Variation in antibiotic prescription rates in febrile children presenting to emergency departments across Europe (MOFICHE) : A multicentre observational study

Funding Information: This project has received funding from the European Union?s Horizon 2020 research and innovation programme under grant agreement No. 668303. The Research was supported by the National Institute for Health Research Biomedical Research Centres at Imperial College London, Newcastle Hospitals NHS Foundation Trust and Newcastle University. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. For the remaining authors no sources of funding were declared. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We acknowledge all research nurses for their help in collecting data, and Anda Nagle (Riga) and the Institute of Microbiology at University Medical Centre Ljubljana for their help in collecting data on antimicrobial resistance. Members of the PERFORM consortium are listed in S11 Text. Publisher Copyright: Copyright: © 2020 Hagedoorn et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.Background The prescription rate of antibiotics is high for febrile children visiting the emergency department (ED), contributing to antimicrobial resistance. Large studies at European EDs covering diversity in antibiotic and broad-spectrum prescriptions in all febrile children are lacking. A better understanding of variability in antibiotic prescriptions in EDs and its relation with viral or bacterial disease is essential for the development and implementation of interventions to optimise antibiotic use. As part of the PERFORM (Personalised Risk assessment in Febrile illness to Optimise Real-life Management across the European Union) project, the MOFICHE (Management and Outcome of Fever in Children in Europe) study aims to investigate variation and appropriateness of antibiotic prescription in febrile children visiting EDs in Europe. Methods and findings Between January 2017 and April 2018, data were prospectively collected on febrile children aged 0–18 years presenting to 12 EDs in 8 European countries (Austria, Germany, Greece, Latvia, the Netherlands [n = 3], Spain, Slovenia, United Kingdom [n = 3]). These EDs were based in university hospitals (n = 9) or large teaching hospitals (n = 3). Main outcomes were (1) antibiotic prescription rate; (2) the proportion of antibiotics that were broad-spectrum antibiotics; (3) the proportion of antibiotics of appropriate indication (presumed bacterial), inappropriate indication (presumed viral), or inconclusive indication (unknown bacterial/viral or other); (4) the proportion of oral antibiotics of inappropriate duration; and (5) the proportion of antibiotics that were guideline-concordant in uncomplicated urinary and upper and lower respiratory tract infections (RTIs). We determined variation of antibiotic prescription and broad-spectrum prescription by calculating standardised prescription rates using multilevel logistic regression and adjusted for general characteristics (e.g., age, sex, comorbidity, referral), disease severity (e.g., triage level, fever duration, presence of alarming signs), use and result of diagnostics, and focus and cause of infection. In this analysis of 35,650 children (median age 2.8 years, 55% male), overall antibiotic prescription rate was 31.9% (range across EDs: 22.4%–41.6%), and among those prescriptions, the broad-spectrum antibiotic prescription rate was 52.1% (range across EDs: 33.0%–90.3%). After standardisation, differences in antibiotic prescriptions ranged from 0.8 to 1.4, and the ratio between broad-spectrum and narrow-spectrum prescriptions ranged from 0.7 to 1.8 across EDs. Standardised antibiotic prescription rates varied for presumed bacterial infections (0.9 to 1.1), presumed viral infections (0.1 to 3.3), and infections of unknown cause (0.1 to 1.8). In all febrile children, antibiotic prescriptions were appropriate in 65.0% of prescriptions, inappropriate in 12.5% (range across EDs: 0.6%–29.3%), and inconclusive in 22.5% (range across EDs: 0.4%–60.8%). Prescriptions were of inappropriate duration in 20% of oral prescriptions (range across EDs: 4.4%–59.0%). Oral prescriptions were not concordant with the local guideline in 22.3% (range across EDs: 11.8%–47.3%) of prescriptions in uncomplicated RTIs and in 45.1% (range across EDs: 11.1%–100%) of prescriptions in uncomplicated urinary tract infections. A limitation of our study is that the included EDs are not representative of all febrile children attending EDs in that country. Conclusions In this study, we observed wide variation between European EDs in prescriptions of antibiotics and broad-spectrum antibiotics in febrile children. Overall, one-third of prescriptions were inappropriate or inconclusive, with marked variation between EDs. Until better diagnostics are available to accurately differentiate between bacterial and viral aetiologies, implementation of antimicrobial stewardship guidelines across Europe is necessary to limit antimicrobial resistance.publishersversionPeer reviewe

The Methodology of Human Diseases Risk Prediction Tools

Disease risk prediction tools are used for population screening and to guide clinical care. They identify which individuals have particularly elevated risk of disease. The development of a new risk prediction tool involves several methodological components including: selection of a general modelling framework and specific functional form for the new tool, making decisions about the inclusion of risk factors, dealing with missing data in those risk factors, and performing validation checks of a new tool's performance. There have been many methodological developments of relevance to these issues in recent years. Developments of importance for disease detection in humans were reviewed and their uptake in risk prediction tool development illustrated. This review leads to guidance on appropriate methodology for future risk prediction development activities

External validation and updating of a prediction model for nosocomial pneumonia after coronary artery bypass graft surgery

The generalizability of a prediction model from North America for incident nosocomial pneumonia following coronary artery bypass graft surgery was assessed for 23247 patients on the Australian and New Zealand Society of Cardiac and Thoracic Surgeons (ANZSCTS) registry. The performance of the North American model was evaluated using measures of calibration and discrimination. The model had reasonable discrimination (area under the receiver-operating characteristic curve, AUC=0·69), but unsatisfactory calibration (Hosmer-Lemeshow test, P<0·001) in the ANZSCTS patients. An update of the model coefficients yielded a model with AUC=0·71 and good calibration (P=0·46). © 2013 Cambridge University Press

The methodology of human diseases risk prediction tools

Disease risk prediction tools are used for population screening and to guide clinical care. They identify which individuals have particularly elevated risk of disease. The development of a new risk prediction tool involves several methodological components including: selection of a general modelling framework and specific functional form for the new tool, making decisions about the inclusion of risk factors, dealing with missing data in those risk factors, and performing validation checks of a new tool's performance. There have been many methodological developments of relevance to these issues in recent years. Developments of importance for disease detection in humans were reviewed and their uptake in risk prediction tool development illustrated. This review leads to guidance on appropriate methodology for future risk prediction development activities

potential link for susceptibility to glaucoma

Central corneal thickness and correlation to optic disc size: a potential link for susceptibility to glaucom

Associations of hospital characteristics with nosocomial pneumonia after cardiac surgery can impact on standardized infection rates

To identify hospital-level factors associated with post-cardiac surgical pneumonia for assessing their impact on standardized infection rates (SIRs), we studied 43 691 patients in a cardiac surgery registry (2001–2011) in 16 hospitals. In a logistic regression model for pneumonia following cardiac surgery, associations with hospital characteristics were quantified with adjustment for patient characteristics while allowing for clustering of patients by hospital. Pneumonia rates varied from 0·7% to 12·4% across hospitals. Seventy percent of variability in the pneumonia rate was attributable to differences in hospitals in their long-term rates with the remainder attributable to within-hospital differences in rates over time. After adjusting for patient characteristics, the pneumonia rate was found to be higher in hospitals with more registered nurses (RNs)/100 intensive-care unit (ICU) admissions [adjusted odds ratio (aOR) 1·2, P = 0·006] and more RNs/available ICU beds (aOR 1·4, P < 0·001). Other hospital characteristics had no significant association with pneumonia. SIRs calculated on the basis of patient characteristics alone differed substantially from the same rates calculated on the basis of patient characteristics and the hospital characteristic of RNs/100 ICU admissions. Since SIRs using patient case-mix information are important for comparing rates between hospitals, the additional allowance for hospital characteristics can impact significantly on how hospitals compare

Transfusion practice varies widely in cardiac surgery: Results from a national registry

Objectives: Evidence is accumulating of adverse outcomes associated with transfusion of blood components. If there are differences in perioperative transfusion rates in cardiac surgery, and what hospital factors may contribute, requires further investigation. Methods: Analysis of 42,743 adult patients who underwent 43,482 procedures from 2005 to 2011 at 25 Australian hospitals, according to the Australian and New Zealand Society of Cardiac and Thoracic Surgeons Cardiac Surgery Database. Multiple logistic regression examined associations of patient and hospital characteristics with transfusion of =1 red blood cell (RBC) unit; platelet (PLT), fresh frozen plasma (FFP), and cryoprecipitate (CRYO) doses; and =5 RBC units, from surgery until hospital discharge. Results: Procedures included 24,222 (55%) isolated coronary artery bypass grafts, 7299 (17%) isolated valve, 4714 (11%) coronary artery bypass graft and valve, and 7247 (17%) other procedures. After adjustment for various patient and procedure characteristics, transfusion rates varied across hospitals for =1 RBC unit from 22% to 67%, =5 RBC units from 5% to 25%, =1 PLT dose from 11% to 39%, =1 FFP dose from 11% to 48% and =1 CRYO dose from 1% to 20%. Hospital characteristics, including state or territory, private versus public, and teaching versus nonteaching, were not associated with variation in transfusion rates. Conclusions: Variation in transfusion of all components and large volume RBC was identified, even after adjustment for patient and procedural factors known to influence transfusion, and this was not explained by hospital characteristics. Copyright © 2014 by The American Association for Thoracic Surgery

Reference effect measures for quantifying, comparing and visualizing variation from random and fixed effects in non-normal multilevel models, with applications to site variation in medical procedure use and outcomes

Abstract Background Multilevel models for non-normal outcomes are widely used in medical and health sciences research. While methods for interpreting fixed effects are well-developed, methods to quantify and interpret random cluster variation and compare it with other sources of variation are less established. Random cluster variation, sometimes referred to as general contextual effects (GCE), may be the main focus of a study; therefore, easily interpretable methods are needed to quantify GCE. We propose a Reference Effect Measure (REM) approach to 1) quantify GCE and compare it to individual subject and cluster covariate effects, and 2) quantify relative magnitudes of GCE and variation from sets of measured factors. Methods To illustrate REM, we consider a two-level mixed logistic model with patients clustered within hospitals and a random intercept for hospitals. We compare patients at hospitals at given percentiles of the estimated random effect distribution to patients at a median or ‘reference’ hospital. These estimates are then compared numerically and graphically to individual fixed effects to quantify GCE in the context of effects of other measured variables (aim 1). We then extend this approach by comparing variation from the random effect distribution to variation from sets of fixed effects to understand their magnitudes relative to overall outcome variation (aim 2). Results Using an example of initiation of rhythm control treatment in atrial fibrillation (AF) patients within the Veterans Affairs (VA), we use REM to demonstrate that random variation across hospitals (GCE) in initiation of treatment is substantially greater than that due to most individual patient factors, and explains at least as much variation in treatment initiation as do all patient factors combined. These results are contrasted with a relatively small GCE compared with patient factors in 1 year mortality following hospitalization for AF patients. Conclusions REM provides a means of quantifying random effect variation (GCE) with multilevel data and can be used to explore drivers of outcome variation. This method is easily interpretable and can be presented visually. REM offers a simple, interpretable approach for evaluating questions of growing importance in the study of health care systems