Clinical trial NCT06218121: Update on the Detection of Frailty in Older Adults
Official title: Update on the Detection of Frailty in Older Adults: A Multicenter Cohort Machine Learning-Based Study
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Strength
Quality of Life
Functional Independence
Functionality
Pain
Sarcopenia
Anxiety
Kinesiophobia
Depression
Keywords: Frailty
Quality of life
Sarcopenia
Ultrasound
Lead sponsor: Universidad Europea de Madrid
Sponsor class: OTHER
First posted: 2024-01-23
Enrollment: 500
Sex: ALL
Minimum age (years): 62
Standard ages: ADULT
OLDER_ADULT
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: The present cohort study will be conducted in older adults diagnosed with frailty. A multicenter study will be conducted among the Residencia de Nuestra Señora de la Soledad y del Carmen (Colmenar Viejo, Madrid), Residencia San Camilo (Tres Cantos, Madrid), Centro de Día San Camilo (Tres Cantos, Madrid), Residencia San Luis de Gonzaga (Carretera de Colmenar Viejo), Hospital Universitario Rey Juan Carlos (Móstoles, Madrid) and Hospital Puerta de Hierro (Majadahonda, Madrid) from their diagnosis

Brief 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.

Eligibility criteria: Inclusion Criteria:

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 Criteria:

* 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.

Primary outcomes:
measure: Manual Grip Strength
timeFrame: Baseline
measure: Quality of Life (EQ-5D-5L)
timeFrame: Baseline
measure: Basic Activities in Daily Life
timeFrame: Baseline
measure: Balance
timeFrame: Baseline
measure: Visual Analog Scale (VAS)
timeFrame: Baseline
measure: Strength
timeFrame: Baseline
measure: Anxiety
timeFrame: Baseline
measure: Kinesiophobia
timeFrame: Baseline
measure: Depression
timeFrame: Baseline
measure: Risk of Falls
timeFrame: Baseline
Secondary outcomes:
Other outcomes:
Locations:
city: Madrid
country: Spain
facility: Hospital Puerta de Hierro de Majadahonda
state: Outside Of The US
Study officials:
affiliation: European University of Madrid
name: Eleuterio A. A. Sánchez Romero, PhD
role: PRINCIPAL_INVESTIGATOR
References: