Objectives: Toxic serum cefepime trough concentrations are not well defined in the current literature. We aimed to define a more precise plasma trough concentration threshold for this antibiotic's neurological toxicity and to identify individuals at risk for developing neurotoxic side effects. Methods: Retrospective study including all individuals who underwent cefepime therapeutic drug monitoring (TDM) between 2013 and 2017. Individuals with cefepime concentrations other than trough were excluded. The primary outcome was to assess the incidence of neurotoxicity and its relationship with cefepime plasma trough concentrations. Secondary outcomes were the relationship of renal function, cefepime daily dose, age, and cerebral and general co-morbidities with the occurrence of neurotoxicity. We also compared the mortality rate during hospitalization in individuals with and without neurotoxicity, and the possible impact of neuroprotective co-medications on outcomes. Results: Cefepime concentrations were determined in 584 individuals. Among 319 individuals with available trough concentrations included, the overall incidence of neurotoxicity was 23.2% (74 of 319 individuals). Higher cefepime plasma trough concentrations were significantly associated with risk of neurotoxicity (no neurotoxicity 6.3 mg/L (interquartile range (IQR) 4.1e8.6) versus with neurotoxicity 21.6 mg/L (IQR 17.0e28.6), p <0.001). Individuals with presumed cefepime neurotoxicity had a significantly lower renal function (estimated glomerular filtration rate 82.0 mL/min/1.73 m 2 (IQR 45.0e105.0) versus 35.0 mL/min/1.73 m 2 (IQR 23.3e53.3], p <0.001), and significantly higher in-hospital mortality (19 (7.8%) versus 26 (35.1%) individuals, p <0.001). No neurotoxic side effects were seen below a trough concentration of 7.7 mg/L. Levels 38.1 mg/L always led to neurological side effects. Conclusion: In individuals with risk factors for cefepime neurotoxicity, such as renal insufficiency, TDM should be systematically performed, aiming at trough concentrations <7.5 mg/L.

The extended insulin regimen, which was easy to implement at ward level, produced a more rapid resolution of ketosis than the conventional regimen.

BackgroundBloodstream infections are often associated with significant mortality and morbidity. We aimed to investigate changes in the epidemiology of bloodstream infections in Switzerland between 2008 and 2014.MethodsData on bloodstream infections were obtained from the Swiss antibiotic resistance surveillance system (ANRESIS).ResultsThe incidence of bloodstream infections increased throughout the study period, especially among elderly patients and those receiving care in emergency departments and university hospitals. Escherichia coli was the predominant pathogen, with Enterococci exhibiting the most prominent increase over the study period.ConclusionsThe described trends may impact morbidity, mortality and healthcare costs associated with bloodstream infections.

The analysis of time‐to‐event data typically makes the censoring at random assumption, ie, that—conditional on covariates in the model—the distribution of event times is the same, whether they are observed or unobserved (ie, right censored). When patients who remain in follow‐up stay on their assigned treatment, then analysis under this assumption broadly addresses the de jure, or “while on treatment strategy” estimand. In such cases, we may well wish to explore the robustness of our inference to more pragmatic, de facto or “treatment policy strategy,” assumptions about the behaviour of patients post‐censoring.This is particularly the case when censoring occurs because patients change, or revert, to the usual (ie, reference) standard of care. Recent work has shown how such questions can be addressed for trials with continuous outcome data and longitudinal follow‐up, using reference‐based multiple imputation. For example, patients in the active arm may have their missing data imputed assuming they reverted to the control (ie, reference) intervention on withdrawal. Reference‐based imputation has two advantages: (a) it avoids the user specifying numerous parameters describing the distribution of patients' postwithdrawal data and (b) it is, to a good approximation, information anchored, so that the proportion of information lost due to missing data under the primary analysis is held constant across the sensitivity analyses. In this article, we build on recent work in the survival context, proposing a class of reference‐based assumptions appropriate for time‐to‐event data. We report a simulation study exploring the extent to which the multiple imputation estimator (using Rubin's variance formula) is information anchored in this setting and then illustrate the approach by reanalysing data from a randomized trial, which compared medical therapy with angioplasty for patients presenting with angina.