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Predicting Severe Pneumonia Outcomes in Children.

Williams DJ, Zhu Y, Grijalva CG, Self WH, Harrell FE, Reed C, Stockmann C, Arnold SR, Ampofo KK, Anderson EJ, Bramley AM, Wunderink RG, McCullers JA, Pavia AT, Jain S, Edwards KM
Pediatrics. 2016 138 (4)

PMID: 27688362 · PMCID: PMC5051209 · DOI:10.1542/peds.2016-1019

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.

Copyright © 2016 by the American Academy of Pediatrics.

MeSH Terms (23)

Age Factors Child, Preschool Community-Acquired Infections Female Hospitalization Humans Infant Intensive Care Units, Pediatric Length of Stay Lung Male Models, Statistical Patient Admission Patient Outcome Assessment Pneumonia Prognosis Prospective Studies Respiration, Artificial Risk Assessment Severity of Illness Index Shock United States Vital Signs

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