A novel screening tool to predict severe acute malnutrition through automated monitoring of weight-for-age growth curves

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dc.contributor.author Nel, Sanja
dc.contributor.author Feucht, Ute Dagmar
dc.contributor.author Nel, Andre L.
dc.contributor.author Becker, Piet J.
dc.contributor.author Wenhold, Friedeburg Anna Maria
dc.date.accessioned 2023-11-29T13:05:31Z
dc.date.available 2023-11-29T13:05:31Z
dc.date.issued 2022-07
dc.description DATA AVAILABILITY STATEMENT : Data are available from the authors on request. en_US
dc.description.abstract Weight‐for‐age (WFA) growth faltering often precedes severe acute malnutrition (SAM) in children, yet it is often missed during routine growth monitoring. Automated interpretation of WFA growth within electronic health records could expedite the identification of children at risk of SAM. This study aimed to develop an automated screening tool to predict SAM risk from WFA growth, and to determine its predictive ability compared with simple changes in weight or WFA z‐score. To develop the screening tool, South African child growth experts (n = 30) rated SAM risk on 100 WFA growth curves, which were then used to train an artificial neural network (ANN) to assess SAM risk from consecutive WFA z‐scores. The ANN was validated in 185 children under five (63 SAM cases; 122 controls) using diagnostic accuracy methodology. The ANN's performance was compared with that of changes in weight or WFA z‐score. Even though experts' SAM risk ratings of the WFA growth curves differed considerably, the ANN achieved a sensitivity of 73.0% (95% confidence interval [CI]: 60.3; 83.4), specificity of 86.1% (95% CI: 78.6; 91.7) and receiveroperating characteristic curve area of 0.795 (95% CI: 0.732; 0.859) during validation with real cases, outperforming changes in weight or WFA z‐scores. The ANN, as an automated screening tool, could markedly improve the identification of children at risk of SAM using routinely collected WFA growth information. en_US
dc.description.department Human Nutrition en_US
dc.description.department Paediatrics and Child Health en_US
dc.description.librarian am2023 en_US
dc.description.sdg SDG-02:Zero Hunger en_US
dc.description.sdg SDG-03:Good heatlh and well-being en_US
dc.description.uri https://wileyonlinelibrary.com/journal/mcn en_US
dc.identifier.citation Nel, S., Feucht, U.D., Nel, A.L., Becker, P.J., & Wenhold, F.A.M. (2022). A novel screening tool to predict severe acute malnutrition through automated monitoring of weight‐for‐age growth curves. Maternal & Child Nutrition, 18, e13364. https://DOI.org/10.1111/mcn.13364. en_US
dc.identifier.issn 1740-8695 (print)
dc.identifier.issn 1740-8709 (online)
dc.identifier.other 10.1111/mcn.13364
dc.identifier.uri http://hdl.handle.net/2263/93547
dc.language.iso en en_US
dc.publisher Wiley Open Access en_US
dc.rights © 2022 The Authors. Maternal & Child Nutrition published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial‐NoDerivs License. en_US
dc.subject Child growth monitoring en_US
dc.subject Computer en_US
dc.subject Electronic health records en_US
dc.subject Failure to thrive en_US
dc.subject Neural networks en_US
dc.subject Nutrition screening en_US
dc.subject Artificial intelligence (AI) en_US
dc.subject Weight‐for‐age (WFA) en_US
dc.subject Severe acute malnutrition (SAM) en_US
dc.subject Artificial neural network (ANN) en_US
dc.subject SDG-03: Good health and well-being en_US
dc.subject SDG-02: Zero hunger en_US
dc.title A novel screening tool to predict severe acute malnutrition through automated monitoring of weight-for-age growth curves en_US
dc.type Article en_US


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