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Validating a linear regression equation using mid-upper arm circumference to predict body mass index
Adwaith Krishna Surendran1, Surendran Deepanjali2
1Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, 605006, India.
Background:
Estimating body mass index (BMI) in hospitalised patients for nutritional assessment is challenging when measurement of weight and height is not feasible. The study aimed to validate a previously published regression equation to predict BMI using mid-upper arm circumference (MUAC). We also evaluated the proposed global MUAC cut-off of ≤24 cm to detect undernutrition.
Methods:
We measured standing height, weight, and MUAC prospectively in a sample of stable patients. Agreement between calculated and predicted BMI was evaluated using Bland-Altman analysis.
Results:
We studied 201 patients; 102 (51%) were male. Median (IQR age was 42 (29-50) years. 95% limits of agreement between predicted and calculated BMI were +0.6767 to +1.712 and the bias was +1.076. MUAC ≤24 cm was 97% sensitive and 83% specific to detect undernutrition.
Conclusion:
BMI derived from MUAC had poor calibration for estimating actual BMI. However, low MUAC has good discriminative accuracy to detect undernutrition.
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