Derivation and internal validation of a data-driven prediction model to guide frontline health workers in triaging children under-five in Nairobi, Kenya

Abstract

Background: Many hospitalized children in developing countries die from infectious diseases. Early recognition of those who are critically ill coupled with timely treatment can prevent many deaths. A data-driven, electronic triage system to assist frontline health workers in categorizing illness severity is lacking. This study aimed to develop a data-driven parsimonious triage algorithm for children under five years of age. Methods: This was a prospective observational study of children under-five years of age presenting to the outpatient department of Mbagathi Hospital in Nairobi, Kenya between January and June 2018. A study nurse examined participants and recorded history and clinical signs and symptoms using a mobile device with an attached low-cost pulse oximeter sensor. The need for hospital admission was determined independently by the facility clinician and used as the primary outcome in a logistic predictive model. We focused on the selection of variables that could be quickly and easily assessed by low skilled health workers. Results: The admission rate (for more than 24 hours) was 12% (N=138/1,132). We identified an eight-predictor logistic regression model including continuous variables of weight, mid-upper arm circumference, temperature, pulse rate, and transformed oxygen saturation, combined with dichotomous signs of difficulty breathing, lethargy, and inability to drink or breastfeed. This model predicts overnight hospital admission with an area under the receiver operating characteristic curve of 0.88 (95% CI 0.82 to 0.94). Low- and high-risk thresholds of 5% and 25%, respectively were selected to categorize participants into three triage groups for implementation. Conclusion: A logistic regression model comprised of eight easily understood variables may be useful for triage of children under the age of five based on the probability of need for admission. This model could be used by frontline workers with limited skills in assessing children. External validation is needed before adoption in clinical practice.

Keywords: children; developing countries; model; prediction; risk; sepsis; triage.

Figure 1.. Flowchart of study population and distribution of outcomes.

OPD, outpatient department.

Figure 2.. Cross validated receiver operating characteristic curve of the final model in the study cohort.

Points represent low risk (0.05) and high risk (0.25) thresholds. AUC, area under the curve.

Figure 3.. Calibration belt of the final model.

The 45-degree bisector represents the identity between predicted probabilities and observed responses. The 80% and 95% confidence level calibration belt are plotted, in light and dark grey respectively. The test’s p-value, the sample size n, and the polynomial order m of the calibration curve are reported in the top left corner.