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Validating a decision tree for serious infection: diagnostic accuracy in acutely ill children in ambulatory care
Jan Y Verbakel1, Marieke B Lemiengre2, Tine De Burghgraeve3
1Department of Public Health and Primary Care, KU Leuven, Leuven, Belgium Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
Insights
This study validated a clinical prediction rule for identifying children with serious infections in primary care. The tool demonstrated high sensitivity in general practice, effectively ruling out serious conditions.
Area of Science:
- Pediatric Emergency Medicine
- Clinical Decision Support
- Infectious Diseases
Background:
- Acute infections are common in children presenting to primary care.
- Early recognition of serious infections (sepsis, meningitis, pneumonia) is crucial to prevent severe outcomes.
- Clinical prediction rules can aid in diagnosing rare but serious conditions.
Purpose of the Study:
- To validate a recently developed clinical decision tree for identifying serious infections in acutely ill children.
- To assess the diagnostic accuracy of the decision tree in a new population.
Main Methods:
- Diagnostic accuracy study validating a clinical prediction rule.
- Included 8962 acutely ill children in ambulatory care settings (general practice, pediatric outpatient, emergency departments) in Flanders, Belgium.
- Physicians applied the decision tree to all children; outcome was hospital admission for serious infection within 5 days.
Main Results:
- The decision tree showed 100% sensitivity and 83.6% specificity in the general practitioner setting.
- In pediatric outpatient and emergency departments, sensitivities were below 92% with specificities below 44.8%.
- 17% of children tested positive in the general practitioner setting.
Conclusions:
- The clinical prediction rule is highly sensitive for identifying children at risk of hospital admission for serious infection in general practice.
- The rule is suitable for ruling out serious infections in primary care settings.
- Performance varied across different ambulatory care settings.
Objective:
Acute infection is the most common presentation of children in primary care with only few having a serious infection (eg, sepsis, meningitis, pneumonia). To avoid complications or death, early recognition and adequate referral are essential. Clinical prediction rules have the potential to improve diagnostic decision-making for rare but serious conditions. In this study, we aimed to validate a recently developed decision tree in a new but similar population.
Design:
Diagnostic accuracy study validating a clinical prediction rule.
Setting And Participants:
Acutely ill children presenting to ambulatory care in Flanders, Belgium, consisting of general practice and paediatric assessment in outpatient clinics or the emergency department.
Intervention:
Physicians were asked to score the decision tree in every child.
Primary Outcome Measures:
The outcome of interest was hospital admission for at least 24 h with a serious infection within 5 days after initial presentation. We report the diagnostic accuracy of the decision tree in sensitivity, specificity, likelihood ratios and predictive values.
Results:
In total, 8962 acute illness episodes were included, of which 283 lead to admission to hospital with a serious infection. Sensitivity of the decision tree was 100% (95% CI 71.5% to 100%) at a specificity of 83.6% (95% CI 82.3% to 84.9%) in the general practitioner setting with 17% of children testing positive. In the paediatric outpatient and emergency department setting, sensitivities were below 92%, with specificities below 44.8%.
Conclusions:
In an independent validation cohort, this clinical prediction rule has shown to be extremely sensitive to identify children at risk of hospital admission for a serious infection in general practice, making it suitable for ruling out.
Trial Registration Number:
NCT02024282.
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