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Smart triage: Development of a rapid pediatric triage algorithm for use in low-and-middle income countries
Alishah Mawji1,2, Edmond Li1, Dustin Dunsmuir1,2
1Department of Anesthesiology, Pharmacology & Therapeutics, University of British Columbia, Vancouver, BC, Canada.
Insights
A new nine-variable triage model accurately identifies critically ill children in low-resource settings. This digital tool aids rapid assessment and improves healthcare delivery for pediatric patients at risk of severe illness.
Area of Science:
- Pediatric Emergency Medicine
- Global Health
- Clinical Decision Support
Background:
- Early recognition of critically ill children is vital for improved outcomes and resource allocation.
- Digital triage tools can enhance healthcare delivery in resource-limited settings.
- A model for rapid identification of critically ill children at triage was needed.
Purpose of the Study:
- To develop and validate a predictive model for identifying critically ill children at triage in a low-income country.
- To create a digital triage tool to support clinical decision-making in emergency departments.
Main Methods:
- Prospective cohort study of acutely ill children in Uganda.
- Logistic regression model developed using bootstrap stepwise regression.
- Performance assessed via ROC analysis and cross-validation on a held-out test set.
Main Results:
- A nine-predictor triage model was derived, including age, heart rate, temperature, oxygen saturation, and clinical signs like parent concern and difficulty breathing.
- The model demonstrated good discrimination, calibration, and risk stratification at thresholds of 8% and 40%.
- The model achieved high sensitivity and specificity for predicting hospital admission.
Conclusions:
- A nine-variable triage model was successfully developed for pediatric populations in low-income settings.
- The model demonstrates potential for integration into digital platforms for rapid identification of critically ill children.
- Further external validation and clinical implementation are ongoing.
Introduction:
Early and accurate recognition of children at risk of progressing to critical illness could contribute to improved patient outcomes and resource allocation. In resource limited settings digital triage tools can support decision making and improve healthcare delivery. We developed a model for rapid identification of critically ill children at triage.
Methods:
This was a prospective cohort study of acutely ill children presenting at Jinja Regional Referral Hospital in Eastern Uganda. Variables collected in the emergency department informed the development of a logistic model based on hospital admission using bootstrap stepwise regression. Low and high-risk thresholds for 90% minimum sensitivity and specificity, respectively generated three risk level categories. Performance was assessed using receiver operating characteristic curve analysis on a held-out test set generated by an 80:20 split with 10-fold cross validation. A risk stratification table informed clinical interpretation.
Results:
The model derivation cohort included 1,612 participants, with an admission rate of approximately 23%. The majority of admitted patients were under five years old and presenting with sepsis, malaria, or pneumonia. A 9-predictor triage model was derived: logit (p) = -32.888 + (0.252, square root of age) + (0.016, heart rate) + (0.819, temperature) + (-0.022, mid-upper arm circumference) + (0.048 transformed oxygen saturation) + (1.793, parent concern) + (1.012, difficulty breathing) + (1.814, oedema) + (1.506, pallor). The model afforded good discrimination, calibration, and risk stratification at the selected thresholds of 8% and 40%.
Conclusion:
In a low income, pediatric population, we developed a nine variable triage model with high sensitivity and specificity to predict who should be admitted. The triage model can be integrated into any digital platform and used with minimal training to guide rapid identification of critically ill children at first contact. External validation and clinical implementation are in progress.
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