A Prognostic Model for Critically Ill Children in Locations With Emerging Critical Care Capacity

Abstract

OBJECTIVES: To develop a clinical prediction model to risk stratify children admitted to PICUs in locations with limited resources, and compare performance of the model to nine existing pediatric severity scores. DESIGN: Retrospective, single-center, cohort study. SETTING: PICU of a pediatric hospital in Siem Reap, northern Cambodia. PATIENTS: Children between 28 days and 16 years old admitted nonelectively to the PICU. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Clinical and laboratory data recorded at the time of PICU admission were collected. The primary outcome was death during PICU admission. One thousand five hundred fifty consecutive non-elective PICU admissions were included, of which 97 died (6.3%). 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 [PLRs], 2.65-2.97 and negative likelihood ratios [NLRs], 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, among 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. CONCLUSIONS: Existing pediatric severity scores have limited potential as risk stratification tools in resource-constrained PICUs. If validated, our prediction model would be a readily implementable mechanism to support triage of critically ill children at admission to PICU and could provide value across a variety of contexts where resource prioritization is important.