RT Journal Article
SR Electronic
T1 Risk score to stratify children with suspected serious bacterial infection: observational cohort study
JF Archives of Disease in Childhood
JO Arch Dis Child
FD BMJ Publishing Group Ltd and Royal College of Paediatrics and Child Health
SP 361
OP 367
DO 10.1136/adc.2010.183111
VO 96
IS 4
A1 Andrew J Brent
A1 Monica Lakhanpaul
A1 Matthew Thompson
A1 Jacqueline Collier
A1 Samiran Ray
A1 Nelly Ninis
A1 Michael Levin
A1 Roddy MacFaul
YR 2011
UL http://adc.bmj.com/content/96/4/361.abstract
AB 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.