An anthropometric approach to characterising neonatal morbidity and body composition, using air displacement plethysmography as a criterion method.

Autor: Huvanandana J; School of Electrical and Information Engineering, University of Sydney, Sydney, Australia., Carberry AE; School of Electrical and Information Engineering, University of Sydney, Sydney, Australia., Turner RM; School of Public Health and Community Medicine, University of New South Wales, Sydney, Australia., Bek EJ; Sydney Medical School, University of Sydney, Sydney, Australia., Raynes-Greenow CH; Sydney School of Public Health, University of Sydney, Sydney, Australia., McEwan AL; School of Electrical and Information Engineering, University of Sydney, Sydney, Australia., Jeffery HE; School of Electrical and Information Engineering, University of Sydney, Sydney, Australia.; Sydney Medical School, University of Sydney, Sydney, Australia.; Sydney School of Public Health, University of Sydney, Sydney, Australia.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2018 Mar 30; Vol. 13 (3), pp. e0195193. Date of Electronic Publication: 2018 Mar 30 (Print Publication: 2018).
DOI: 10.1371/journal.pone.0195193
Abstrakt: 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.
Databáze: MEDLINE
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