Risk score to stratify children with suspected serious bacterial infection: observational cohort study

Abstract

ObjectivesTo derive and validate a clinical score to risk stratify children presenting with acute infection.Study design and participantsObservational 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 measureFinal diagnosis of SBI according to criteria defined by the Royal College of Paediatrics and Child Health working group on Recognising Acute Illness in Children.ResultsData 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.ConclusionsThis 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.

Systematic review and validation of prediction rules for identifying children with serious infections in emergency departments and urgent-access primary care.

“…For the YOS, three data sets had variables which were identical to the original Yale scoring (Berger et al, Brent et al 79 and Thompson et al…”

“…11,28,29,60,72,79 Two had been collected in the UK, 29,79 four were from the Netherlands and one was from Belgium, 11 providing data on a total of 11,045 children. Two data sets had been collected from primary care, 11 the remainder from ED settings.…”

“…), and an additional six data sets were suitable for validation (Berger et al, Roukema et al, 28 Brent et al, 79 Bleeker et al, 50 Thompson et al 29 and Oostenbrink et al…”

“…Different cut-offs (8 and 10) were used to validate the YOS, which was consistent with the cut-offs identified by the systematic review. Four data sets were available to validate the YOS using a cut-off of 10 (Van den Bruel et al, 11 Berger et al, Thompson et al 29 and Brent et al 79 ), and an additional data set when using a cut-off of 8 (Oostenbrink et al 72 ). One of the data sets had been used to derive the five-stage decision tree (Van den Bruel et al…”

Use of alarm features in referral of febrile children to the emergency department: an observational study

“…Clinical prediction rules may, alongside guidelines, help physicians to identify children at low risk of disease. [19][20][21][22][23][24][25] The only clinical prediction rule developed for primary care specifically showed a promising high sensitivity and low negative likelihood ratio at derivation; 6 however, it lacked generalisability on external validation in other low-prevalence populations. 17 In addition, another study has shown that other clinical prediction rules developed for hospital emergency care were of limited use in the primary outof-hours care setting as well.…”