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Predictors of Weight Gain in Under Five Children With Severe Acute Malnutrition: An Analysis of the Icddr, B Hospital Dataset

OBJECTIVES: Children admitted to hospital with severe acute malnutrition (SAM) and acute illness can be challenging to nutritionally rehabilitate. There is limited understanding on predictors of weight gain during hospitalization in this vulnerable population. This work aimed to predict the weight g...

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Autores principales: Fahim, Shah Mohammad, Massara, Paraskevi, Das, Subhasish, Alam, Md Ashraful, Hasan, S M Tafsir, Brals, Daniella, Erdman, Lauren, Comelli, Elena, Mahfuz, Mustafa, Voskuijl, Wieger, Bandsma, Robert, Ahmed, Tahmeed
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9194369/
http://dx.doi.org/10.1093/cdn/nzac061.023
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author Fahim, Shah Mohammad
Massara, Paraskevi
Das, Subhasish
Alam, Md Ashraful
Hasan, S M Tafsir
Brals, Daniella
Erdman, Lauren
Comelli, Elena
Mahfuz, Mustafa
Voskuijl, Wieger
Bandsma, Robert
Ahmed, Tahmeed
author_facet Fahim, Shah Mohammad
Massara, Paraskevi
Das, Subhasish
Alam, Md Ashraful
Hasan, S M Tafsir
Brals, Daniella
Erdman, Lauren
Comelli, Elena
Mahfuz, Mustafa
Voskuijl, Wieger
Bandsma, Robert
Ahmed, Tahmeed
author_sort Fahim, Shah Mohammad
collection PubMed
description OBJECTIVES: Children admitted to hospital with severe acute malnutrition (SAM) and acute illness can be challenging to nutritionally rehabilitate. There is limited understanding on predictors of weight gain during hospitalization in this vulnerable population. This work aimed to predict the weight gain in children using anthropometric, biochemical, clinical, and socio-demographic variables. METHODS: We included 5,044 children aged 0–59 months with SAM hospitalized in the Dhaka Hospital at icddr, b between 2011 and 2019. Surveillance data was collected during hospitalization and analyzed retrospectively. The 15% weight gain from hospital admission to discharge was considered as outcome because it is recommended as the transition criteria from facility to community-based management. We trained a Random Forest classifier to identify the best set of predictors of a 15% weight gain. A total of 78 features were considered. The developed diagnostic model was validated based on the area under the curve (AUC) between the true positive and the false positive rates. RESULTS: The classification of data based on the outcome (weight gain > 15%) created unbalanced classes, a larger group with < 15% changes in weight and a very small group with > 15% weight gain. To balance this data disparity, we finally included 263 children in this analysis. A model including 197 children (75% of the dataset) was identified in the training dataset, while the rest were used as a test dataset. Validation in the test dataset revealed an AUC of 69.05% when considering all 78 predictors. Among the top predictors were mid-upper arm circumference at admission, family income and breastfeeding duration. CONCLUSIONS: This analysis revealed the role of socio-economic status as well as the importance of breastfeeding practices in attaining 15% weight gain from hospital admission to discharge in under five children treated for SAM. This finding has important implications for future work regarding childhood feeding practices and community-based detection of children with SAM. FUNDING SOURCES: Joannah and Brian Lawson Center for Child Nutrition, Ontario Graduate Scholarship, Canadian Institutes of Health Research Healthy Cities Research Initiative, and icddr, b, Dhaka, Bangladesh.
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spelling pubmed-91943692022-06-15 Predictors of Weight Gain in Under Five Children With Severe Acute Malnutrition: An Analysis of the Icddr, B Hospital Dataset Fahim, Shah Mohammad Massara, Paraskevi Das, Subhasish Alam, Md Ashraful Hasan, S M Tafsir Brals, Daniella Erdman, Lauren Comelli, Elena Mahfuz, Mustafa Voskuijl, Wieger Bandsma, Robert Ahmed, Tahmeed Curr Dev Nutr Maternal, Perinatal and Pediatric Nutrition OBJECTIVES: Children admitted to hospital with severe acute malnutrition (SAM) and acute illness can be challenging to nutritionally rehabilitate. There is limited understanding on predictors of weight gain during hospitalization in this vulnerable population. This work aimed to predict the weight gain in children using anthropometric, biochemical, clinical, and socio-demographic variables. METHODS: We included 5,044 children aged 0–59 months with SAM hospitalized in the Dhaka Hospital at icddr, b between 2011 and 2019. Surveillance data was collected during hospitalization and analyzed retrospectively. The 15% weight gain from hospital admission to discharge was considered as outcome because it is recommended as the transition criteria from facility to community-based management. We trained a Random Forest classifier to identify the best set of predictors of a 15% weight gain. A total of 78 features were considered. The developed diagnostic model was validated based on the area under the curve (AUC) between the true positive and the false positive rates. RESULTS: The classification of data based on the outcome (weight gain > 15%) created unbalanced classes, a larger group with < 15% changes in weight and a very small group with > 15% weight gain. To balance this data disparity, we finally included 263 children in this analysis. A model including 197 children (75% of the dataset) was identified in the training dataset, while the rest were used as a test dataset. Validation in the test dataset revealed an AUC of 69.05% when considering all 78 predictors. Among the top predictors were mid-upper arm circumference at admission, family income and breastfeeding duration. CONCLUSIONS: This analysis revealed the role of socio-economic status as well as the importance of breastfeeding practices in attaining 15% weight gain from hospital admission to discharge in under five children treated for SAM. This finding has important implications for future work regarding childhood feeding practices and community-based detection of children with SAM. FUNDING SOURCES: Joannah and Brian Lawson Center for Child Nutrition, Ontario Graduate Scholarship, Canadian Institutes of Health Research Healthy Cities Research Initiative, and icddr, b, Dhaka, Bangladesh. Oxford University Press 2022-06-14 /pmc/articles/PMC9194369/ http://dx.doi.org/10.1093/cdn/nzac061.023 Text en © The Author 2022. Published by Oxford University Press on behalf of The International Society for Human and Animal Mycology. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Maternal, Perinatal and Pediatric Nutrition
Fahim, Shah Mohammad
Massara, Paraskevi
Das, Subhasish
Alam, Md Ashraful
Hasan, S M Tafsir
Brals, Daniella
Erdman, Lauren
Comelli, Elena
Mahfuz, Mustafa
Voskuijl, Wieger
Bandsma, Robert
Ahmed, Tahmeed
Predictors of Weight Gain in Under Five Children With Severe Acute Malnutrition: An Analysis of the Icddr, B Hospital Dataset
title Predictors of Weight Gain in Under Five Children With Severe Acute Malnutrition: An Analysis of the Icddr, B Hospital Dataset
title_full Predictors of Weight Gain in Under Five Children With Severe Acute Malnutrition: An Analysis of the Icddr, B Hospital Dataset
title_fullStr Predictors of Weight Gain in Under Five Children With Severe Acute Malnutrition: An Analysis of the Icddr, B Hospital Dataset
title_full_unstemmed Predictors of Weight Gain in Under Five Children With Severe Acute Malnutrition: An Analysis of the Icddr, B Hospital Dataset
title_short Predictors of Weight Gain in Under Five Children With Severe Acute Malnutrition: An Analysis of the Icddr, B Hospital Dataset
title_sort predictors of weight gain in under five children with severe acute malnutrition: an analysis of the icddr, b hospital dataset
topic Maternal, Perinatal and Pediatric Nutrition
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9194369/
http://dx.doi.org/10.1093/cdn/nzac061.023
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