Predicting severe pneumonia outcomes in children

Derek J. Williams, Yuwei Zhu, Carlos G. Grijalva, Wesley H. Self, Frank E. Harrell, Carrie Reed, Chris Stockmann, Sandra Arnold, Krow K. Ampofo, Evan J. Anderson, Anna M. Bramley, Richard G. Wunderink, Jonathan Mccullers, Andrew T. Pavia, Seema Jain, Kathryn M. Edwards

Research output: Contribution to journalArticle

11 Citations (Scopus)

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.

Original languageEnglish (US)
Article numbere20161019
JournalPediatrics
Volume138
Issue number4
DOIs
StatePublished - Oct 1 2016

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Pneumonia
Electronic Health Records
Hospitalization
Vital Signs
Critical Care
Artificial Respiration
Shock
Thorax
Prospective Studies
Pediatrics
Morbidity

All Science Journal Classification (ASJC) codes

  • Pediatrics, Perinatology, and Child Health

Cite this

Williams, D. J., Zhu, Y., Grijalva, C. G., Self, W. H., Harrell, F. E., Reed, C., ... Edwards, K. M. (2016). Predicting severe pneumonia outcomes in children. Pediatrics, 138(4), [e20161019]. https://doi.org/10.1542/peds.2016-1019

Predicting severe pneumonia outcomes in children. / Williams, Derek J.; Zhu, Yuwei; Grijalva, Carlos G.; Self, Wesley H.; Harrell, Frank E.; Reed, Carrie; Stockmann, Chris; Arnold, Sandra; Ampofo, Krow K.; Anderson, Evan J.; Bramley, Anna M.; Wunderink, Richard G.; Mccullers, Jonathan; Pavia, Andrew T.; Jain, Seema; Edwards, Kathryn M.

In: Pediatrics, Vol. 138, No. 4, e20161019, 01.10.2016.

Research output: Contribution to journalArticle

Williams, DJ, Zhu, Y, Grijalva, CG, Self, WH, Harrell, FE, Reed, C, Stockmann, C, Arnold, S, Ampofo, KK, Anderson, EJ, Bramley, AM, Wunderink, RG, Mccullers, J, Pavia, AT, Jain, S & Edwards, KM 2016, 'Predicting severe pneumonia outcomes in children', Pediatrics, vol. 138, no. 4, e20161019. https://doi.org/10.1542/peds.2016-1019
Williams DJ, Zhu Y, Grijalva CG, Self WH, Harrell FE, Reed C et al. Predicting severe pneumonia outcomes in children. Pediatrics. 2016 Oct 1;138(4). e20161019. https://doi.org/10.1542/peds.2016-1019
Williams, Derek J. ; Zhu, Yuwei ; Grijalva, Carlos G. ; Self, Wesley H. ; Harrell, Frank E. ; Reed, Carrie ; Stockmann, Chris ; Arnold, Sandra ; Ampofo, Krow K. ; Anderson, Evan J. ; Bramley, Anna M. ; Wunderink, Richard G. ; Mccullers, Jonathan ; Pavia, Andrew T. ; Jain, Seema ; Edwards, Kathryn M. / Predicting severe pneumonia outcomes in children. In: Pediatrics. 2016 ; Vol. 138, No. 4.
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AU - Stockmann, Chris

AU - Arnold, Sandra

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AU - Anderson, Evan J.

AU - Bramley, Anna M.

AU - Wunderink, Richard G.

AU - Mccullers, Jonathan

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AU - Jain, Seema

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N2 - 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.

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