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An anthropometric approach to characterising neonatal morbidity and body composition, using air displacement
Jacqueline Huvanandana1, Angela E Carberry1, Robin M Turner2
1School of Electrical and Information Engineering, University of Sydney, Sydney, Australia.
Insights
New models using anthropometry can identify neonates at risk of malnutrition in low- and middle-income countries. These simple, low-cost tools aid in early detection and nutritional management of infants.
Area of Science:
- Neonatal health
- Nutritional science
- Biostatistics
Background:
- Infant undernutrition and morbidity are significant burdens in low- and middle-income countries (LMICs).
- Simple, low-cost, and effective infant monitoring approaches are needed.
- Anthropometry remains crucial for assessing growth and nutritional status.
Purpose of the Study:
- To develop models for identifying neonates at risk of malnutrition.
- To utilize logistic and linear regression for screening neonatal morbidity and estimating body composition.
- To assess the effectiveness of anthropometric variables in predicting neonatal health outcomes.
Main Methods:
- Logistic regression models were developed using combinations of anthropometric variables (birthweight, length, chest, and mid-thigh circumferences) to predict composite neonatal morbidity and low/high body fat (BF%).
- Linear regression models were used for estimating neonatal fat mass as a measure of body composition.
- Air displacement plethysmography was used to measure BF% for comparison.
Main Results:
- Models combining birthweight, length, chest, and mid-thigh circumferences achieved an area under the receiver-operator characteristic curve (AUC) of 0.740 for composite morbidity.
- AUCs for identifying low and high BF% were 0.827 and 0.834, respectively.
- BF% measured by air displacement plethysmography showed strong predictive ability (AUC 0.786) for morbidity, while birthweight percentiles had lower AUCs for morbidity (0.695) but were effective for BF% prediction (0.792-0.834).
Conclusions:
- Developed models show potential for use in LMICs to identify infants requiring nutritional management.
- These anthropometric models can supplement or replace traditional methods like birthweight for gestational age percentiles, especially when they are variable or unavailable.
- The models can detect "appropriately grown, low fat" newborns who might be missed by other methods.
Background:
With the greatest burden of infant undernutrition and morbidity in low and middle income countries (LMICs), there is a need for suitable approaches to monitor infants in a simple, low-cost and effective manner. Anthropometry continues to play a major role in characterising growth and nutritional status.
Methods:
We developed a range of models to aid in identifying neonates at risk of malnutrition. We first adopted a logistic regression approach to screen for a composite neonatal morbidity, low and high body fat (BF%) infants. We then developed linear regression models for the estimation of neonatal fat mass as an assessment of body composition and nutritional status.
Results:
We fitted logistic regression models combining up to four anthropometric variables to predict composite morbidity and low and high BF% neonates. The greatest area under receiver-operator characteristic curves (AUC with 95% confidence intervals (CI)) for identifying composite morbidity was 0.740 (0.63, 0.85), resulting from the combination of birthweight, length, chest and mid-thigh circumferences. The AUCs (95% CI) for identifying low and high BF% were 0.827 (0.78, 0.88) and 0.834 (0.79, 0.88), respectively. For identifying composite morbidity, BF% as measured via air displacement plethysmography showed strong predictive ability (AUC 0.786 (0.70, 0.88)), while birthweight percentiles had a lower AUC (0.695 (0.57, 0.82)). Birthweight percentiles could also identify low and high BF% neonates with AUCs of 0.792 (0.74, 0.85) and 0.834 (0.79, 0.88). We applied a sex-specific approach to anthropometric estimation of neonatal fat mass, demonstrating the influence of the testing sample size on the final model performance.
Conclusions:
These models display potential for further development and evaluation in LMICs to detect infants in need of further nutritional management, especially where traditional methods of risk management such as birthweight for gestational age percentiles may be variable or non-existent, or unable to detect appropriately grown, low fat newborns.
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