Development and Internal Validation of a Prediction Model to Risk Stratify Children With Suspected Community-Acquired Pneumonia

Background Although community-acquired pneumonia (CAP) is one of the most common infections in children, no tools exist to risk stratify children with suspected CAP. We developed and validated a prediction model to risk stratify and inform hospitalization decisions in children with suspected CAP. Methods We performed a prospective cohort study of children aged 3 months to 18 years with suspected CAP in a pediatric emergency department. Primary outcome was disease severity, defined as mild (discharge home or hospitalization for 24 hours), or severe (intensive care unit stay for >24 hours, septic shock, vasoactive agents, positive-pressure ventilation, chest drainage, extracorporeal membrane oxygenation, or death). Ordinal logistic regression and bootstrapped backwards selection were used to derive and internally validate our model. Results Of 1128 children, 371 (32.9%) developed moderate disease and 48 (4.3%) severe disease. Severity models demonstrated excellent discrimination (optimism-corrected c-indices of 0.81) and outstanding calibration. Severity predictors in the final model included respiratory rate, systolic blood pressure, oxygenation, retractions, capillary refill, atelectasis or pneumonia on chest radiograph, and pleural effusion. Conclusions We derived and internally validated a score that accurately predicts disease severity in children with suspected CAP. Once externally validated, this score has potential to facilitate management decisions by providing individualized risk estimates that can be used in conjunction with clinical judgment to improve the care of children with suspected CAP.