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Bioelectrical Impedance Analysis for the Assessment of Sarcopenia in Patients with Cancer: A Systematic Review

Aleixo GFP, Shachar SS, Nyrop KA, Muss HB, Battaglini CL, Williams GR

The oncologist, 2020

3,607 participants · 118 citations

Abstract

Background: The loss of muscle mass, known as sarcopenia, is a natural process of aging that is associated with adverse health outcomes regardless of age. Because cancer is a disease of aging, interest in sarcopenia and its potential impact in multiple cancer populations has increased significantly. Bioelectrical impedance analysis (BIA) is a guideline-accepted method for sarcopenia detection. This systematic review assesses the literature pertaining to BIA use in the detection of sarcopenia in adults with cancer.

Materials and methods: In this systematic review, a search of the literature for randomized controlled trials and observational studies was conducted using MEDLINE, Cochrane CENTRAL, and EMBASE, through July 15, 2019. The study is registered at Prospero (CRD 42019130707). For study inclusion, patients had to be aged 18 years or older and diagnosed with solid or hematological neoplasia, and BIA had to be used to detect sarcopenia.

Results: Through our search strategy, 5,045 articles were identified, of which 24 studies were selected for inclusion in the review (total number of 3,607 patients). In five studies, BIA was rated comparable to axial computed tomography (CT) scan, calf circumference, or grip strength for sarcopenia screening. In 14 studies, BIA-identified sarcopenia was associated with adverse clinical outcomes.

Conclusion: BIA is an accurate method for detecting sarcopenia in adults with cancer prior to treatment and is a viable alternative to CT,…

How strong is the evidence?

Evidence Hub reads this as a systematic review, done in people. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid. Reported size: 3,607 participants or records.

How the five levels of evidence work. The level is assigned automatically from the abstract and can be wrong, so check the paper.

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