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.

Figure 1.

Summary performance measures of the quick Pediatric Logistic Organ Dysfunction-2 score. Top left: Discrimination (area under the receiver operating characteristic curve, 0.75; 95% CI, 0.70–0.80). Top right: Calibration. Proportion of admissions at each level of the score that died during their PICU stay; error bars indicate Wilson 95% CIs. Bottom left: Negative (black line) and positive (gray line) likelihood ratios at different cutoffs, illustrated on a log₁₀ scale. Bottom right: Sensitivity (black line) and specificity (gray line) at different cutoffs; gray shaded ribbons indicate 95% CIs. NLR = negative likelihood ratio, PLR = positive likelihood ratio.

Figure 2.

Discrimination and calibration of the new model. A, Discrimination of the new model. Perfect discrimination is indicated by an area under the receiver operating characteristic curve (AUC) of 1.0. B, Calibration of the new model. Dashed line indicates perfect calibration. Solid line indicates calibration of the model, with 95% CI (gray ribbon). Rug plots indicate distribution of predicted risks for participants who did (top) and did not (bottom) meet the primary outcome.

Figure 3.

Clinical utility of the new model across a range of plausible decision thresholds. A cutoff (decision threshold or threshold probability) of 10% reflects a triage strategy whereby all admissions with a predicted probability of death greater than or equal to 10% are directed to a high-acuity area and all other admissions managed on the main unit. The net benefit of the new model (dotted line) is compared with “Treat All” (solid line; all PICU admissions are triaged to the high-acuity area) and “Treat None” (shortdash; no PICU admissions are triaged to the high-acuity area) strategies, as well as the three existing scores that demonstrated potential for stratifying admissions into low- and high-risk groups from the external validation (Pediatric Advanced Warning Score [PAWS] = dot-shortdash; quick Sequential Organ Failure Assessment [qSOFA] = longdash; quick Pediatric Logistic Organ Dysfunction-2 [qPELOD-2] = dot-longdash). Above a cutoff of 7.5% using the new model to triage admissions appears to be the optimal strategy. A color version of the figure is provided (Appendix 20, http://links.lww.com/PCC/C445) for additional clarity.