ABSTRACT
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.
ABSTRACT
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.
ABSTRACT
OBJECTIVES: To assess the status and change in self-rated health among Aussiedler, ethnic German immigrants from the former Soviet Union, as a predictor for premature death 10 years after first assessment. Moreover, to identify subgroups which are particular at risk of anticipated severe health impairment. DESIGN: Cross-sectional questionnaire. SETTING: The study was conducted in the catchment area of Augsburg, a city in southern Bavaria, Germany, in 2011/2012 that has a large community of Aussiedler. PARTICIPANTS: 595 Aussiedler (231 male, 364 female, mean age 55 years) who in majority migrated to Germany between 1990 and 1999. OUTCOME: Primary outcome: self-rated health (very good/good/not so good/bad) and its association with demographic, social and morbidity related variables. METHODS: Self-rated health was dichotomised as 'very good' and 'good' versus 'not so good' and 'bad'. Multivariable logistic models were created. Missing values with regard to pain were addressed by a second analysis. RESULTS: Although low response suggests a healthier sample, the findings are alarming. Altogether47% of the Aussiedler perceived their health as less than good, which is worse compared with the first assessment in 2000 (25% compared with 20% of the general public). Prevalence of high blood pressure was present in 52% of Aussiedler, 34.5% were obese, 40.7% suffered from frequent pain and 13.1% had diabetes mellitus. According to the multivariable models, individuals suffering from pain, limited mobility, diabetes mellitus and high blood pressure are particularly in jeopardy. CONCLUSIONS: 10 years after the first assessment of self-rated health among Aussiedler their situation deteriorated. Tailored risk factor counselling of general practitioners is highly recommended.
