• Chih Horng Wu, Chia Ter Chao, Po Chin Liang, Tiffany Ting Fang Shih, and Jenq Wen Huang.
• Departments of Medical Imaging and Radiology, National Taiwan University Hospital and College of Medicine, National Taiwan University, Taipei, Taiwan; Graduate Institute of Clinical Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan.
• J Formos Med Assoc. 2022 Feb 1; 121 (2): 500-509.

Background/PurposeWe evaluated whether the results of the computed tomography (CT)-based sarcopenia assessment were correlated with edema-free lean soft tissue (LST) and were associated with the prognosis of patients receiving peritoneal dialysis (PD).MethodsWe conducted a prospective cohort study and enrolled patients aged >20 years who started to undergo PD between February 2009 and February 2012. All patients underwent LST evaluation and non-contrast abdominal CT for assessing the total skeletal muscle (TSM) and psoas muscle (PM) indices at the level of the third lumbar vertebra. We analyzed the correlation between LST and CT assessment of muscle mass. Then we determined optimal sex-specific cutoff values for TSM-defined and PM-defined sarcopenia to predict mortality, aided by the maximally selected rank statistics.ResultsA total of 158 patients were enrolled, of whom 41 (25.9%) and 65 (41.1%) had sarcopenia based on the TSM and PM indices, respectively. LST was significantly strong correlated with TSM and PM indices (r = 0.517, p < 0.001 and r = 0.688, p < 0.001, respectively). In univariate and multivariate analyses after adjusting clinical and PD-related parameters, only patients with PM-defined sarcopenia had poorer survival than did those without (hazard ratio [HR]: 2.386, 95% confidence interval [CI]: 1.315-4.330), but patients with TSM-defined sarcopenia did not show a poorer survival (HR: 1.608, 95% CI: 0.860-3.006).ConclusionSarcopenia assessment based on CT was strongly correlated with LST and PM-defined sarcopenia indicated poor prognosis in patients receiving long-term PD.Copyright © 2021. Published by Elsevier B.V.

18 keywords...

hide…