Predicting Severe Pneumonia Outcomes in Children

Abstract

Background: Substantial morbidity and excessive care variation are seen with pediatric pneumonia. Accurate risk-stratification tools to guide clinical decision-making are needed.

Methods: We developed risk models to predict severe pneumonia outcomes in children (<18 years) by using data from the Etiology of Pneumonia in the Community Study, a prospective study of community-acquired pneumonia hospitalizations conducted in 3 US cities from January 2010 to June 2012. In-hospital outcomes were organized into an ordinal severity scale encompassing severe (mechanical ventilation, shock, or death), moderate (intensive care admission only), and mild (non-intensive care hospitalization) outcomes. Twenty predictors, including patient, laboratory, and radiographic characteristics at presentation, were evaluated in 3 models: a full model included all 20 predictors, a reduced model included 10 predictors based on expert consensus, and an electronic health record (EHR) model included 9 predictors typically available as structured data within comprehensive EHRs. Ordinal regression was used for model development. Predictive accuracy was estimated by using discrimination (concordance index).

Results: Among the 2319 included children, 21% had a moderate or severe outcome (14% moderate, 7% severe). Each of the models accurately identified risk for moderate or severe pneumonia (concordance index across models 0.78-0.81). Age, vital signs, chest indrawing, and radiologic infiltrate pattern were the strongest predictors of severity. The reduced and EHR models retained most of the strongest predictors and performed as well as the full model.

Conclusions: We created 3 risk models that accurately estimate risk for severe pneumonia in children. Their use holds the potential to improve care and outcomes.

Conflict of interest statement

POTENTIAL CONFLICT OF INTEREST: The authors have indicated they have no potential conflicts of interest to disclose.

Copyright © 2016 by the American Academy of Pediatrics.

Figures

FIGURE 1

Importance of individual predictors. The importance of each predictor in the full model was calculated as the proportion of explainable outcome variation contributed by each predictor (partial χ2 value for each predictor divided by the model’s total χ2). aPredictor included in the reduced model. bPredictor included in the EHR model.

FIGURE 2

Observed outcomes according to predicted risk for moderate or severe pneumonia, EHR model. Fit curves demonstrating the proportion of children that experienced a mild (black), moderate (green), or severe (blue) outcome at each level of predicted risk for moderate or severe pneumonia. For illustration purposes, the vertical dashed line indicates a 10% predicted risk of moderate or severe pneumonia. At this predicted risk level, 12% of children experienced a moderate (6%) or severe outcome (6%).