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Can clinical prediction models assess antibiotic need in childhood pneumonia? A validation study in paediatric
Josephine van de Maat1, Daan Nieboer2, Matthew Thompson3
1Department of General Paediatrics, Erasmus MC-Sophia Children's Hospital, Rotterdam, The Netherlands.
Insights
Three clinical prediction models show potential for diagnosing childhood pneumonia and guiding antibiotic use in emergency departments. Further research is needed to refine their implementation and threshold selection for optimal patient care.
Area of Science:
- Pediatrics
- Infectious Diseases
- Clinical Epidemiology
Background:
- Pneumonia is a leading cause of pediatric emergency department visits.
- Existing clinical prediction models for childhood pneumonia often rely on chest x-rays, which have diagnostic limitations.
- There is a need to validate these models for clinical diagnosis and antibiotic stewardship.
Purpose of the Study:
- To validate existing clinical prediction models for childhood pneumonia using a clinical reference standard.
- To assess the performance of these models in guiding antibiotic treatment decisions in the emergency department.
- To evaluate the models' ability to differentiate between bacterial and viral infections in children.
Main Methods:
- Systematic identification and quality assessment of clinical prediction models for childhood pneumonia.
- Validation of selected models in two independent pediatric emergency department populations (Rotterdam and Coventry).
- Performance measurement using the ordinal c-statistic (ORC) against a clinical reference standard for infection type.
Main Results:
- Seven of eight identified models were evaluated, showing good original performance.
- Three models demonstrated fair discrimination between bacterial and viral infections (ORC ≥0.55).
- These models identified children at low risk for bacterial infection, but require biomarker measurements.
Conclusions:
- Three clinical prediction models show promise for differentiating bacterial from viral pneumonia in children.
- Implementation challenges include the need for biomarkers and defining optimal thresholds for antibiotic prescription.
- Careful consideration of benefits and harms is crucial when using these models to guide treatment.
Objectives:
Pneumonia is the most common bacterial infection in children at the emergency department (ED). Clinical prediction models for childhood pneumonia have been developed (using chest x-ray as their reference standard), but without implementation in clinical practice. Given current insights in the diagnostic limitations of chest x-ray, this study aims to validate these prediction models for a clinical diagnosis of pneumonia, and to explore their potential to guide decisions on antibiotic treatment at the ED.
Methods:
We systematically identified clinical prediction models for childhood pneumonia and assessed their quality. We evaluated the validity of these models in two populations, using a clinical reference standard (1. definite/probable bacterial, 2. bacterial syndrome, 3. unknown bacterial/viral, 4. viral syndrome, 5. definite/probable viral), measuring performance by the ordinal c-statistic (ORC). Validation populations included prospectively collected data of children aged 1 month to 5 years attending the ED of Rotterdam (2012-2013) or Coventry (2005-2006) with fever and cough or dyspnoea.
Results:
We identified eight prediction models and could evaluate the validity of seven, with original good performance. In the Dutch population 22/248 (9%) had a bacterial infection, in Coventry 53/301 (17%), antibiotic prescription was 21% and 35% respectively. Three models predicted a higher risk in children with bacterial infections than in those with viral disease (ORC ≥0.55) and could identify children at low risk of bacterial infection.
Conclusions:
Three clinical prediction models for childhood pneumonia could discriminate fairly well between a clinical reference standard of bacterial versus viral infection. However, they all require the measurement of biomarkers, raising questions on the exact target population when implementing these models in clinical practice. Moreover, choosing optimal thresholds to guide antibiotic prescription is challenging and requires careful consideration of potential harms and benefits.
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