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Derivation and external validation of clinical prediction rules identifying children at risk of linear growth faltering
BACKGROUND: Nearly 150 million children under-5 years of age were stunted in 2020. We aimed to develop a clinical prediction rule (CPR) to identify children likely to experience additional stunting following acute diarrhea, to enable targeted approaches to prevent this irreversible outcome. METHODS:...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
eLife Sciences Publications, Ltd
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9833824/ https://www.ncbi.nlm.nih.gov/pubmed/36607225 http://dx.doi.org/10.7554/eLife.78491 |
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author | Ahmed, Sharia M Brintz, Ben J Pavlinac, Patricia B Shahrin, Lubaba Huq, Sayeeda Levine, Adam C Nelson, Eric J Platts-Mills, James A Kotloff, Karen L Leung, Daniel T |
author_facet | Ahmed, Sharia M Brintz, Ben J Pavlinac, Patricia B Shahrin, Lubaba Huq, Sayeeda Levine, Adam C Nelson, Eric J Platts-Mills, James A Kotloff, Karen L Leung, Daniel T |
author_sort | Ahmed, Sharia M |
collection | PubMed |
description | BACKGROUND: Nearly 150 million children under-5 years of age were stunted in 2020. We aimed to develop a clinical prediction rule (CPR) to identify children likely to experience additional stunting following acute diarrhea, to enable targeted approaches to prevent this irreversible outcome. METHODS: We used clinical and demographic data from the Global Enteric Multicenter Study (GEMS) to build predictive models of linear growth faltering (decrease of ≥0.5 or ≥1.0 in height-for-age z-score [HAZ] at 60-day follow-up) in children ≤59 months presenting with moderate-to-severe diarrhea, and community controls, in Africa and Asia. We screened variables using random forests, and assessed predictive performance with random forest regression and logistic regression using fivefold cross-validation. We used the Etiology, Risk Factors, and Interactions of Enteric Infections and Malnutrition and the Consequences for Child Health and Development (MAL-ED) study to (1) re-derive, and (2) externally validate our GEMS-derived CPR. RESULTS: Of 7639 children in GEMS, 1744 (22.8%) experienced severe growth faltering (≥0.5 decrease in HAZ). In MAL-ED, we analyzed 5683 diarrhea episodes from 1322 children, of which 961 (16.9%) episodes experienced severe growth faltering. Top predictors of growth faltering in GEMS were: age, HAZ at enrollment, respiratory rate, temperature, and number of people living in the household. The maximum area under the curve (AUC) was 0.75 (95% confidence interval [CI]: 0.75, 0.75) with 20 predictors, while 2 predictors yielded an AUC of 0.71 (95% CI: 0.71, 0.72). Results were similar in the MAL-ED re-derivation. A 2-variable CPR derived from children 0–23 months in GEMS had an AUC = 0.63 (95% CI: 0.62, 0.65), and AUC = 0.68 (95% CI: 0.63, 0.74) when externally validated in MAL-ED. CONCLUSIONS: Our findings indicate that use of prediction rules could help identify children at risk of poor outcomes after an episode of diarrheal illness. They may also be generalizable to all children, regardless of diarrhea status. FUNDING: This work was supported by the National Institutes of Health under Ruth L. Kirschstein National Research Service Award NIH T32AI055434 and by the National Institute of Allergy and Infectious Diseases (R01AI135114). |
format | Online Article Text |
id | pubmed-9833824 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | eLife Sciences Publications, Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-98338242023-01-12 Derivation and external validation of clinical prediction rules identifying children at risk of linear growth faltering Ahmed, Sharia M Brintz, Ben J Pavlinac, Patricia B Shahrin, Lubaba Huq, Sayeeda Levine, Adam C Nelson, Eric J Platts-Mills, James A Kotloff, Karen L Leung, Daniel T eLife Epidemiology and Global Health BACKGROUND: Nearly 150 million children under-5 years of age were stunted in 2020. We aimed to develop a clinical prediction rule (CPR) to identify children likely to experience additional stunting following acute diarrhea, to enable targeted approaches to prevent this irreversible outcome. METHODS: We used clinical and demographic data from the Global Enteric Multicenter Study (GEMS) to build predictive models of linear growth faltering (decrease of ≥0.5 or ≥1.0 in height-for-age z-score [HAZ] at 60-day follow-up) in children ≤59 months presenting with moderate-to-severe diarrhea, and community controls, in Africa and Asia. We screened variables using random forests, and assessed predictive performance with random forest regression and logistic regression using fivefold cross-validation. We used the Etiology, Risk Factors, and Interactions of Enteric Infections and Malnutrition and the Consequences for Child Health and Development (MAL-ED) study to (1) re-derive, and (2) externally validate our GEMS-derived CPR. RESULTS: Of 7639 children in GEMS, 1744 (22.8%) experienced severe growth faltering (≥0.5 decrease in HAZ). In MAL-ED, we analyzed 5683 diarrhea episodes from 1322 children, of which 961 (16.9%) episodes experienced severe growth faltering. Top predictors of growth faltering in GEMS were: age, HAZ at enrollment, respiratory rate, temperature, and number of people living in the household. The maximum area under the curve (AUC) was 0.75 (95% confidence interval [CI]: 0.75, 0.75) with 20 predictors, while 2 predictors yielded an AUC of 0.71 (95% CI: 0.71, 0.72). Results were similar in the MAL-ED re-derivation. A 2-variable CPR derived from children 0–23 months in GEMS had an AUC = 0.63 (95% CI: 0.62, 0.65), and AUC = 0.68 (95% CI: 0.63, 0.74) when externally validated in MAL-ED. CONCLUSIONS: Our findings indicate that use of prediction rules could help identify children at risk of poor outcomes after an episode of diarrheal illness. They may also be generalizable to all children, regardless of diarrhea status. FUNDING: This work was supported by the National Institutes of Health under Ruth L. Kirschstein National Research Service Award NIH T32AI055434 and by the National Institute of Allergy and Infectious Diseases (R01AI135114). eLife Sciences Publications, Ltd 2023-01-06 /pmc/articles/PMC9833824/ /pubmed/36607225 http://dx.doi.org/10.7554/eLife.78491 Text en © 2023, Ahmed et al https://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited. |
spellingShingle | Epidemiology and Global Health Ahmed, Sharia M Brintz, Ben J Pavlinac, Patricia B Shahrin, Lubaba Huq, Sayeeda Levine, Adam C Nelson, Eric J Platts-Mills, James A Kotloff, Karen L Leung, Daniel T Derivation and external validation of clinical prediction rules identifying children at risk of linear growth faltering |
title | Derivation and external validation of clinical prediction rules identifying children at risk of linear growth faltering |
title_full | Derivation and external validation of clinical prediction rules identifying children at risk of linear growth faltering |
title_fullStr | Derivation and external validation of clinical prediction rules identifying children at risk of linear growth faltering |
title_full_unstemmed | Derivation and external validation of clinical prediction rules identifying children at risk of linear growth faltering |
title_short | Derivation and external validation of clinical prediction rules identifying children at risk of linear growth faltering |
title_sort | derivation and external validation of clinical prediction rules identifying children at risk of linear growth faltering |
topic | Epidemiology and Global Health |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9833824/ https://www.ncbi.nlm.nih.gov/pubmed/36607225 http://dx.doi.org/10.7554/eLife.78491 |
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