through GLIM criteria. Data will be collected throughout follow-up to enable Machine Learning analysis for understanding patient evolution and symptom patterns. During the study, participants will be assessed on various measures including manual grip strength, quality of life, daily living activities, balance, pain levels, strength, anxiety, fear of movement (kinesiophobia), depression, and risk of falls. The study involves comprehensive evaluations using questionnaires, physical tests, and ultrasound scans to monitor these outcomes. This detailed data collection will help create profiles that guide tailored treatment approaches for frail older adults, with the study planned to continue through June 2025.

Update on the Detection of Frailty in Older Adults