Ignite Creation Date:
2024-07-17 @ 10:47 AM
Last Modification Date:
2024-10-26 @ 3:34 PM
Study NCT ID:
NCT06488872
Status:
RECRUITING
Last Update Posted:
2024-07-11
First Post:
2024-06-18
Brief Title:
Comparison of Computed Tomography Data With Routine Measurements Concerning Bone and Muscle Health of Aged Individuals
Sponsor:
University Department of Geriatric Medicine FELIX PLATTER
Organization:
University Department of Geriatric Medicine FELIX PLATTER
Study Overview
Official Title:
Comparison of Artificial Intelligence-based Analysis of Computed Tomography Data With Routinely Performed Measurements Concerning Bone and Muscle Health of Aged Individuals to Validate Surrogate Parameters for the Aging Population
Status:
RECRUITING
Status Verified Date:
2024-07
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
SARCATAR
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
Detailed Description:
Age-related muscle wasting and bone loss are significant public health challenges impacting elderly mobility and independence Sarcopenia a decline in muscle strength and mass heightens fall risk particularly in individuals with dementia or cognitive impairments This leads to complications reduced independence and diminished quality of life Osteoporosis increases the risk of fractures from falls or spontaneously necessitating early diagnosis and prevention
Despite effective treatments for sarcopenia such as a protein-rich diet and strength training the condition remains underrecognized due to diagnostic challenges Common methods like hand strength measurement are problematic for those with rheumatic conditions or Parkinsons often yielding inaccurate results Other methods like measuring leg strength and walking speed require coordination and balance which can be difficult for those with dementia or visual impairments There is a need for tailored diagnostic solutions for diverse aging populations
Bone mineral density BMD is crucial for assessing health in older adults as low BMD indicates osteoporosis and a higher fracture risk Traditionally measured by dual X-ray absorptiometry DEXA BMD assessment can be difficult for patients with mobility issues pressure ulcers or dementia due to the requirement to remain still for extended periods
New AI-based algorithms can now automatically evaluate body tissues and patterns from routine CT scans offering reproducible results beyond human capability AI can quantify muscle mass at specific body cross-sections such as lumbar vertebral point 3 L3 which correlates with total body muscle mass and predicts muscle health CT measurements of thigh and psoas muscles can also indicate whole body skeletal muscle mass European guidelines highlight the need for muscle quantification in early sarcopenia diagnosis
Correlating AI-measured muscle mass with functional muscle strength assessments can help identify surrogate parameters for early sarcopenia detection Additionally measuring muscle fat content which correlates with strength loss is essential for assessing muscle health Innovative approaches are required as sarcopenia diagnosis is still evolving and geriatric diseases often need proportionate diagnostic and treatment strategies due to multiple comorbidities
For osteoporosis diagnostics AI can determine Hounsfield Unit HU values from CT images to represent bone density which can be correlated with DEXA results These surrogate diagnostics should follow current guidelines with results obtained within an appropriate interval of up to 18 months
Based on the primary endpoints the prevalence of muscle-healthy and probably sarcopenic individuals will be recorded Additionally the prevalence of individuals without osteoporosis with osteopenia and with confirmed osteoporosis will be calculated and included in the analysis
The secondary endpoints compare functional muscle strength measurements with retrospective quantitative CT results This exploratory analysis will test whether the AI algorithm can correlate functionality with muscle volume Additionally AI-measured muscle fatness myosteatosis could serve as a new quantifiable quality criterion for muscle health This is important because many elderly individuals cannot meet functional strength test requirements due to conditions like visual impairment dementia chronic pain joint diseases and frailty
For osteoporosis diagnostics the investigators aim to correlate bone density from DEXA measurements with CT-derived bone density of thoracic and abdominal vertebral bodies This exploratory surrogate measurement method could provide insights into bone mineral density and health from existing CT images CT-based diagnosis could be an alternative for those unable to remain still for DEXA scans
Design
This retrospective study will be conducted at a single center Participants will be identified using databases of patients who have undergone CT and DEXA scans To be included in the sarcopenia arm participants must have had a CT scan from the radiology department at the University Hospital Basel within one month of an inpatient stay For the osteoporosis arm a thorax andor abdomen CT scan and a DEXA scan must be available conducted within 18 months of each other
Anonymized DICOM datasets from the CT scans will be analyzed by an AI algorithm Data analysis will involve standard statistical comparison using Students t-test The algorithms values will be tested against reference standards and its diagnostic accuracy will be evaluated for various diseases The algorithm has been tested on 104 anatomical structures organs and organ groups Req-2022-00495
Origin of the data
At the largest geriatric medical center in Switzerland the investigators would like to include geriatric patients aged 65 and over who were undergoing inpatient treatment in the period from 01072017 to 31122022 inclusive
By testing alternative diagnostic procedures exploratory approach the investigators want to reach more people especially those who are unable to follow current diagnostic procedures due to dementia visual impairment or post-operative condition This is crucial as these patients in particular have a significantly increased risk of falling The research approach aims to address real-world challenges in order to consider prevention programs and personalized therapies for as many people as possible in the future
By using existing data and dispensing with further investigations the economic aspect in the context of rising healthcare costs is also taken into account
Scientific methodology and targets
Exploratory analysis of quantitative variables that can be determined by CT to evaluate the primary endpoints
Sarcopenia By evaluating muscle volume and fat percentage in muscle in mm3 and density in HU on CT
Osteoporosis By evaluating bone densityattenuation in HU on CT For the evaluation of muscle volumes and densities a feasibility study has already been carried out on a representative sample of over 4000 patients which suggests a normal distribution of muscle density and volume 21 Statistical deviations from this reference group are determined using a t-test or Wilcoxon test in the case of a lack of normal distribution The test for normal distribution is performed using the Kolmogorov-Smirnov test The significance level is set at α 005
The evaluation of osteoporosis is performed analogously The relationship between attenuation in HU and BMD has already been documented in the literature 22 18
Inclusion of as many patients as possible is essential for representative results
For which health-related personal data should consent be granted A general consent form Declaration of consent for the further use of health-related data and samples was introduced at the beginning of 2020 Unfortunately in practice it proved to be unreasonable to implement
The investigators consider the sample size to be n300 No consent has been obtained for any of the participants meaning that an application for exemption is being made for all persons
Quantitative clinical data
Hand strength measurement on both hands geriatric routine assessment to assess the muscle strength of the upper extremity
Gait speed geriatric routine assessment to assess mobility and muscle strength of the lower extremity important for predicting falls
Timed up go geriatric routine assessment to assess muscle strength of the lower extremity coordination and sense of balance important for predicting falls
Vitamin D level in serum underestimated vitamin in old age which is produced by the body itself through natural sunlight exposure of the skin and good kidney function in the investigators latitudes through many intermediate steps and contributes significantly to bone and muscle health in the investigators latitudes angle of incidence of the suns rays sufficient vitamin D production is only possible in the months of April to October with sufficient time spent outdoors With age however the ability to synthesize vitamin D production also decreases considerably in the spring to summer months
Quantitative data from imaging
Previous DEXA measurements of bone density T-score Z-score
Muscle mass and fat content in the muscle from previous CT scans thorax abdomen pelvis spine with muscle parts shown
Despite effective treatments for sarcopenia such as a protein-rich diet and strength training the condition remains underrecognized due to diagnostic challenges Common methods like hand strength measurement are problematic for those with rheumatic conditions or Parkinsons often yielding inaccurate results Other methods like measuring leg strength and walking speed require coordination and balance which can be difficult for those with dementia or visual impairments There is a need for tailored diagnostic solutions for diverse aging populations
Bone mineral density BMD is crucial for assessing health in older adults as low BMD indicates osteoporosis and a higher fracture risk Traditionally measured by dual X-ray absorptiometry DEXA BMD assessment can be difficult for patients with mobility issues pressure ulcers or dementia due to the requirement to remain still for extended periods
New AI-based algorithms can now automatically evaluate body tissues and patterns from routine CT scans offering reproducible results beyond human capability AI can quantify muscle mass at specific body cross-sections such as lumbar vertebral point 3 L3 which correlates with total body muscle mass and predicts muscle health CT measurements of thigh and psoas muscles can also indicate whole body skeletal muscle mass European guidelines highlight the need for muscle quantification in early sarcopenia diagnosis
Correlating AI-measured muscle mass with functional muscle strength assessments can help identify surrogate parameters for early sarcopenia detection Additionally measuring muscle fat content which correlates with strength loss is essential for assessing muscle health Innovative approaches are required as sarcopenia diagnosis is still evolving and geriatric diseases often need proportionate diagnostic and treatment strategies due to multiple comorbidities
For osteoporosis diagnostics AI can determine Hounsfield Unit HU values from CT images to represent bone density which can be correlated with DEXA results These surrogate diagnostics should follow current guidelines with results obtained within an appropriate interval of up to 18 months
Based on the primary endpoints the prevalence of muscle-healthy and probably sarcopenic individuals will be recorded Additionally the prevalence of individuals without osteoporosis with osteopenia and with confirmed osteoporosis will be calculated and included in the analysis
The secondary endpoints compare functional muscle strength measurements with retrospective quantitative CT results This exploratory analysis will test whether the AI algorithm can correlate functionality with muscle volume Additionally AI-measured muscle fatness myosteatosis could serve as a new quantifiable quality criterion for muscle health This is important because many elderly individuals cannot meet functional strength test requirements due to conditions like visual impairment dementia chronic pain joint diseases and frailty
For osteoporosis diagnostics the investigators aim to correlate bone density from DEXA measurements with CT-derived bone density of thoracic and abdominal vertebral bodies This exploratory surrogate measurement method could provide insights into bone mineral density and health from existing CT images CT-based diagnosis could be an alternative for those unable to remain still for DEXA scans
Design
This retrospective study will be conducted at a single center Participants will be identified using databases of patients who have undergone CT and DEXA scans To be included in the sarcopenia arm participants must have had a CT scan from the radiology department at the University Hospital Basel within one month of an inpatient stay For the osteoporosis arm a thorax andor abdomen CT scan and a DEXA scan must be available conducted within 18 months of each other
Anonymized DICOM datasets from the CT scans will be analyzed by an AI algorithm Data analysis will involve standard statistical comparison using Students t-test The algorithms values will be tested against reference standards and its diagnostic accuracy will be evaluated for various diseases The algorithm has been tested on 104 anatomical structures organs and organ groups Req-2022-00495
Origin of the data
At the largest geriatric medical center in Switzerland the investigators would like to include geriatric patients aged 65 and over who were undergoing inpatient treatment in the period from 01072017 to 31122022 inclusive
By testing alternative diagnostic procedures exploratory approach the investigators want to reach more people especially those who are unable to follow current diagnostic procedures due to dementia visual impairment or post-operative condition This is crucial as these patients in particular have a significantly increased risk of falling The research approach aims to address real-world challenges in order to consider prevention programs and personalized therapies for as many people as possible in the future
By using existing data and dispensing with further investigations the economic aspect in the context of rising healthcare costs is also taken into account
Scientific methodology and targets
Exploratory analysis of quantitative variables that can be determined by CT to evaluate the primary endpoints
Sarcopenia By evaluating muscle volume and fat percentage in muscle in mm3 and density in HU on CT
Osteoporosis By evaluating bone densityattenuation in HU on CT For the evaluation of muscle volumes and densities a feasibility study has already been carried out on a representative sample of over 4000 patients which suggests a normal distribution of muscle density and volume 21 Statistical deviations from this reference group are determined using a t-test or Wilcoxon test in the case of a lack of normal distribution The test for normal distribution is performed using the Kolmogorov-Smirnov test The significance level is set at α 005
The evaluation of osteoporosis is performed analogously The relationship between attenuation in HU and BMD has already been documented in the literature 22 18
Inclusion of as many patients as possible is essential for representative results
For which health-related personal data should consent be granted A general consent form Declaration of consent for the further use of health-related data and samples was introduced at the beginning of 2020 Unfortunately in practice it proved to be unreasonable to implement
The investigators consider the sample size to be n300 No consent has been obtained for any of the participants meaning that an application for exemption is being made for all persons
Quantitative clinical data
Hand strength measurement on both hands geriatric routine assessment to assess the muscle strength of the upper extremity
Gait speed geriatric routine assessment to assess mobility and muscle strength of the lower extremity important for predicting falls
Timed up go geriatric routine assessment to assess muscle strength of the lower extremity coordination and sense of balance important for predicting falls
Vitamin D level in serum underestimated vitamin in old age which is produced by the body itself through natural sunlight exposure of the skin and good kidney function in the investigators latitudes through many intermediate steps and contributes significantly to bone and muscle health in the investigators latitudes angle of incidence of the suns rays sufficient vitamin D production is only possible in the months of April to October with sufficient time spent outdoors With age however the ability to synthesize vitamin D production also decreases considerably in the spring to summer months
Quantitative data from imaging
Previous DEXA measurements of bone density T-score Z-score
Muscle mass and fat content in the muscle from previous CT scans thorax abdomen pelvis spine with muscle parts shown
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None