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Objective: To assess the diagnostic value of existing clinical prediction models (CPM; ie, statistically derived) in febrile young infants at risk for serious bacterial infections.Methods: A systematic literature review identified eight CPMs for predicting serious bacterial infections in febrile children. We validated these CPMs on four validation cohorts of febrile children in Spain (age <3 months), France (age <3 months) and two cohorts in the Netherlands (age 1-3 months and >3-12 months). We evaluated the performance of the CPMs by sensitivity/specificity, area under the receiver operating characteristic curve (AUC) and calibration studies.Results: The original cohorts in which the prediction rules were developed (derivation cohorts) ranged from 381 to 15 781 children, with a prevalence of serious bacterial infections varying from 0.8% to 27% and spanned an age range of 0-16 years. All CPMs originally performed moderately to very well (AUC 0.60-0.93). The four validation cohorts included 159-2204 febrile children, with a median age range of 1.8 (1.2-2.4) months for the three cohorts <3 months and 8.4 (6.0-9.6) months for the cohort >3-12 months of age. The prevalence of serious bacterial infections varied between 15.1% and 17.2% in the three cohorts <3 months and was 9.8% for the cohort >3-12 months of age. Although discriminative values varied greatly, best performance was observed for four CPMs including clinical signs and symptoms, urine dipstick analyses and laboratory markers with AUC ranging from 0.68 to 0.94 in the three cohorts <3 months (ranges sensitivity: 0.48-0.94 and specificity: 0.71-0.97). For the >3-12 months' cohort AUC ranges from 0.80 to 0.89 (ranges sensitivity: 0.70-0.82 and specificity: 0.78-0.90). In general, the specificities exceeded sensitivities in our cohorts, in contrast to derivation cohorts with high sensitivities, although this effect was stronger in infants <3 months than in infants >3-12 months.Conclusion: We identified four CPMs, including clinical signs and symptoms, urine dipstick analysis and laboratory markers, which can aid clinicians in identifying serious bacterial infections. We suggest clinicians should use CPMs as an adjunctive clinical tool when assessing the risk of serious bacterial infections in febrile young infants. [ABSTRACT FROM AUTHOR]

BackgroundCD64 is expressed on the surface membrane of neutrophils (nCD64) in the presence of bacterial infection. Although initial studies in intensive care settings have been promising, only two small, methodologically flawed studies have been performed in feverish children presenting to the emergency departement (ED), both of which were showing a moderate diagnostic value of nCD64 to detect a serious bacterial infection (SBI). This study aimed to determine the diagnostic value of nCD64 in children presenting with fever to the ED for detecting SBI.MethodsIn this prospective observational multi-centre study previously healthy children aged 1 month–16 years with fever, presenting to the ED of two hospitals in the Netherlands in 2011–2012 were included. Standardised information on clinical features were collected and nCD64 and CRP were measured routinely. Multivariable logistic regression was used to determine the discriminative ability to detect SBI (ROC-area) of nCD64 compared with CRP. Diagnostic performance measures including sensitivity, specificity and likelihood ratios were calculated.ResultsIn 392 children (45%) with both CRP and nCD64 determined, 52 children (13%) had an SBI. The AUC of the ROC curve for CD64 was 0.62 (95% CI = 0.54–0.70) and 0.75 (95% CI = 0.67-0.83) for CRP. Neither duration of fever nor deviated vital signs influenced the diagnostic performance of nCD64.ConclusionNCD64 expression has poor discriminative value to detect children with an SBI in a general population of febrile children at the ED. It has no superior value compared to CRP in this setting, neither in total nor in sub-populations. [ABSTRACT FROM AUTHOR]

Background: The limited diagnostic accuracy of biomarkers in children at risk of a serious bacterial infection (SBI) might be due to the imperfect reference standard of SBI. We aimed to evaluate the diagnostic performance of a new classification algorithm for biomarker discovery in children at risk of SBI. Methods: We used data from five previously published, prospective observational biomarker discovery studies, which included patients aged 0- <16 years: the Alder Hey emergency department ( n = 1,120), Alder Hey pediatric intensive care unit ( n = 355), Erasmus emergency department ( n = 1,993), Maasstad emergency department ( n = 714) and St. Mary's hospital ( n = 200) cohorts. Biomarkers including procalcitonin (PCT) (4 cohorts), neutrophil gelatinase-associated lipocalin-2 (NGAL) (3 cohorts) and resistin (2 cohorts) were compared for their ability to classify patients according to current standards (dichotomous classification of SBI vs. non-SBI), vs. a proposed PERFORM classification algorithm that assign patients to one of eleven categories. These categories were based on clinical phenotype, test outcomes and C-reactive protein level and accounted for the uncertainty of final diagnosis in many febrile children. The success of the biomarkers was measured by the Area under the receiver operating Curves (AUCs) when they were used individually or in combination. Results: Using the new PERFORM classification system, patients with clinically confident bacterial diagnosis ("definite bacterial" category) had significantly higher levels of PCT, NGAL and resistin compared with those with a clinically confident viral diagnosis ("definite viral" category). Patients with diagnostic uncertainty had biomarker concentrations that varied across the spectrum. AUCs were higher for classification of "definite bacterial" vs. "definite viral" following the PERFORM algorithm than using the "SBI" vs. "non-SBI" classification; summary AUC for PCT was 0.77 (95% CI 0.72-0.82) vs. 0.70 (95% CI 0.65-0.75); for NGAL this was 0.80 (95% CI 0.69-0.91) vs. 0.70 (95% CI 0.58-0.81); for resistin this was 0.68 (95% CI 0.61-0.75) vs. 0.64 (0.58-0.69) The three biomarkers combined had summary AUC of 0.83 (0.77-0.89) for "definite bacterial" vs. "definite viral" infections and 0.71 (0.67-0.74) for "SBI" vs. "non-SBI." Conclusion: Biomarkers of bacterial infection were strongly associated with the diagnostic categories using the PERFORM classification system in five independent cohorts. Our proposed algorithm provides a novel framework for phenotyping children with suspected or confirmed infection for future biomarker studies., Competing Interests: CF is affiliated with Micropathology Ltd., an Independent Rapid Diagnosis & Biomedical Research Company. Micropathology Ltd. provides a clinically supported service for the rapid diagnosis and management of infectious and genetic disease. It is a formal partner of the PERFORM research consortium. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Nijman, Oostenbrink, Moll, Casals-Pascual, von Both, Cunnington, De, Eleftheriou, Emonts, Fink, van der Flier, de Groot, Kaforou, Kohlmaier, Kuijpers, Lim, Maconochie, Paulus, Martinon-Torres, Pokorn, Romaine, Calle, Schlapbach, Smit, Tsolia, Usuf, Wright, Yeung, Zavadska, Zenz, Levin, Herberg, Carrol and the PERFORM consortium (Personalized Risk assessment in febrile children to optimize Real-life Management across the European Union).)