Smart triage: Development of a rapid pediatric triage algorithm for use in low-and-middle income countries

Differentiating mortality risk of individual infants and children to improve survival: opportunity for impact

Children are not born equal in their likelihood of survival. The risk of mortality is highest during and shortly after birth. In the immediate postnatal period and beyond, perinatal events, nutrition, infections, family and environmental exposures, and health services largely determine the risk of death. We argue that current public health programmes do not fully acknowledge this spectrum of risk or respond accordingly. As a result, opportunities to improve the care, survival, and development of children in resource-poor settings are overlooked. Children at high risk of mortality are underidentified and commonly treated using guidelines that do not differentiate care according to the magnitude or drivers of those risks. Children at low risk of mortality are often provided with more intensive care than needed, disproportionately using limited health-care resources with minimal or no benefits. Declines in newborn, infant, and child mortality rates globally are slowing, and further reductions are likely to be incrementally more difficult to achieve once simple, high impact interventions have been universally implemented. Currently, 63 countries have rates of neonatal mortality that are off track to meet the Sustainable Development Goal 2030 target of 12 deaths per 1000 livebirths or less, and 54 countries have rates of mortality in children younger than 5 years that are off track to meet the target of 25 deaths per 1000 livebirths or less. If these targets are to be met, a change of approach is needed to address infant and child mortality and for health-care systems to more efficiently address residual mortality.

Diagnostic Accuracy of the World Health Organization Pediatric Emergency Triage, Assessment and Treatment Tool Plus Among Patients Seeking Care in Nairobi, Kenya: A Cross-sectional Study

INTRODUCTION

The World Health Organization developed Emergency Triage Assessment and Treatment Plus (ETAT+) guidelines to facilitate pediatric care in resource-limited settings. ETAT+ triages patients as nonurgent, priority, or emergency cases, but there is limited research on the performance of ETAT+ regarding patient-oriented outcomes. This study assessed the diagnostic accuracy of ETAT+ in predicting the need for hospital admission in a pediatric emergency unit at Kenyatta National Hospital in Nairobi, Kenya.

METHODS

This was a secondary analysis of a cross-sectional study of pediatric emergency unit patients enrolled over a 4-week period using fixed random sampling. Diagnostic accuracy of ETAT+ was evaluated using receiver operating curves (ROCs) and respective 95% confidence intervals (CIs) with associated sensitivity and specificity (reference category: nonurgent). The ROC analysis was performed for the overall population and stratified by age group.

RESULTS

A total of 323 patients were studied. The most common reasons for presentation were upper respiratory tract disease (32.8%), gastrointestinal disease (15.5%), and lower respiratory tract disease (12.4%). Two hundred twelve participants were triaged as nonurgent (65.6%), 60 as priority (18.6%), and 51 as emergency (15.8%). In the overall study population, the area under the ROC curve was 0.97 (95% CI, 0.95-0.99). The ETAT+ sensitivity was 93.8% (95% CI, 87.0%-99.0%), and the specificity was 82.0% (95% CI, 77.0%-87.0%) for admission of priority group patients. The sensitivity and specificity for the emergency patients were 66.0% (95% CI, 55.0%-77.0%) and 98.0% (95% CI, 97.0%-100.0%), respectively.

CONCLUSIONS

ETAT+ demonstrated diagnostic accuracy for predicting patient need for hospital admission. This finding supports the utility of ETAT+ to inform emergency care practice. Further research on ETAT+ performance in larger populations and additional patient-oriented outcomes would enhance its generalizability and application in resource-limited settings.

Geographical validation of the Smart Triage Model by age group

Infectious diseases in neonates account for half of the under-five mortality in low- and middle-income countries. Data-driven algorithms such as clinical prediction models can be used to efficiently detect critically ill children in order to optimize care and reduce mortality. Thus far, only a handful of prediction models have been externally validated and are limited to neonatal in-hospital mortality. The aim of this study is to externally validate a previously derived clinical prediction model (Smart Triage) using a combined prospective baseline cohort from Uganda and Kenya with a composite endpoint of hospital admission, mortality, and readmission. We evaluated model discrimination using area under the receiver-operator curve (AUROC) and visualized calibration plots with age subsets (< 30 days, ≤ 2 months, ≤ 6 months, and < 5 years). Due to reduced performance in neonates (< 1 month), we re-estimated the intercept and coefficients and selected new thresholds to maximize sensitivity and specificity. 11595 participants under the age of five (under-5) were included in the analysis. The proportion with an endpoint ranged from 8.9% in all children under-5 (including neonates) to 26% in the neonatal subset alone. The model achieved good discrimination for children under-5 with AUROC of 0.81 (95% CI: 0.79-0.82) but poor discrimination for neonates with AUROC of 0.62 (95% CI: 0.55-0.70). Sensitivity at the low-risk thresholds (CI) were 85% (83%-87%) and 68% (58%-76%) for children under-5 and neonates, respectively. After model revision for neonates, we achieved an AUROC of 0.83 (95% CI: 0.79-0.87) with 13% and 41% as the low- and high-risk thresholds, respectively. The updated Smart Triage performs well in its predictive ability across different age groups and can be incorporated into current triage guidelines at local healthcare facilities. Additional validation of the model is indicated, especially for the neonatal model.

External validation of a paediatric Smart triage model for use in resource limited facilities

Models for digital triage of sick children at emergency departments of hospitals in resource poor settings have been developed. However, prior to their adoption, external validation should be performed to ensure their generalizability. We externally validated a previously published nine-predictor paediatric triage model (Smart Triage) developed in Uganda using data from two hospitals in Kenya. Both discrimination and calibration were assessed, and recalibration was performed by optimizing the intercept for classifying patients into emergency, priority, or non-urgent categories based on low-risk and high-risk thresholds. A total of 2539 patients were eligible at Hospital 1 and 2464 at Hospital 2, and 5003 for both hospitals combined; admission rates were 8.9%, 4.5%, and 6.8%, respectively. The model showed good discrimination, with area under the receiver-operator curve (AUC) of 0.826, 0.784 and 0.821, respectively. The pre-calibrated model at a low-risk threshold of 8% achieved a sensitivity of 93% (95% confidence interval, (CI):89%-96%), 81% (CI:74%-88%), and 89% (CI:85%-92%), respectively, and at a high-risk threshold of 40%, the model achieved a specificity of 86% (CI:84%-87%), 96% (CI:95%-97%), and 91% (CI:90%-92%), respectively. Recalibration improved the graphical fit, but new risk thresholds were required to optimize sensitivity and specificity.The Smart Triage model showed good discrimination on external validation but required recalibration to improve the graphical fit of the calibration plot. There was no change in the order of prioritization of patients following recalibration in the respective triage categories. Recalibration required new site-specific risk thresholds that may not be needed if prioritization based on rank is all that is required. The Smart Triage model shows promise for wider application for use in triage for sick children in different settings.

Repeatability of Pulse Oximetry Measurements in Children During Triage in 2 Ugandan Hospitals

BACKGROUND

In low- and middle-income countries, health workers use pulse oximeters for intermittent spot measurements of oxygen saturation (SpO2). However, the accuracy and reliability of pulse oximeters for spot measurements have not been determined. We evaluated the repeatability of spot measurements and the ideal observation time to guide recommendations during spot check measurements.

METHODS

Two 1-minute measurements were taken for the 3,903 subjects enrolled in the study conducted April 2020-January 2022 in Uganda, collecting 1 Hz SpO2 and signal quality index (SQI) data. The repeatability between the 2 measurements was assessed using an intraclass correlation coefficient (ICC), calculated using a median of all seconds of non-zero SpO2 values for each recording (any quality, Q1) and again with a quality filter only using seconds with SQI 90% or higher (good quality, Q2). The ICC was also recalculated for both conditions of Q1 and Q2 using the initial 5 seconds, then the initial 10 seconds, and continuing with 5-second increments up to the full 60 seconds. Lastly, the whole minute ICC was calculated with good quality (Q2), including only records where both measurements had a mean SQI of more than 70% (Q3).

RESULTS

The repeatability ICC with condition Q1 was 0.591 (95% confidence interval [CI]=0.570, 0.611). Using only the first 5 seconds of each measurement reduced the repeatability to 0.200 (95% CI=0.169, 0.230). Filtering with Q2, the whole-minute ICC was 0.855 (95% CI=0.847, 0.864). The ICC did not improve beyond the first 35 seconds. For Q3, the repeatability rose to 0.908 (95% CI=0.901, 0.914).

CONCLUSIONS

Training guidelines must emphasize the importance of signal quality and duration of measurement, targeting a minimum of 35 seconds of adequate-quality, stable data. In addition, the design of new devices should incorporate user prompts and force quality checks to encourage more accurate pulse oximetry measurements.