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 nonelective 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.