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Empirically Derived Dehydration Scoring and Decision Tree Models for Children With Diarrhea: Assessment and Internal
Adam C Levine1, Justin Glavis-Bloom2, Payal Modi2
1The Warren Alpert Medical School of Brown University, Providence, RI, USA adam_levine@brown.edu.
Insights
This study developed new clinical tools to accurately diagnose dehydration in children with diarrhea in resource-limited settings. These validated models can improve diarrhea management for frontline providers.
Area of Science:
- Pediatrics
- Global Health
- Clinical Diagnostics
Background:
- Diarrhea is a leading cause of child mortality globally.
- Accurate dehydration assessment is crucial for effective treatment.
- Existing diagnostic models lack validation in resource-limited settings.
Purpose of the Study:
- To develop and validate clinical diagnostic models for dehydration in children.
- To address the need for reliable tools in resource-limited environments.
- To improve the management of acute diarrhea in pediatric populations.
Main Methods:
- Prospective cohort study (DHAKA) in Bangladesh involving children under 5 with acute diarrhea.
- Clinical assessments of dehydration signs and serial weight measurements during rehydration.
- Logistic regression and recursive partitioning models developed to create the DHAKA Dehydration Score and Tree.
- Internal validation using receiver operating characteristic curves (AUC) and bootstrapping.
Main Results:
- The DHAKA Dehydration Score and Tree demonstrated significant accuracy (AUC 0.79 and 0.76) for diagnosing severe dehydration.
- Both models showed significant positive and negative likelihood ratios, indicating diagnostic utility.
- High inter-rater reliability (90% agreement) and good reproducibility were observed.
Conclusions:
- The study successfully derived and internally validated accurate and reliable clinical diagnostic models for dehydration.
- These models represent a novel approach for managing childhood diarrhea in resource-limited settings.
- Further external validation is recommended before widespread clinical adoption.
Introduction:
Diarrhea remains one of the most common and most deadly conditions affecting children worldwide. Accurately assessing dehydration status is critical to determining treatment course, yet no clinical diagnostic models for dehydration have been empirically derived and validated for use in resource-limited settings.
Methods:
In the Dehydration: Assessing Kids Accurately (DHAKA) prospective cohort study, a random sample of children under 5 with acute diarrhea was enrolled between February and June 2014 in Bangladesh. Local nurses assessed children for clinical signs of dehydration on arrival, and then serial weights were obtained as subjects were rehydrated. For each child, the percent weight change with rehydration was used to classify subjects with severe dehydration (>9% weight change), some dehydration (3-9%), or no dehydration (<3%). Clinical variables were then entered into logistic regression and recursive partitioning models to develop the DHAKA Dehydration Score and DHAKA Dehydration Tree, respectively. Models were assessed for their accuracy using the area under their receiver operating characteristic curve (AUC) and for their reliability through repeat clinical exams. Bootstrapping was used to internally validate the models.
Results:
A total of 850 children were enrolled, with 771 included in the final analysis. Of the 771 children included in the analysis, 11% were classified with severe dehydration, 45% with some dehydration, and 44% with no dehydration. Both the DHAKA Dehydration Score and DHAKA Dehydration Tree had significant AUCs of 0.79 (95% CI = 0.74, 0.84) and 0.76 (95% CI = 0.71, 0.80), respectively, for the diagnosis of severe dehydration. Additionally, the DHAKA Dehydration Score and DHAKA Dehydration Tree had significant positive likelihood ratios of 2.0 (95% CI = 1.8, 2.3) and 2.5 (95% CI = 2.1, 2.8), respectively, and significant negative likelihood ratios of 0.23 (95% CI = 0.13, 0.40) and 0.28 (95% CI = 0.18, 0.44), respectively, for the diagnosis of severe dehydration. Both models demonstrated 90% agreement between independent raters and good reproducibility using bootstrapping.
Conclusion:
This study is the first to empirically derive and internally validate accurate and reliable clinical diagnostic models for dehydration in a resource-limited setting. After external validation, frontline providers may use these new tools to better manage acute diarrhea in children.
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