Derivation and external validation of clinical prediction rules identifying children at risk of linear growth faltering
- University of Utah, United States
- University of Washington, United States
- International Centre for Diarrhoeal Disease Research, Bangladesh
- Brown University, United States
- University of Florida, United States
- University of Virginia, United States
- University of Maryland, Baltimore, United States
Abstract
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) study 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 (MSD), 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 5-fold 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 A) re-derive, and B) 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 AUC was 0.75 (95% 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).
Data availability
This submitted manuscript is a secondary analysis of the GEMS and MAL-ED datasets. These data are available to the public through the following website https://clinepidb.org/ce/app/. Data requests are submitted through the website listed, and requests are reviewed and approved by the investigators of those original studies consistent with their protocols and data sharing policies.As of the time of submission of this manuscript, the GEMS Data Access Request asked for purpose, hypothesis/research question, analysis plan, dissemination plan, and if anyone from the GEMS study team had already been approached regarding this request. The MAL-ED data was available for download without submitting a Data Access Request.Data cleaning and statistical code needed to reproduce all parts of this analysis are available from the corresponding author's GitHub page: https://github.com/LeungLab/CPRgrowthfaltering.The following previously published datasets were used:Dataset 1: Gates Enterics Project, Levine MM, Kotloff K, Nataro J, Khan AZA, Saha D, Adegbola FR, Sow S, Alonso P, Breiman R, Sur D, Faruque A. 2018. Study GEMS1 Case Control. https://clinepidb.org/ce/app/record/dataset/DS_841a9f5259#Contacts. Database and Identifier: ClinEpiDB, DS_841a9f5259Dataset 2: The Etiology, Risk Factors, and Interactions of Enteric Infections and Malnutrition and the Consequences for Child Health (MAL-ED). Primary Contact: David Spiro, Fogarty International Center, National Institutes of Health, Bethesda, MD, USA. https://clinepidb.org/ce/app/workspace/analyses/DS_5c41b87221/new/details
Article and author information
Author details
Funding
National Institute of Allergy and Infectious Diseases (R01AI135114)
- Sharia M Ahmed
- Ben J Brintz
- Daniel T Leung
National Institutes of Health (T32AI055434)
- Sharia M Ahmed
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Human subjects: Parents or caregivers of participants provided informed consent, either in writing or witnessed if parents or caregivers were illiterate. The GEMS study protocol was approved by ethical review boards at each field site and the University of Maryland, Baltimore, USA.Parents or caregivers of participants provided informed consent. This analysis utilized publicly available data, see Data Availability statement, and as such is non-human subjects research. The MAL-ED study protocol was approved by ethical review boards at each field site and the Johns Hopkins Institutional Review Board, Baltimore, USA. This analysis utilized publicly available data, see Data Availability statement, and as such is non-human subjects research.
Copyright
© 2023, Ahmed et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Derivation and external validation of clinical prediction rules identifying children at risk of linear growth faltering
eLife 12:e78491.
https://doi.org/10.7554/eLife.78491
