Validation of Childhood Pneumonia Prognostic Models for Use in Emergency Care Settings

Abstract

BACKGROUND Unwarranted variation in disposition decisions exist among children with pneumonia. We validated three prognostic models for predicting pneumonia severity among children in the emergency department (ED) and hospital. METHODS We performed a two-center, prospective study of children 6 months to <18 years presenting to the ED with pneumonia from January 2014 to May 2019. We evaluated three previously developed disease-specific prognostic models which use demographic, clinical, and diagnostic predictor variables, with each model estimating risk for Very Severe (mechanical ventilation or shock), Severe (ICU without very severe features), and Moderate/Mild (Hospitalization without severe features or ED discharge) pneumonia. Predictive accuracy was measured using discrimination (concordance or c-statistic) and re-calibration. RESULTS There were 1088 children included in one or more of the three models. Median age was 3.6 years and the majority of children were male (53.7%) and identified as non-Hispanic White (63.7%). The distribution for the ordinal severity outcome was mild or moderate (79.1%), severe (15.9%), and very severe (4.9%). The three models each demonstrated excellent discrimination (C-statistic range across models (0.786-0.803) with no appreciable degradation in predictive accuracy from the derivation cohort. CONCLUSION All three prognostic models accurately identified risk for three clinically meaningful levels of pneumonia severity and demonstrated very good predictive performance. Physiologic variables contributed the most to model prediction. Application of these objective tools may help standardize and improve disposition and other management decisions for children with pneumonia.

User-Centered Design and Implementation of an Interoperable FHIR Application for Pediatric Pneumonia Prognostication in a Randomized Trial

“…The previously described multivariable proportional odds logistic regression model uses age, sex, race, temperature, heart rate, respiratory rate, systolic blood pressure, and ratio of partial pressure of arterial oxygen to fraction of inspired oxygen (PaO 2 :FiO 2 ) as predictors, applying restricted cubic splines for continuous variables to relax linearity assumptions and interaction terms to account for age-dependent vital sign norms. 2 When true PaO 2 and FiO 2 measurements were unavailable, we estimated them using oxygen flow rates and arterial oxygen saturation. 10 11 12 13 The model predicts three severity classes based on the most severe outcome experienced during the encounter: very severe represents respiratory failure requiring invasive mechanical ventilation, shock requiring vasopressors, or death; severe represents other intensive care units (ICU)-level care; and mild–moderate represents all other admitted or discharged children not requiring ICU care.…”

“…Our team previously validated the model, which demonstrated very good discrimination and calibration in the ED setting. 2 14…”

“…Working toward that end, our team previously developed and validated a proportional odds ordinal logistic regression model using electronic health record (EHR) data to predict pneumonia severity in hospitalized children. 1 2…”