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. 2019 Jul 1:2019:4740825.
doi: 10.1155/2019/4740825. eCollection 2019.

Predictive Model for the Risk of Severe Acute Malnutrition in Children

Olivier Mukuku  1 , Augustin Mulangu Mutombo  2 , Lewis Kipili Kamona  2 , Toni Kasole Lubala  2 , Paul Makan Mawaw  3 , Michel Ntetani Aloni  4 , Stanislas Okitotsho Wembonyama  2 , Oscar Numbi Luboya  1   2   3
Affiliations

Predictive Model for the Risk of Severe Acute Malnutrition in Children

Olivier Mukuku et al. J Nutr Metab. .

Abstract

Background: The nutritional status is the best indicator of the well-being of the child. Inadequate feeding practices are the main factors that affect physical growth and mental development. The aim of this study was to develop a predictive score of severe acute malnutrition (SAM) in children under 5 years of age.

Methods: It was a case-control study. The case group (n = 263) consisted of children aged 6 to 59 months admitted to hospital for SAM that was defined by a z-score weight/height < -3 SD or presence of edema of malnutrition. We performed a univariate and multivariate analysis. Discrimination score was assessed using the ROC curve and the calibration of the score by Hosmer-Lemeshow test.

Results: Low birth weight, history of recurrent or chronic diarrhea, daily meal's number less than 3, age of breastfeeding's cessation less than 6 months, age of introduction of complementary diets less than 6 months, maternal age below 25 years, parity less than 5, family history of malnutrition, and number of children under 5 over 2 were predictive factors of SAM. Presence of these nine criteria affects a certain number of points; a score <6 points defines children at low risk of SAM, a score between 6 and 8 points defines a moderate risk of SAM, and a score >8 points presents a high risk of SAM. The area under ROC curve of this score was 0.9685, its sensitivity was 93.5%, and its specificity was 93.1%.

Conclusion: We propose a simple and efficient prediction model for the risk of occurrence of SAM in children under 5 years of age in developing countries. This predictive model of SAM would be a useful and simple clinical tool to identify people at risk, limit high rates of malnutrition, and reduce disease and child mortality registered in developing countries.

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Figures

Figure 1
Figure 1
ROC curve showing the performance of SAM predictive score.
Figure 2
Figure 2
Sensitivity and specificity of SAM predictive score.
See this image and copyright information in PMC

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