Grant R Williams1, Allison M Deal2, Hyman B Muss3, Marc S Weinberg4, Hanna K Sanoff3, Emily J Guerard5, Kirsten A Nyrop3, Mackenzi Pergolotti6, Shlomit Strulov Shachar7. 1. University of Alabama at Birmingham, Birmingham, AL, USA; University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. Electronic address: gwillia@uab.edu. 2. UNC Lineberger Comprehensive Cancer Center, Chapel Hill, NC, USA. 3. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; UNC Lineberger Comprehensive Cancer Center, Chapel Hill, NC, USA. 4. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. 5. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; University of Wisconsin at Madison, Madison, WI, USA. 6. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Colorado State University, Fort Collins, CO, USA. 7. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Division of Oncology, Rambam Health Care Campus, Haifa, Israel.
Abstract
OBJECTIVE: Computerized tomography (CT) imaging is routine in oncologic care and can be used to measure muscle quantity and composition that may improve prognostic assessment of older patients. This study examines the association of single-slice CT-assessed muscle measurements with a frailty index in older adults with cancer. MATERIALS AND METHODS: Using the Carolina Senior Registry, we identified patients with CT imaging within 60days ± of geriatric assessment (GA). A 36-item Carolina Frailty Index was calculated. Cross-sectional skeletal muscle area (SMA) and Skeletal Muscle Density (SMD) were analyzed from CT scan L3 lumbar segments. SMA and patient height (m2) were used to calculate skeletal muscle index (SMI). Skeletal Muscle Gauge (SMG) was calculated by multiplying SMI×SMD. RESULTS: Of the 162 patients, mean age 73, 53% were robust, 27% pre-frail, and 21% frail. Significant differences were found between robust and frail patients for SMD (29.4 vs 24.1 HU, p<0.001) and SMG (1188 vs 922AU, p=0.003), but not SMI (41.9 vs 39.5cm2/m2, p=0.29). After controlling for age and gender, for every 5 unit decrease in SMD, the prevalence ratio of frailty increased by 20% (PR=1.20 [1.09, 1.32]) while the prevalence of frailty did not differ based on SMI. CONCLUSIONS: Muscle mass (measured as SMI) was poorly associated with a GA-based frailty index. Muscle density, which reflects muscle lipid content, was more associated with frailty. Although frailty and loss of muscle mass are both age-related conditions that are predictive of adverse outcomes, our results suggest they are separate entities.
OBJECTIVE: Computerized tomography (CT) imaging is routine in oncologic care and can be used to measure muscle quantity and composition that may improve prognostic assessment of older patients. This study examines the association of single-slice CT-assessed muscle measurements with a frailty index in older adults with cancer. MATERIALS AND METHODS: Using the Carolina Senior Registry, we identified patients with CT imaging within 60days ± of geriatric assessment (GA). A 36-item Carolina Frailty Index was calculated. Cross-sectional skeletal muscle area (SMA) and Skeletal Muscle Density (SMD) were analyzed from CT scan L3 lumbar segments. SMA and patient height (m2) were used to calculate skeletal muscle index (SMI). Skeletal Muscle Gauge (SMG) was calculated by multiplying SMI×SMD. RESULTS: Of the 162 patients, mean age 73, 53% were robust, 27% pre-frail, and 21% frail. Significant differences were found between robust and frail patients for SMD (29.4 vs 24.1 HU, p<0.001) and SMG (1188 vs 922AU, p=0.003), but not SMI (41.9 vs 39.5cm2/m2, p=0.29). After controlling for age and gender, for every 5 unit decrease in SMD, the prevalence ratio of frailty increased by 20% (PR=1.20 [1.09, 1.32]) while the prevalence of frailty did not differ based on SMI. CONCLUSIONS: Muscle mass (measured as SMI) was poorly associated with a GA-based frailty index. Muscle density, which reflects muscle lipid content, was more associated with frailty. Although frailty and loss of muscle mass are both age-related conditions that are predictive of adverse outcomes, our results suggest they are separate entities.
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