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Updated: Aug 15, 2025
Assessment of Child Anthropometry in a Large Epidemiologic Study
Published on: February 2, 2017
Derivation and external validation of clinical prediction rules identifying children at risk of linear growth
Sharia M Ahmed1, Ben J Brintz2, Patricia B Pavlinac3
1Division of Infectious Diseases, University of Utah School of Medicine, Salt lake City, United States.
Insights
This study developed a clinical prediction rule (CPR) to identify children at risk of stunting after diarrhea. The CPR can help target interventions to prevent irreversible growth faltering in young children.
Area of Science:
- Pediatric gastroenterology
- Global child health
- Nutritional epidemiology
Background:
- Childhood stunting affects nearly 150 million children under-5 globally.
- Acute diarrhea is a significant risk factor for irreversible stunting in young children.
- Targeted interventions are needed to prevent stunting post-diarrhea.
Purpose of the Study:
- To develop a clinical prediction rule (CPR) to identify children at high risk of stunting after acute diarrhea.
- To enable early, targeted interventions for preventing stunting in vulnerable children.
Main Methods:
- Utilized data from the Global Enteric Multicenter Study (GEMS) and the Etiology, Risk Factors, and Interactions of Enteric Infections and Malnutrition (MAL-ED) study.
- Employed random forests for variable screening and random forest/logistic regression for predictive modeling with cross-validation.
- Developed and validated a CPR for linear growth faltering (decrease in height-for-age z-score) in children under 5.
Main Results:
- Identified key predictors of growth faltering including age, HAZ at enrollment, respiratory rate, temperature, and household size.
- A 20-predictor model achieved an Area Under the Curve (AUC) of 0.75; a 2-predictor model yielded an AUC of 0.71.
- The 2-variable CPR for children 0-23 months showed AUCs of 0.63 (GEMS) and 0.68 (MAL-ED validation).
Conclusions:
- Clinical prediction rules can effectively identify children at risk of poor outcomes following diarrheal illness.
- These prediction rules may be generalizable to all children, irrespective of diarrhea status.
- The findings support the development of targeted strategies to mitigate stunting in at-risk populations.
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).
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