<br /><strong>Background:</strong> Age is an important risk factor among critically ill children with neonates being the most vulnerable. Clinical prediction models need to account for age differences and must be externally validated and updated, if necessary, to enhance reliability, reproducibility, and generalizability. We externally validated the Smart Triage model using a combined prospective baseline cohort from three hospitals in Uganda and two in Kenya using admission, mortality, and readmission.<br/><br /><strong>Methods:</strong> We evaluated model discrimination using area under the receiver-operator curve (AUROC) and visualized calibration plots. In addition, we performed subsetting analysis based on age groups (< 30 days, ≤ 2 months, ≤ 6 months, and < 5 years). We revised the model for neonates (< 1 month) by re-estimating 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.<br/><br /><strong>Results:</strong> The proportion with an outcome 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 0.85 (0.83-0.87) and 0.68 (0.58-0.76) for children under-5 and neonates, respectively. Specificity at the high-risk thresholds were 0.93 (0.93-0.94) and 0.96 (0.94-0.98) for children under-5 and neonates, respectively. After model revision for neonates, we achieved an AUROC of 0.83 (0.79-0.87) with 13% and 41% as the low- and high-risk thresholds, respectively. <br/><br /><strong>Discussion:</strong> The Smart Triage model showed good discrimination for children under-5. However, a revised model is recommended for neonates due to their uniqueness in disease susceptibly, host response, and underlying physiological reserve. External validation of the neonatal model and additional external validation of the under-5 model in different contexts is required.<br/>

<br/><strong>Background:</strong> The reduction in the deaths of millions of children who die from infectious diseases requires early initiation of treatment and improved access to care available in health facilities. A major challenge is the lack of objective evidence to guide front line health workers in the community to recognize critical illness in children earlier in their course. <br><br /><strong>Methods:</strong> We undertook a prospective observational study of children less than 5 years of age presenting at the outpatient or emergency department of a rural tertiary care hospital between October 2012 and April 2013. Study physicians collected clinical signs and symptoms from the facility records, and with a mobile application performed recordings of oxygen saturation, heart rate and respiratory rate. Facility physicians decided the need for hospital admission without knowledge of the oxygen saturation. Multiple logistic predictive models were tested. <br><br /><strong>Findings:</strong> Twenty-five percent of the 3374 assessed children, with a median (interquartile range) age of 1.02 (0.42–2.24), were admitted to hospital. We were unable to contact 20% of subjects after their visit. A logistic regression model using continuous oxygen saturation, respiratory rate, temperature and age combined with dichotomous signs of chest indrawing, lethargy, irritability and symptoms of cough, diarrhea and fast or difficult breathing predicted admission to hospital with an area under the receiver operating characteristic curve of 0.89 (95% confidence interval -CI: 0.87 to 0.90). At a risk threshold of 25% for admission, the sensitivity was 77% (95% CI: 74% to 80%), specificity was 87% (95% CI: 86% to 88%), positive predictive value was 70% (95% CI: 67% to 73%) and negative predictive value was 91% (95% CI: 90% to 92%). <br><br /><strong>Conclusion:</strong> A model using oxygen saturation, respiratory rate and temperature in combination with readily obtained clinical signs and symptoms predicted the need for hospitalization of critically ill children. External validation of this model in a community setting will be required before adoption into clinical practice. <br>