Derivation of a prognostic model for critically ill children in locations with limited resources

BackgroundCapacity and demand for paediatric critical care are growing in many resource-constrained contexts. However, tools to support resource stewardship and promote sustainability of critical care services are lacking. MethodsThis study assessed the ability of nine severity scores to risk stratify children admitted to a paediatric intensive care unit (PICU) in Siem Reap, northern Cambodia. It then developed a bespoke clinical prediction model to enable risk stratification in resource-constrained PICU contexts. The primary outcome was death during PICU admission. Results1,550 consecutive PICU admissions were included, of which 97 (6.3%) died. Most existing severity scores achieved comparable discrimination (area under the receiver operating characteristic curves [AUCs] 0.71-0.76) but only three scores demonstrated moderate diagnostic utility for triaging admissions into high- and low-risk groups (positive likelihood ratios 2.65-2.97 and negative likelihood ratios 0.40-0.46). The newly derived model outperformed all existing severity scores (AUC 0.84, 95% CI 0.80-0.88; p < 0.001). Using one particular threshold, the model classified 13.0% of admissions as high-risk, amongst which probability of mortality was almost ten-fold greater than admissions triaged as low-risk (PLR 5.75; 95% CI 4.57-7.23 and NLR 0.47; 95% CI 0.37-0.59). Decision curve analyses indicated that the model would be superior to all existing severity scores and could provide utility across the range of clinically plausible decision thresholds. ConclusionsExisting paediatric severity scores have limited potential as risk stratification tools in resource-constrained PICUs. If validated, the prediction model developed herein would provide a readily implementable mechanism to support triage of critically ill children on admission to PICU and could be tailored to suit a variety of contexts where resource prioritisation is important.