Mid‐Upper Arm Circumference Z‐Score as Determinant of Nutrition Status: Does Occam's Razor Apply?

Pompeii-Wolfe, Cecelia; Sentongo, Timothy

doi:10.1002/ncp.10012

Cited by 1 publication

(2 citation statements)

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“…19 MUAC is easy to measure, requires simple inexpensive equipment, and is less prone to error, with less interindividual variability compared with other anthropometric measurements. 20 In 2014, the Academy of Nutrition and Dietetics together with ASPEN published standardized guidelines for identifying and categorizing malnutrition in children using single anthropometric data points including BMI, WFL, and MUAC z-scores. 10 In 2017, Abdel-Rahman et al published the age-specific reference values for MUAC zscores in children aged 2 months through 18.5 years.…”

Section: Discussionmentioning

confidence: 99%

“…MUAC has been widely used across nations, especially in resource‐limited settings, as an early indicator of malnutrition 18 and as an effective marker to predict mortality due to malnutrition in children and adolescents 19 . MUAC is easy to measure, requires simple inexpensive equipment, and is less prone to error, with less interindividual variability compared with other anthropometric measurements 20 . In 2014, the Academy of Nutrition and Dietetics together with ASPEN published standardized guidelines for identifying and categorizing malnutrition in children using single anthropometric data points including BMI, WFL, and MUAC z ‐scores 10 .…”

Section: Discussionmentioning

confidence: 99%

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Utility of Mid‐Upper Arm Circumference in Diagnosing Malnutrition in Children With Cystic Fibrosis

et al. 2020

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Background: Standardized diagnostic indicators for malnutrition using growth percentile z-scores (weight for length or body mass index [BMI]) and mid-upper arm circumference (MUAC) z-scores are being used in clinical practice; however, their application to the pediatric cystic fibrosis (CF) population is not well described. In this study, we aim to compare growth percentile z-scores and MUAC z-scores in diagnosing and classifying malnutrition in children with CF and assess the relationship between their degree of malnourishment and corresponding pulmonary function tests (PFTs). Methods: In this retrospective observational outpatient study of 49 pediatric CF patients, data were collected on baseline characteristics, anthropometrics, and PFTs over 12 months. Agreement in malnutrition diagnoses was quantified by Cohen κ statistics. Pearson test assessed the correlation between MUAC and BMI z-scores as well as PFTs and anthropometrics. Serial anthropometrics and PFTs were obtained and compared for a subset of patients (n = 28). Results: Growth percentile and MUAC z-scores were positively correlated in diagnosing malnutrition (Pearson correlation r = 0.87), but MUAC z-scores identified more patients as malnourished compared with growth percentile z-scores (49% vs 12%, Cohen κ of 0.22 [95% CI, 0.04-0.40]). There was no significant relationship between anthropometrics and PFTs. MUAC z-scores increased significantly over time, but BMI z-scores did not show this trend. Conclusions: Our small-scale data suggest a promising role for MUAC z-scores in classifying malnutrition and in measuring changes in nutrition status over time in pediatric CF.

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Section: Discussionmentioning

confidence: 99%