• Community-acquired pneumonia (CAP) remains a leading cause of pediatric hospitalization and mortality worldwide. Relying solely on clinician judgment can lead to both unnecessary admissions and missed high-risk cases. To address this gap, the Pediatric Emergency Research Network (PERN) developed two predictive models using data from 2,200 children (3 months–14 years) across 73 emergency departments in 14 countries.

• The clinical model, based on bedside signs and symptoms within 4 hours to 7 days of presentation, and an enhanced radiograph model, incorporating the number of lung regions affected on chest X-ray, both demonstrated “good to excellent” discrimination compared with unaided clinician assessment. 

• Key predictors of progression to moderate or severe CAP included:

  • Hypoxemia (SpO₂ 90–92%): adjusted odds ratio (aOR) ≈ 3.24

  • Chest retractions: aOR ≈ 2.86

  • Abdominal pain, refusal to drink, tachycardia/tachypnea (> 95th percentile), and prior antibiotic use

  • Conversely, runny nose or nasal congestion was protective (aOR 0.59), identifying low-risk children.

• Among 1,900 children with complete data, 53–58% had mild CAP, 36–41% moderate, and 5–6% severe. Lead author Dr. Todd Florin emphasized that early identification of children at risk enables prompt aggressive treatment and more efficient resource allocation.

• Next steps include external validation in diverse settings, integration into electronic health records or mobile apps, and implementation studies to assess impacts on admission rates, resource utilization, and patient outcomes. Once validated, these models could be incorporated into pediatric pneumonia guidelines, standardizing care and improving safety for children globally.

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