Developing a screening tool for sarcopenia in hospitalized geriatric patients: Estimation of appendicular skeletal muscle mass using bioelectrical impedance

Rachel R Deer; Leyla Akhverdiyeva; Yong-Fang Kuo; Elena Volpi

doi:10.1016/j.clnu.2019.10.005

Developing a screening tool for sarcopenia in hospitalized geriatric patients: Estimation of appendicular skeletal muscle mass using bioelectrical impedance

Clin Nutr. 2020 Jul;39(7):2233-2237. doi: 10.1016/j.clnu.2019.10.005. Epub 2019 Oct 14.

Authors

Rachel R Deer¹, Leyla Akhverdiyeva², Yong-Fang Kuo³, Elena Volpi⁴

Affiliations

¹ Sealy Center on Aging, University of Texas Medical Branch, 301 University Blvd, Galveston, TX, 77555, USA; Division of Rehabilitation Sciences, University of Texas Medical Branch, 301 University Blvd, Galveston, TX, 77555, USA. Electronic address: rrdeer@utmb.edu.
² School of Medicine, University of Texas Medical Branch, 301 University Blvd, Galveston, TX, 77555, USA. Electronic address: leakhver@utmb.edu.
³ Sealy Center on Aging, University of Texas Medical Branch, 301 University Blvd, Galveston, TX, 77555, USA; Dept. of Preventive Medicine and Population Health, Office of Biostatistics, University of Texas Medical Branch, 301 University Blvd, Galveston, TX, 77555, USA. Electronic address: yokuo@utmb.edu.
⁴ Sealy Center on Aging, University of Texas Medical Branch, 301 University Blvd, Galveston, TX, 77555, USA; Dept. of Internal Medicine, Division of Geriatrics, University of Texas Medical Branch, 301 University Blvd, Galveston, TX, 77555, USA. Electronic address: evolpi@utmb.edu.

Abstract

Background & aims: Sarcopenia 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.

Methods: Geriatric 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.

Results: The 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%.

Conclusions: We 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.

Keywords: Appendicular skeletal muscle mass; Bioelectrical impendence analysis; Dual-energy X-ray absorptiometry; Geriatric patients; Sarcopenia.

Publication types

Comparative Study
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Validation Study

MeSH terms

Absorptiometry, Photon
Aged
Aged, 80 and over
Body Composition*
Diagnostic Screening Programs*
Electric Impedance
Female
Geriatric Assessment*
Hospitalization*
Humans
Inpatients
Male
Muscle, Skeletal / physiopathology*
Predictive Value of Tests
Prognosis
Reproducibility of Results
Sarcopenia / diagnosis*
Sarcopenia / physiopathology

Abstract

Publication types

MeSH terms

Grants and funding