Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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RECRUITING
500 participants
OBSERVATIONAL
2024-04-20
2025-06-15
Brief Summary
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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.
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Detailed Description
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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
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Study Design
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COHORT
PROSPECTIVE
Study Groups
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Frail Older Adult Patients
For the cohort study, 500 frail older adult patients will be evaluated by means of instrumental and functional tests that assess their functional capacity, in addition to ultrasound imaging to study sarcopenia and nutrition, as well as psychological variables. 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. All available information will be collected during the follow-up in order to generate Machine Learning on the objective evolution and symptomatology of these patients, generating profiles that facilitate the most accurate and appropriate treatment for each patient.
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.
Interventions
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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.
Other Intervention Names
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Eligibility Criteria
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Inclusion Criteria
Exclusion Criteria
* 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.
62 Years
ALL
No
Sponsors
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Universidad Europea de Madrid
OTHER
Responsible Party
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Eleuterio Atanasio Sánchez Romero
Principal Investigator
Principal Investigators
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Eleuterio A. A. Sánchez Romero, PhD
Role: PRINCIPAL_INVESTIGATOR
European University of Madrid
Locations
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Hospital Puerta de Hierro de Majadahonda
Madrid, Outside of the US, Spain
Countries
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Central Contacts
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Facility Contacts
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References
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Fernandez-Carnero S, Martinez-Pozas O, Pecos-Martin D, Pardo-Gomez A, Cuenca-Zaldivar JN, Sanchez-Romero EA. Update on the detection of frailty in older adults: a multicenter cohort machine learning-based study protocol. Aging (Albany NY). 2025 May 21;17(5):1328-1339. doi: 10.18632/aging.206254. Epub 2025 May 21.
Other Identifiers
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Detection of Frailty
Identifier Type: -
Identifier Source: org_study_id
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