Respiratory tract infections (RTI) are more commonly caused by viral pathogens in children than in adults. Surprisingly, little is known about antibiotic use in children as compared to adults with RTI. This prospective study aimed to determine antibiotic misuse in children and adults with RTI, using an expert panel reference standard, in order to prioritise the target age population for antibiotic stewardship interventions. We recruited children and adults who presented at the emergency department or were hospitalised with clinical presentation of RTI in The Netherlands and Israel. A panel of three experienced physicians adjudicated a reference standard diagnosis (i.e. bacterial or viral infection) for all the patients using all available clinical and laboratory information, including a 28-day follow-up assessment. The cohort included 284 children and 232 adults with RTI (median age, 1.3 years and 64.5 years, respectively). The proportion of viral infections was larger in children than in adults (209(74%) versus 89(38%),
p
< 0.001). In case of viral RTI, antibiotics were prescribed (i.e. overuse) less frequently in children than in adults (77/209 (37%) versus 74/89 (83%),
p
< 0.001). One (1%) child and three (2%) adults with bacterial infection were not treated with antibiotics (i.e. underuse); all were mild cases. This international, prospective study confirms major antibiotic overuse in patients with RTI. Viral infection is more common in children, but antibiotic overuse is more frequent in adults with viral RTI. Together, these findings support the need for effective interventions to decrease antibiotic overuse in RTI patients of all ages.
Electronic supplementary material
The online version of this article (10.1007/s10096-018-03454-2) contains supplementary material, which is available to authorized users.
Background: Amino-terminal pro-B-type natriuretic peptide (NT-proBNP) level is useful to diagnose or exclude acutely decompensated heart failure (ADHF) in dyspnoeic patients presenting to the emergency department (ED). Aim: To evaluate the impact of ED NT-proBNP testing on admission, length of stay (LOS), discharge diagnosis and longterm outcome. Methods: Dyspnoeic patients were randomized in the ED to NT-proBNP testing. Admission and discharge diagnoses, and outcomes were examined. Results: During 17 months, 470 patients were enrolled and followed for 2.0±1.3 years. ADHF likelihood, determined at study conclusion by validated criteria, established ADHF diagnosis as unlikely in 86 (17%), possible in 120 (24%), and likely in 293 (59%) patients. The respective admission rates in these subgroups were 80, 91, and 96%, regardless of blinding, and 61.9% of blinded vs. 74.5% of unblinded ADHF-likely patients were correctly diagnosed at discharge (p=0.029), with similar LOS. 2-year mortality within subgroups was unaffected by test, but was lower in ADHF-likely patients with NTproBNP levels below median (5000 pg/ml) compared with those above median (p=0.002). Incidence of recurrent cardiac events tracked NT-proBNP levels. Conclusion: ED NT-proBNP testing did not affect admission, LOS, 2-year survival, or recurrent cardiac events among study patients but improved diagnosis at discharge, and allowed risk stratification even within the ADHF-likely group. (ClinicalTrials.gov#NCT00271128)
Background
The ability to accurately distinguish bacterial from viral infection would help clinicians better target antimicrobial therapy during suspected lower respiratory tract infections (LRTI). Although technological developments make it feasible to rapidly generate patient-specific microbiota profiles, evidence is required to show the clinical value of using microbiota data for infection diagnosis. In this study, we investigated whether adding nasal cavity microbiota profiles to readily available clinical information could improve machine learning classifiers to distinguish bacterial from viral infection in patients with LRTI.
Results
Various multi-parametric Random Forests classifiers were evaluated on the clinical and microbiota data of 293 LRTI patients for their prediction accuracies to differentiate bacterial from viral infection. The most predictive variable was C-reactive protein (CRP). We observed a marginal prediction improvement when 7 most prevalent nasal microbiota genera were added to the CRP model. In contrast, adding three clinical variables, absolute neutrophil count, consolidation on X-ray, and age group to the CRP model significantly improved the prediction. The best model correctly predicted 85% of the ‘bacterial’ patients and 82% of the ‘viral’ patients using 13 clinical and 3 nasal cavity microbiota genera (Staphylococcus, Moraxella, and Streptococcus).
Conclusions
We developed high-accuracy multi-parametric machine learning classifiers to differentiate bacterial from viral infections in LRTI patients of various ages. We demonstrated the predictive value of four easy-to-collect clinical variables which facilitate personalized and accurate clinical decision-making. We observed that nasal cavity microbiota correlate with the clinical variables and thus may not add significant value to diagnostic algorithms that aim to differentiate bacterial from viral infections.
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