Clinical prediction model to aid emergency doctors managing febrile children at risk of serious bacterial infections: Diagnostic study

Department of General Paediatrics, Erasmus MC-Sophia Children's Hospital, 3015 GJ, Rotterdam, Netherlands.
BMJ (online) (Impact Factor: 17.45). 04/2013; 346(apr02 1):f1706. DOI: 10.1136/bmj.f1706
Source: PubMed

ABSTRACT To derive, cross validate, and externally validate a clinical prediction model that assesses the risks of different serious bacterial infections in children with fever at the emergency department.
Prospective observational diagnostic study.
Three paediatric emergency care units: two in the Netherlands and one in the United Kingdom.
Children with fever, aged 1 month to 15 years, at three paediatric emergency care units: Rotterdam (n=1750) and the Hague (n=967), the Netherlands, and Coventry (n=487), United Kingdom. A prediction model was constructed using multivariable polytomous logistic regression analysis and included the predefined predictor variables age, duration of fever, tachycardia, temperature, tachypnoea, ill appearance, chest wall retractions, prolonged capillary refill time (>3 seconds), oxygen saturation <94%, and C reactive protein.
Pneumonia, other serious bacterial infections (SBIs, including septicaemia/meningitis, urinary tract infections, and others), and no SBIs.
Oxygen saturation <94% and presence of tachypnoea were important predictors of pneumonia. A raised C reactive protein level predicted the presence of both pneumonia and other SBIs, whereas chest wall retractions and oxygen saturation <94% were useful to rule out the presence of other SBIs. Discriminative ability (C statistic) to predict pneumonia was 0.81 (95% confidence interval 0.73 to 0.88); for other SBIs this was even better: 0.86 (0.79 to 0.92). Risk thresholds of 10% or more were useful to identify children with serious bacterial infections; risk thresholds less than 2.5% were useful to rule out the presence of serious bacterial infections. External validation showed good discrimination for the prediction of pneumonia (0.81, 0.69 to 0.93); discriminative ability for the prediction of other SBIs was lower (0.69, 0.53 to 0.86).
A validated prediction model, including clinical signs, symptoms, and C reactive protein level, was useful for estimating the likelihood of pneumonia and other SBIs in children with fever, such as septicaemia/meningitis and urinary tract infections.

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Available from: Ewout W Steyerberg, Sep 29, 2015
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    • "This study showed no correlation between clinical presentation and treatment outcome of severe pneumonia in children. However, duration of fever, rather than mere fever and tachypnea were documented as strong predictors for diagnosis of pneumonia rather than outcome in one study [20]. Another study reported tachypnea and chest indrawing as best predictors of pneumonia severity [21]. "
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    BMC Research Notes 08/2014; 7(1):531. DOI:10.1186/1756-0500-7-531
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    • "In a previous study in our setting (Nademi et al., 2001), bacterial infections were identified in 29% of children, and only history of poor feeding or restlessness was significant predictor. Therefore, combining a prediction model and defined cut-offs with clinical findings could enhance the diagnosis of likely causative pathogens of pneumonia in children (Nijman et al., 2013; Oostenbrink et al., 2013). Discriminant analysis relies on assumption of normality distribution (Spruijt et al., 2013; Zweig and Campbell, 1993). "
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