RecruitingNCT06218121

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

Sponsor

Universidad Europea de Madrid

Enrollment

500 participants

Start Date

Apr 20, 2024

Study Type

OBSERVATIONAL

Conditions

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

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.

Eligibility

Min Age: 62 Years

Inclusion Criteria1

A diagnosis of signs and symptoms of frailty by a geriatric physician in the research group will be used as the primary inclusion criterion. Frailty will be assessed and diagnosed using the frailty phenotype and the Clinical Frailty Scale.

Exclusion Criteria9

Acute myocardial infarction in the last 3 months and/or unstable angina pectoris
Uncontrolled arrhythmia, recent thromboembolism and terminal illness.
Patients undergoing MMII unloading or MMSS/MMII fractures in the last three months.
Patients with a functional gait index of 1 (Inability to walk)
Severe pain (7/10 VAS)
Previous neuromuscular pathology presenting with weakness
Medication that does not allow the patient's actual muscle reaction to be assessed
Severe cognitive impairment that would prevent collaboration and understanding of the tests to be performed.
Cardiovascularly unstable patients and uncontrolled arterial hypertension.

Interventions

DIAGNOSTIC_TESTInstrumental 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.