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Validating a linear regression equation using mid-upper arm circumference to predict body mass index.

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Summary

Estimating body mass index (BMI) in hospitalized patients using mid-upper arm circumference (MUAC) showed poor accuracy for actual BMI. However, low MUAC effectively identifies undernutrition in clinical settings.

Keywords:
95% limits of agreementAnthropometryBiasBody mass indexMid-upper arm circumferenceValidation

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Area of Science:

  • Clinical Nutrition
  • Anthropometry
  • Patient Assessment

Background:

  • Accurate nutritional assessment in hospitalized patients is difficult without weight and height measurements.
  • Mid-upper arm circumference (MUAC) is a potential alternative anthropometric measure.

Purpose of the Study:

  • To validate a regression equation for predicting Body Mass Index (BMI) using MUAC.
  • To evaluate a global MUAC cut-off (≤24 cm) for detecting undernutrition.

Main Methods:

  • Prospective measurement of standing height, weight, and MUAC in 201 stable patients.
  • Bland-Altman analysis to assess agreement between calculated and predicted BMI.

Main Results:

  • Predicted BMI from MUAC showed poor calibration with a bias of +1.076.
  • MUAC ≤24 cm demonstrated high sensitivity (97%) and good specificity (83%) for detecting undernutrition.

Conclusions:

  • BMI prediction using MUAC is unreliable for precise estimation in hospitalized patients.
  • Low MUAC (≤24 cm) is a valuable and accurate indicator for identifying undernutrition.