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

Abstract

Objective: 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.

Design: Prospective observational diagnostic study.

Setting: Three paediatric emergency care units: two in the Netherlands and one in the United Kingdom.

Participants: 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.

Main outcome measures: Pneumonia, other serious bacterial infections (SBIs, including septicaemia/meningitis, urinary tract infections, and others), and no SBIs.

Results: 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).

Conclusion: 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.

Fig 1 Flowchart of derivation populations

Fig 2 Predicted risks of pneumonia and other serious bacterial infections (SBIs) in children with a diagnosis of pneumonia, other SBIs, or no SBI

Fig 3 Positive and negative likelihood ratios plotted for several risk thresholds for predicted risks of pneumonia and other serious bacterial infections (SBIs)

Fig 4 Receiver operating characteristic curve for risk of pneumonia and other serious bacterial infections (SBIs). Sensitivity and specificity of several risk thresholds of the prediction model are plotted

Fig 5 Calibration plot of predicted risks of pneumonia and other serious bacterial infections (SBIs) and observed frequencies (95% confidence interval). Triangles represent mean (predicted versus observed) risk estimates of outcomes by fifths of predicted risk. Dashed diagonal line represents ideal calibration. Distribution of predicted risks of patients with outcome (pneumonia n=59, other SBIs n=65) and other patients (pneumonia n=428, other SBIs n=422) is shown at bottom of graph