NCT06218121

Update on the Detection of Frailty in Older Adults

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

• Status: Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 500 (estimated)
• Sponsor: Universidad Europea de Madrid · Academic / Other
• Sex: All
• Age: 62 Years
• Healthy volunteers: —

Summary

The main objective is to update the diagnostic assessment of frailty by correlating several variables with the ultrasound image of the frail elderly patient. Secondarily, the investigators intend to collect and analyze data on functional capacity and quality of life variables on the evolution of musculoskeletal symptoms, as well as on pain and psychological variables. Similarly, it is intended to make a record of different profiles and subtypes of frail older adult patients to be stored in Machine Learning in order to establish therapeutic intervention plans that allow both the evaluation and treatment of patients.

Detailed description

The present cohort study will be conducted in 500 older adults diagnosed with frailty. The correlation of the demographic variables, physical functionality tests and psychoemotional constructs that will be analyzed in this study with the ultrasound image obtained from the patients will improve the ultrasound diagnosis of frailty, providing new information that will facilitate the work of healthcare personnel in the diagnosis and management of frailty. Similarly, the use of Machine Learning will allow institutions to extract data on different patient profiles, signs and symptoms of frailty and the different risk factors that affect frailty patients, which will improve treatments and favor the development of educational programs tailored to the patient's needs.

Conditions

• Strength
• Quality of Life
• Functional Independence
• Functionality
• Pain
• Sarcopenia
• Anxiety
• Kinesiophobia
• Depression

Interventions

Type	Name	Description
DIAGNOSTIC_TEST	Instrumental and Functional Tests that Assess Functional Capacity	The correlation between all functional, ultrasound, nutritional, and psychological variables will be analyzed. Through GLIM diagnosis, anthropometric data (weight, height, BMI) as well as analytical data including inflammation information (CRP and albumin) will be used to reach a diagnosis that allows comparison/correlation with the rest of the variable parameters.

Timeline

• Start date: 2024-04-20
• Primary completion: 2025-04-15
• Completion: 2025-06-15
• First posted: 2024-01-23
• Last updated: 2024-07-09

Locations

1 site across 1 country: Spain