Examining Mid-Upper Arm Circumference Malnutrition z-Score Thresholds.
Karen Stephens1, Meike Orlick2, Shannon Beattie2
1Nutrition Services, Children's Mercy Hospital, Kansas City, Missouri, USA.
Summary
Existing malnutrition thresholds for mid-upper arm circumference (MUACz) misclassify many children, especially those with severe malnutrition. Optimization is needed to accurately identify children at risk and improve nutrition status classification.
Area of Science:
- Pediatric Nutrition
- Anthropometry
- Malnutrition Diagnosis
Background:
- Common anthropometric z-scores (BMIz, WLz, MUACz) for malnutrition diagnosis have identical thresholds but are not fully concordant.
- Existing thresholds may not accurately reflect a child's true nutritional status.
Purpose of the Study:
- To critically examine mid-upper arm circumference z-score (MUACz) thresholds for their ability to correctly classify nutrition status in children.
- To assess the concordance and accuracy of anthropometric z-scores in diagnosing malnutrition.
Main Methods:
- A 2-year prospective single-center study involving 10,401 children aged ≤18 years.
- Estimated sensitivity, specificity, and predictive performance of malnutrition classification thresholds against clinician-based assessments.
- Analyzed distributions of weight-for-length (WLz), body mass index (BMIz), and MUACz z-scores.
Main Results:
- Participant z-scores indicated they were smaller and shorter than the reference U.S. population.
- Broad and overlapping distributions of MUACz, BMIz, and WLz were observed between nutrition classification groups.
- Misclassification rates increased with malnutrition severity, from 8% (no malnutrition) to 71% (severe malnutrition).
Conclusions:
- The sensitivity of proposed MUACz thresholds decreases with increasing malnutrition severity, necessitating optimization.
- Current thresholds do not accurately classify all children, leading to significant misclassification, particularly in severe cases.
- Further refinement of thresholds is required to minimize misclassification and accurately identify children at risk.
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