ArticleClinical nutrition (Edinburgh, Scotland)2020
Developing a screening tool for sarcopenia in hospitalized geriatric patients: Estimation of appendicular skeletal muscle mass using bioelectrical impedance.
Article in Clinical nutrition (Edinburgh, Scotland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 2 of them syntheses that pooled it.
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Who cites it
6 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The risk prediction models for sarcopenia in older adults: a systematic review and critical appraisal.Frontiers in public health · 2026Pooled it
- Risk prediction models for sarcopenia in elderly people: a systematic review and meta-analysis.Frontiers in medicine · 2025Pooled it
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- The utility of bioelectrical impedance analysis in the diagnosis of sarcopenia: a systematic review.The Journal of the Canadian Chiropractic Association · 2022Article
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4 authors.
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Abstract
BACKGROUND &
aimsSarcopenia is now a billable ICD-10 geriatric condition characterized by low appendicular skeletal muscle mass (ASMM) and low function. There is an increasing need for portable, provider-friendly, cost-effective methods for estimating ASMM. The overall goal of this project was to create and validate a regression model for obtaining ASMM from Bioelectrical Impedance Analysis (BIA) measurements using Dual-energy X-ray Absorptiometry (DXA) as the reference.
methodsGeriatric patients (≥65 years of age) were enrolled during an acute hospitalization. Body composition measurements were obtained through DXA and BIA devices. The ASMM prediction model was derived using stepwise multiple regression modeling. The model was 10 fold validated and tested as a screening tool (sensitivity, specificity, positive and negative predictive values) using the Foundation for the NIH Sarcopenia Project (FNIH) definition.
resultsThe following variables were selected by stepwise regression modeling: sex, body mass index, max grip strength, and fat mass derived by BIA. The model was internally validated with 10 fold cross validation. Using the FNIH definition, the model was found to have a sensitivity of 80%, a specificity of 91%, a positive predictive value of 73% and a negative predictive value of 93%.
conclusionsWe have developed a screening tool that can be easily used in geriatric patients to screen for sarcopenia. Once validated with a larger sample, the developed prediction model can be used to estimate ASMM using provider-friendly measurements and can be easily implemented as a sensitive screening tool for identifying patients at risk for sarcopenia. Those identified at risk would undergo further functional testing for diagnosis and treatment of sarcopenia.
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