4.5 Article

Refining dual-energy x-ray absorptiometry data to predict mortality among cirrhotic outpatients: A retrospective study

期刊

NUTRITION
卷 85, 期 -, 页码 -

出版社

ELSEVIER SCIENCE INC
DOI: 10.1016/j.nut.2020.111132

关键词

Liver cirrhosis; Sarcopenia; Dual-energy x-ray absorptiometry; Mortality; Edema; Ascites

资金

  1. Sao Paulo Research Foundation (FAPESP) [2014/22572-9, 2017/25592-9]
  2. Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq)

向作者/读者索取更多资源

This study found that in outpatients with cirrhosis, upper limb muscle mass index (ULMMI) is more suitable for predicting mortality rate, making it a more reliable and effective measure compared to appendicular muscle mass index (AMMI).
Objective: The aim of this study was to compare the effects of muscle wasting according to measures obtained by different limb muscle mass indexes, to find the best mortality predictor among outpatients with cirrhosis. Methods: Patients with liver cirrhosis (N = 210) were submitted to dual-energy x-ray absorptiometry (DXA). Appendicular muscle mass (AMM), AMM index (AMMI), upper limb muscle mass (ULMM), and ULMM index (ULMMI) were calculated. The Model for End-Stage Liver Disease, anthropometric measures, and the presence of ascites and edema were also registered. Multiple logistic regressions were performed to determine mortality predictors; the area under the receiver operating characteristic curve was used to establish the best cutoff point to predict mortality. Results: The mean follow-up duration was 49 & sect; 15.59 mo. ULMM and ULMMI were clearly associated with mortality (P = 0.007 and 0.001, respectively), whereas AMM and AMMI were not. After calculating the cutoff points for men and women, the presence of a depleted ULMMI as a categorical variable was associated with a mortality risk 2.5 times higher. Conclusions: The results suggest that using ULMMI is better than AMMI for predicting mortality of outpatients with cirrhosis, thus offering a better measure to detect muscle wasting in this population using DXA. (c) 2020 Elsevier Inc. All rights reserved.

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