Comparison of Computed Tomography Data With Routine Measurements Concerning Bone and Muscle Health of Aged Individuals

NCT06488872 | Recruiting

• 1 other identifier: 2024-0705
• Study Type: observational
• Target: 300
• Locations: 1 country
• Sites: 1

Brief Summary

This study focuses on researching sarcopenia and bone loss (osteoporosis), aiming to develop early and effective methods for diagnosis and treatment. These health issues significantly contribute to falls, fractures, and loss of independence and quality of life in old age, particularly affecting individuals impairments. To address these challenges, the study employs innovative imaging techniques based on artificial intelligence (AI) to accurately assess age-related muscle atrophy. A central approach is to analyze existing computed tomography (CT) images of older adults, using retrospective data to evaluate muscle quality. This method aims to efficiently assess muscle quality without additional resources. AI algorithms analyze fine details of muscle tissue, such as muscle adiposity and density. The algorithm can detect fat content within muscles, which negatively impacts muscle health and functionality, and identify irregularities or abnormalities in muscle fibers. This non-invasive approach is crucial for early detection of muscle atrophy and monitoring treatment success. Integrating AI technologies advances beyond conventional imaging techniques, allowing precise analysis of muscle quality. This method not only offers efficient diagnosis and monitoring of sarcopenia but also opens new avenues for personalized therapeutic approaches and improved patient care. Almost every elderly person has at least one existing CT scan, a common and excellent method of medical imaging for significant health issues. These images can be retrospectively analyzed for muscle health. In addition to imaging techniques, the study includes functional tests such as hand strength and walking speed measurements to assess muscle health and condition. These tests establish objective quality characteristics of muscles and assess the effectiveness of prevention and treatment measures. This research aims to provide early diagnosis and effective treatment strategies for sarcopenia and osteoporosis, ultimately improving the quality of life for the elderly. By leveraging AI and existing medical imaging data, the study promotes efficient, sustainable, and precise healthcare solutions for age-related muscle and bone deterioration.

Conditions

Sarcopenia; Muscle Loss; Osteoporosis

Outcome Measures

Primary Outcomes (3)

• Muscle Volume in mm3 on CT

  Exploratory analysis of quantitative variables that can be determined by CT to evaluate the primary endpoint Sarcopenia: By evaluating muscle volume (in mm3) on CT.

  through study completion, an average o 1 year

• fat percentage in muscle density (in HU) on CT.

  Exploratory analysis of quantitative variables that can be determined by CT to evaluate the primary endpoint Sarcopenia: By evaluating fat percentage in muscle density (in HU) on CT.

  through study completion, an average o 1 year

• bone density/attenuation in HU on CT.

  Analysis of bone density/attenuation in HU on CT to evaluate the primary endpoint Osteoporosis:

  through study completion, an average o 1 year

Interventions

comparison of CT scan with routinely assessed methods of muscle mass and strength PROCEDURE

comparison of CT scan with routinely assessed methods of muscle mass and strength

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

We would like to retrospectively examine a total of 300 data sets of geriatric patients from 01.01.2017 to 31.12.2022, who were treated as inpatients at the UAFP. Individuals will be identified using various databases containing information on patients who have undergone CT scans imaging the thoracic and/or abdominal organs and DEXA scans. Participants must have had a CT scan performed by the in-house radiology department one month before or after the inpatient stay. In order to be included in the osteoporosis arm, a thorax and/or abdomen CT scan and a DEXA scan from an in-house DEXA measurement must be available. Both examinations must not be more than 18 months apart.

You may qualify if:

• CT examination (thorax, abdomen, pelvis, spine with muscle parts to be visualized) by the responsible in-house radiology department one month before or after the inpatient stay at the UAFP (sarcopenia arm).
• CT thorax and CT abdomen images of patients from the responsible in-house radiology department and an in-house DEXA measurement. Both examinations may be performed no more than 18 months apart (osteoporosis arm).
• Diagnostic image quality of CT scans.

You may not qualify if:

• Presence of a documented refusal.
• Non-diagnostic image quality
• Absence of the following functional measurements: Hand strength on both hands, timed-up \& go test, gait speed.

Sponsors & Collaborators

• University Department of Geriatric Medicine FELIX PLATTER: lead

Study Sites (1)

Universitäre Altersmedizin Felix Platter

Basel, Canton of Basel-City, 4055, Switzerland

RECRUITING

Related Publications (1)

• Cruz-Jentoft AJ, Bahat G, Bauer J, Boirie Y, Bruyere O, Cederholm T, Cooper C, Landi F, Rolland Y, Sayer AA, Schneider SM, Sieber CC, Topinkova E, Vandewoude M, Visser M, Zamboni M; Writing Group for the European Working Group on Sarcopenia in Older People 2 (EWGSOP2), and the Extended Group for EWGSOP2. Sarcopenia: revised European consensus on definition and diagnosis. Age Ageing. 2019 Jan 1;48(1):16-31. doi: 10.1093/ageing/afy169.

  PMID: 30312372 BACKGROUND

MeSH Terms

Conditions

Sarcopenia; Muscular Atrophy; Osteoporosis

Condition Hierarchy (Ancestors)

Neuromuscular Manifestations; Neurologic Manifestations; Nervous System Diseases; Atrophy; Pathological Conditions, Anatomical; Pathological Conditions, Signs and Symptoms; Signs and Symptoms; Bone Diseases, Metabolic; Bone Diseases; Musculoskeletal Diseases; Metabolic Diseases; Nutritional and Metabolic Diseases

Study Officials

• Andreas M. Fischer, PD Dr.

  Universitäre Altersmedizin Felix Platter

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Andreas M. Fischer, PD Dr.

CONTACT

061 3265102; andreasmfischer.basel@gmail.com

Natalie N Godau, Dr.

CONTACT

061 3265102; andreasmfischer.basel@gmail.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: PD Dr.med. Andreas M. Fischer, Principal Investigator, Head of NutriCare Clinic

Study Record Dates

• First Submitted: June 18, 2024
• First Posted: July 5, 2024
• Study Start: May 1, 2024
• Primary Completion: September 1, 2025
• Study Completion: September 1, 2025
• Last Updated: July 11, 2024

Record last verified: 2024-07

Locations

CH (1)