Current Antibiotic Treatment and Outcome for Lower Respiratory Tract Infections

A number of national guidelines have been published to aid the antimicrobial management of community-acquired pneumonia. However, data on prescriptions for lower respiratory tract infection (LRTI) indicate considerable variation in the choice of first-line and subsequent therapy at national and local levels. Outcomes research in LRTI, whether based on clinical, economic or patient-focused criteria, is still evolving. Clinical outcomes are best studied for both pneumonia and exacerbation of chronic obstructive pulmonary disease. Economic evaluations often do not encompass all of the costs, for example, time off from work or the economic impact of antibacterial resistance. Duration of hospital stay is a good marker of costs for hospital providers and may be affected by age. marital status and comorbidities. Antibiotic choice may have an impact on the duration of hospital stay by increasing side effects, predisposing patients to hospitalacquired infection or reduced clinical efficacy. Patient expectation is largely unstudied in pulmonary infection

Antimicrobial Drug Resistance, Regulation, and Research1

Research models and regulatory measures could aid in developing antimicrobial drugs to address bacterial resistance

Time-to-positivity in bloodstream infection is not a prognostic marker for mortality:analysis of a prospective multicentre randomized control trial

Objectives Time to positivity (TTP), calculated automatically in modern blood culture systems, is considered a proxy for microbial load and has been suggested as a potential prognostic marker in bloodstream infections. In this large, multicentre, prospectively collected cohort, our primary analysis aimed to quantify the relationship between the TTP of monomicrobial blood cultures and mortality. Methods Data from a multicentre randomized controlled trial (RAPIDO) in bloodstream infection were analysed. Bloodstream infections were classified into 13 groups/subgroups. The relationship between mortality and TTP was assessed by logistic regression, adjusted for site, organism, and clinical variables, and linear regression was applied to examine the association between clinical variables and TTP. Robustness was assessed by sensitivity analysis. Results In total 4468 participants were included in the RAPIDO. After exclusions, 3462 were analysed, with the most common organisms being coagulase-negative staphylococci (1072 patients) and Escherichia coli (861 patients); 785 patients (22.7%) died within 28 days. We found no relationship between TTP and mortality for any groups except for streptococci (odds ratio (OR) with each hour 0.98, 95%CI 0.96–1.00) and Candida (OR 1.03, 95%CI 1.00–1.05). There was large variability between organisms and sites in TTP. Fever (geometric mean ratio (GMR) 0.95, 95%CI 0.92–0.99), age (GMR per 10 years 1.01, 95%CI 1.00–1.02), and neutrophilia were associated with TTP (GMR 1.03, 95%CI 1.02–1.04). Conclusions Time to positivity is not associated with mortality, except in the case of Candida spp. (longer times associated with worse outcomes) and possibly streptococci (shorter times associated with worse outcomes). There was a large variation between median times across centres, limiting external validity

Analytical challenges in estimating the effect of exposures that are bounded by follow-up time: experiences from the Blood Stream Infection—Focus on Outcomes study

Abstract Objective To illustrate the challenges of estimating the effect of an exposure that is bounded by duration of follow-up on all-cause 28-day mortality, whilst simultaneously addressing missing data and time-varying covariates. Study design and methods BSI-FOO is a multicentre cohort study with the primary aim of quantifying the effect of modifiable risk factors, including time to initiation of therapy, on all-cause 28-day mortality in patients with bloodstream infection. The primary analysis involved two Cox proportional hazard models, first one for non-modifiable risk factors and second one for modifiable risk factors, with a risk score calculated from the first model included as a covariate in the second model. Modifiable risk factors considered in this study were recorded daily for a maximum of 28 days after infection. Follow-up was split at daily intervals from day 0 to 28 with values of daily collected data updated at each interval (i.e., one row per patient per day). Analytical challenges Estimating the effect of time to initiation of treatment on survival is analytically challenging since only those who survive to time t can wait until time t to start treatment, introducing immortal time bias. Time-varying covariates representing cumulative counts were used for variables bounded by survival time e.g. the cumulative count of days before first receipt of treatment. Multiple imputation using chained equations was used to impute missing data, using conditional imputation to avoid imputing non-applicable data e.g. ward data after discharge. Conclusion Using time-varying covariates represented by cumulative counts within a one row per day per patient framework can reduce the risk of bias in effect estimates. The approach followed uses established methodology and is easily implemented in standard statistical packages

Effects of common mutations in the SARS-CoV-2 Spike RBD domain and its ligand the human ACE2 receptor on binding affinity and kinetics

The interaction between the SARS-CoV-2 virus Spike protein receptor binding domain (RBD) and the ACE2 cell surface protein is required for viral infection of cells. Mutations in the RBD are present in SARS-CoV-2 variants of concern that have emerged independently worldwide. For example, the B.1.1.7 lineage has a mutation (N501Y) in its Spike RBD that enhances binding to ACE2. There are also ACE2 alleles in humans with mutations in the RBD binding site. Here we perform a detailed affinity and kinetics analysis of the effect of five common RBD mutations (K417N, K417T, N501Y, E484K, and S477N) and two common ACE2 mutations (S19P and K26R) on the RBD/ACE2 interaction. We analysed the effects of individual RBD mutations and combinations found in new SARS-CoV-2 Alpha (B.1.1.7), Beta (B.1.351), and Gamma (P1) variants. Most of these mutations increased the affinity of the RBD/ACE2 interaction. The exceptions were mutations K417N/T, which decreased the affinity. Taken together with other studies, our results suggest that the N501Y and S477N mutations enhance transmission primarily by enhancing binding, the K417N/T mutations facilitate immune escape, and the E484K mutation enhances binding and immune escape