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A Prognostic Model for Critically Ill Children in Locations With Emerging Critical Care Capacity
Arjun Chandna1,2, Suy Keang1,3, Meas Vorlark3
1Cambodia Oxford Medical Research Unit, Angkor Hospital for Children, Siem Reap, Cambodia.
Summary
A new clinical prediction model effectively risk stratifies children in resource-limited pediatric intensive care units (PICUs), outperforming existing scores. This tool aids in prioritizing care for critically ill children where resources are scarce.
Area of Science:
- Pediatric Critical Care Medicine
- Clinical Prediction Modeling
- Global Health
Background:
- Pediatric intensive care units (PICUs) in resource-limited settings face challenges in accurately assessing patient severity.
- Existing pediatric severity scores may have limited utility for risk stratification in these specific environments.
Purpose of the Study:
- To develop and validate a novel clinical prediction model for risk stratification of children admitted to PICUs in resource-constrained locations.
- To compare the performance of the new model against nine established pediatric severity scores.
Main Methods:
- Retrospective, single-center cohort study including 1,550 nonelective PICU admissions.
- Clinical and laboratory data at admission were collected.
- Primary outcome was PICU mortality; performance was assessed using discrimination (AUC) and diagnostic utility (PLR, NLR).
Main Results:
- The newly developed model demonstrated superior discrimination (AUC, 0.84) compared to existing scores (AUC, 0.71-0.76).
- The model identified a high-risk group with nearly a ten-fold increased mortality probability (PLR, 5.75).
- Decision curve analysis indicated the model's superiority and utility across various clinical thresholds.
Conclusions:
- Current pediatric severity scores have limited effectiveness in resource-constrained PICUs.
- The developed prediction model offers a potentially valuable and implementable tool for triage and resource prioritization.
- Further validation is recommended to support its widespread use in diverse settings.
