Diagnostic Value of Sarcopenia Computed Tomography Metrics for Older Patients with or without Cancers with Gastrointestinal Disorders |
| |
| Institution: | 1. The Second School of Clinical Medicine, Xuzhou Medical University, Xuzhou, Jiangsu, China;2. Department of Rehabilitation, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China;3. Medical Technology School, Xuzhou Medical University, Xuzhou, Jiangsu, China;4. Department of Medical Imaging, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, China;1. Department of Medicine, University Hospitals Cleveland Medical Center, Cleveland, OH, USA;2. Division of Infectious Diseases and HIV Medicine in the Department of Medicine, Case Western Reserve University School of Medicine, Cleveland, OH, USA;3. Geriatric Research Education and Clinical Center (GRECC), VA Northeast Ohio Healthcare System, Cleveland, OH, USA;4. Cleveland Institute for Computational Biology at Case Western Reserve University School of Medicine, Cleveland, OH, USA;5. Department of Pharmacy, VA Northeast Ohio Healthcare System, Cleveland, OH, USA;1. Center for Excellence in Assisted Living (CEAL), Workforce & Quality Innovations, LLC, Bear Creek, NC, USA;2. Center for Excellence in Assisted Living (CEAL), Pioneer Network, Orlando, FL, USA;3. Center for Excellence in Assisted Living (CEAL), AMDA, The Society for Post-Acute and Long-Term Care Medicine, Columbia, MD, USA;4. Center for Excellence in Assisted Living (CEAL), American Assisted Living Nurses Association (AALNA), NAPA, CA, USA;5. Center for Excellence in Assisted Living (CEAL), ADvancing States, Arlington, VA, USA;6. Center for Excellence in Assisted Living (CEAL), Alzheimer’s Association, Washington, DC, USA;7. CLP Consulting, Bloomington, IN, USA;1. Department of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada;2. Department of Medicine, Cumming School of Medicine, University of Calgary and Alberta Health Services, Calgary, Alberta, Canada;3. Department of Family Medicine, Cumming School of Medicine, University of Calgary and Alberta Health Services, Calgary, Alberta, Canada;4. Infection Prevention and Control, Alberta Health Services, Calgary, Alberta, Canada;1. Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada;2. ICES, Toronto, Ontario, Canada;3. Department of Medicine, Ottawa Hospital Research Institute, University of Ottawa, Ottawa, Ontario, Canada;4. Bruyère Research Institute, Ottawa, Ontario, Canada;5. Schlegel Research Chair in Geriatric Medicine, Schlegel Research Institute for Aging, Waterloo, Ontario, Canada;6. School of Public Health Sciences, University of Waterloo, Ontario, Canada;1. Department of Sociology and Gerontology, Miami University, Oxford, OH, USA;2. Scripps Gerontology Center, Miami University, Oxford, OH, USA;3. Department of Economics, Miami University Farmer School of Business, Oxford, OH, USA;4. The Pennsylvania State University, Program for Person Centered Living Systems of Care, University Park, PA, USA;5. The Polisher Research Institute at Abramson Senior Care, Blue Bell, PA, USA;1. University of California Los Angeles, Center for Health Policy Research, Los Angeles, CA, USA;2. Department of Health Policy and Management, Fielding School of Public Health, University of California Los Angeles, Los Angeles, CA, USA;3. Senior Care Action Network (SCAN) Health Group, Long Beach, CA, USA;4. Division of Geriatric Medicine, University of California Los Angeles, Los Angeles, CA, USA |
| |
| Abstract: | ObjectivesThe diagnostic utility of poor body composition measures in sarcopenia remains unclear. We hypothesize that the skeletal muscle gauge combination of skeletal muscle index (SMI) and skeletal muscle density (SMD); SMG = SMI × SMD] would have significant diagnostic and predictive value in certain muscle regions and populations.DesignProspective cross-sectional study.Setting and ParticipantsWe examined inpatients age ≥60 years with or without cancer and with gastrointestinal disorders.MethodsWe used computed tomography (CT) image metrics in the 12th thoracic (T12), third lumbar (L3), erector spinae muscle (ESM), and psoas muscle (PM) regions to establish correlations with the 2019 Asian Working Group for Sarcopenia Consensus and used receiver operating characteristic area under the curve (AUC) to compare differences between metrics. Associations between CT metrics and mortality were reported as relative risk after adjustments.ResultsWe evaluated 385 patients (median age, 69.0 years; 60.8% men) and found consistent trends in cancer (49.6%) and noncancer (50.4%) cohorts. SMG had a stronger correlation with muscle mass than SMD mean rho: 0.68 (range, 0.59?0.73) vs 0.39 (range, 0.28?0.48); all P < .01] in T12, L3, and PM regions and a stronger correlation with muscle function than SMI mean rho: 0.60 (range, 0.50?0.77) vs 0.36 (range, 0.22?0.58); all P < .05] in T12, ESM, and L3 regions. SMG outperformed SMI in diagnostic accuracy in all regions, particularly for L3 (AUC: 0.87?0.88 vs 0.80?0.82; both P < .05). PMG (PM gauge) and L3SMG did not differ, whereas EMG (ESM gauge) or T12SMG and L3SMG did (AUC: 0.80?0.82 vs 0.87?0.88; all P < .05). L3SMI, L3SMD, T12SMG, EMG, and PMG showed no association with 1-year cancer-related mortality after adjusting for confounders; however, L3SMG relative risk = 0.92 (0.85?0.99); P = .023) was.Conclusions and ImplicationsL3SMG covers all features of sarcopenia with more diagnostic value than other metrics, allowing a complete sarcopenia assessment with CT alone and not just in populations with cancer. |
| |
| Keywords: | Sarcopenia CT imaging AWGS2019 skeletal muscle gauge hospitalized elderly |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
