Derivation and external validation of clinical prediction rules identifying children at risk of linear growth faltering

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    This work would be of interest to global health scientists, particularly in low- and middle-income countries where childhood stunting is an ongoing challenge, and to statisticians interested in building clinical prediction rules. The authors leveraged large, rich datasets from multi-center studies to build and validate predictive models. But by using change in growth, rather than absolute growth, as the only outcome, it may be missing children of concern who are already experiencing growth failure and require intervention but have reached a growth faltering floor.

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Abstract

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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  1. Author Response

    Public Evaluation Summary:

    This work would be of interest to global health scientists, particularly in low- and middleincome countries where childhood stunting is an ongoing challenge, and to statisticians interested in building clinical prediction rules. The authors leveraged large, rich datasets from multi-center studies to build and validate predictive models. But by using change in growth, rather than absolute growth, as the only outcome, it may be missing children of concern who are already experiencing growth failure and require intervention but have reached a growth faltering floor.

    Thank you for this suggestion. We have added additional models for the following predictions: a) growth faltering in those NOT stunted (HAZ≥-2) at presentation, b) any stunting (HAZ<-2) at follow-up, and c) any stunting at …

  2. eLife assessment

    This work would be of interest to global health scientists, particularly in low- and middle-income countries where childhood stunting is an ongoing challenge, and to statisticians interested in building clinical prediction rules. The authors leveraged large, rich datasets from multi-center studies to build and validate predictive models. But by using change in growth, rather than absolute growth, as the only outcome, it may be missing children of concern who are already experiencing growth failure and require intervention but have reached a growth faltering floor.

  3. Reviewer #1 (Public Review):

    In this manuscript, the authors built logistic regression prediction models for linear growth faltering using demographic, socioeconomic, and clinical variables, with the objective of developing a clinical prediction rule that could be applied by healthcare workers to identify and treat high-risk children. A model with 2 variables selected by random forest variable importance performed similarly to a model with 10 variables. Age and HAZ at baseline were selected for the 2-variable model, consistent with existing literature. The authors externally validated the 2-variable model and found similar discriminative ability. Based on typical rule-of-thumb cutoffs, model performance was moderate (AUCs of ~0.65-0.75, depending on model specification); models may still be useful in practice, but this should be further …

  4. Reviewer #2 (Public Review):

    The manuscript documents a thorough and well-validated clinical prediction model for risk of severe child linear growth faltering after diarrheal disease episodes, using data from multiple studies and countries. They identified a parsimonious model of child age and current size with relatively good predictive accuracy. However, I don't believe the prediction rule should be used in it's current form due to the outcome used the danger of missing treating children who require nutritional supplementation.

    The outcome used for prediction in a binary indicatory for a decrease in height-for-age Z-score >= 0.5. A child who fails to gain height by future measurements is of concern, but this outcome also misses children who are already experiencing growth failure, and is vulnerable to regression to the mean effect. …