@article{3029682,
    title = "Development and validation of a prediction model for invasive bacterial
infections in febrile children at European Emergency Departments:
MOFICHE, a prospective observational study",
    author = "Hagedoorn, Nienke N. and Borensztajn, Dorine and Nijman, Ruud Gerard and and Nieboer, Daan and Herberg, Jethro Adam and Balode, Anda and von Both, and Ulrich and Carrol, Enitan and Eleftheriou, Irini and Emonts, Marieke and and van der Flier, Michiel and de Groot, Ronald and Kohlmaier, Benno and and Lim, Emma and Maconochie, Ian and Martinon-Torres, Federico and Pokorn, and Marko and Strle, Franc and Tsolia, Maria and Zavadska, Dace and Zenz, and Werner and Levin, Michael and Vermont, Clementien and Moll, Henriette A.",
    journal = "Archives of Disease in Childhood",
    year = "2021",
    volume = "106",
    number = "7",
    pages = "641-647",
    publisher = "BMJ Publishing Group",
    issn = "0003-9888, 1468-2044",
    doi = "10.1136/archdischild-2020-319794",
    keywords = "epidemiology; therapeutics",
    abstract = "Objectives To develop and cross-validate a multivariable clinical
prediction model to identify invasive bacterial infections (IBI) and to
identify patient groups who might benefit from new biomarkers. Design
Prospective observational study. Setting 12 emergency departments (EDs)
in 8 European countries. Patients Febrile children aged 0-18 years. Main
outcome measures IBI, defined as bacteraemia, meningitis and bone/joint
infection. We derived and cross-validated a model for IBI using
variables from the Feverkidstool (clinical symptoms, C reactive
protein), neurological signs, non-blanching rash and comorbidity. We
assessed discrimination (area under the receiver operating curve) and
diagnostic performance at different risk thresholds for IBI:
sensitivity, specificity, negative and positive likelihood ratios (LRs).
Results Of 16 268 patients, 135 (0.8%) had an IBI. The discriminative
ability of the model was 0.84 (95% CI 0.81 to 0.88) and 0.78 (95% CI
0.74 to 0.82) in pooled cross-validations. The model performed well for
the rule-out threshold of 0.1% (sensitivity 0.97 (95% CI 0.93 to
0.99), negative LR 0.1 (95% CI 0.0 to 0.2) and for the rule-in
threshold of 2.0% (specificity 0.94 (95% CI 0.94 to 0.95), positive LR
8.4 (95% CI 6.9 to 10.0)). The intermediate thresholds of 0.1%-2.0%
performed poorly (ranges: sensitivity 0.59-0.93, negative LR 0.14-0.57,
specificity 0.52-0.88, positive LR 1.9-4.8) and comprised 9784 patients
(60%). Conclusions The rule-out threshold of this model has potential
to reduce antibiotic treatment while the rule-in threshold could be used
to target treatment in febrile children at the ED. In more than half of
patients at intermediate risk, sensitive biomarkers could improve
identification of IBI and potentially reduce unnecessary antibiotic
prescriptions."
}