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Risk score to stratify children with suspected serious bacterial infection: observational cohort study
Andrew J Brent1, Monica Lakhanpaul, Matthew Thompson
1Working Group on Recognising Acute Illness in Children, Royal College of Paediatrics and Child Health, London, UK. dr.a.brent@gmail.com
Insights
A new clinical score can help identify children with serious bacterial infections (SBI) during acute illness. This tool aids in risk stratification for better clinical decision-making in pediatric emergency care.
Area of Science:
- Pediatric Emergency Medicine
- Infectious Diseases
- Clinical Risk Stratification
Background:
- Acute infections are common in children presenting to emergency departments.
- Accurate risk stratification is crucial for timely management of serious bacterial infections (SBI).
- Existing clinical signs have limitations in sensitivity for diagnosing SBI.
Purpose of the Study:
- To derive and validate a clinical score for risk stratification of children with acute infections.
- To identify clinical predictors of serious bacterial infection (SBI) in pediatric patients.
Main Methods:
- Observational cohort study of 1951 children with suspected infections.
- Prospective data collection on clinical features, investigations, and outcomes.
- Multivariate logistic regression used to derive and validate a clinical risk score for SBI.
Main Results:
- 74 (3.8%) children were diagnosed with SBI.
- A derived clinical score demonstrated reasonable ability to discriminate SBI (AUC 0.77).
- The score effectively risk-stratified children with suspected SBI.
Conclusions:
- A derived clinical score shows potential utility in risk stratifying children with suspected SBI.
- Further validation in diverse settings is recommended.
- Integration into broader management algorithms could enhance clinical decision-making.
Objectives:
To derive and validate a clinical score to risk stratify children presenting with acute infection.
Study Design And Participants:
Observational cohort study of children presenting with suspected infection to an emergency department in England. Detailed data were collected prospectively on presenting clinical features, laboratory investigations and outcome. Clinical predictors of serious bacterial infection (SBI) were explored in multivariate logistic regression models using part of the dataset, each model was then validated in an independent part of the dataset, and the best model was chosen for derivation of a clinical risk score for SBI. The ability of this score to risk stratify children with SBI was then assessed in the entire dataset.
Main Outcome Measure:
Final diagnosis of SBI according to criteria defined by the Royal College of Paediatrics and Child Health working group on Recognising Acute Illness in Children.
Results:
Data from 1951 children were analysed. 74 (3.8%) had SBI. The sensitivity of individual clinical signs was poor, although some were highly specific for SBI. A score was derived with reasonable ability to discriminate SBI (area under the receiver operator characteristics curve 0.77, 95% CI 0.71 to 0.83) and risk stratify children with suspected SBI.
Conclusions:
This study demonstrates the potential utility of a clinical score in risk stratifying children with suspected SBI. Further work should aim to validate the score and its impact on clinical decision making in different settings, and ideally incorporate it into a broader management algorithm including additional investigations to further stratify a child's risk.
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