An outbreak of multi-drug-resistant Acinetobacter baumannii on a burns ICU and its control with multi-faceted containment measures.

BACKGROUND: Patients in burns centres are at high risk of acquiring multi-drug-resistant organisms (MDROs) due to the reduced skin barrier and long hospital stay. METHODS: This study reports the investigation and control of an outbreak of MDR Acinetobacter baumannii in a burns centre. The 27 patients hospitalized in the centre during the outbreak were screened regularly, and a total of 132 environmental samples were analysed to identify a potential source. Fourier-transform infra-red (FT-IR) spectroscopy and multi-locus sequence typing were applied to characterize the outbreak strain. RESULTS: Between August and November 2022, the outbreak affected eight patients, with 11 infections and three potentially related fatal outcomes. An interdisciplinary and multi-professional outbreak team implemented a bundle strategy with repetitive admission stops, isolation precaution measures, patient screenings, enhanced cleaning and disinfection, and staff education. FT-IR spectroscopy suggested that the outbreak started from a patient who had been repatriated 1 month previously from a country with high prevalence of MDR A. baumannii. Environmental sampling did not identify a common source. Acquisition of the outbreak strain was associated with a higher percentage of body surface area with burn lesions ≥2a [per percent increase: odds ratio (OR) 1.05, 95% confidence interval (CI) 0.99-1.12; P=0.09], and inversely associated with a higher nurse-to-patient ratio (per 0.1 increase: OR 0.34, 95% CI 0.10-1.12; P=0.06). CONCLUSIONS: Burn patients with a higher percentage of body surface area with burn lesions ≥2a are at high risk of colonization and infection due to MDROs, particularly during periods of high workload. A multi-faceted containment strategy can successfully control outbreaks due to MDR A. baumannii in a burns centre.

Requirements for a Compton camera for in vivo range verification of proton therapy.

To ensure the optimal outcome of proton therapy, in vivo range verification is highly desired. Prompt γ-ray imaging (PGI) is a possible approach for in vivo range monitoring. For PGI, dedicated detection systems, e.g. Compton cameras, are currently under investigation. The presented paper deals with substantial requirements regarding hardware and software that a Compton camera used in clinical routine has to meet. By means of GEANT4 simulations, we investigate the load on the detectors and the percentage of background expected in a realistic irradiation and we simulate γ-ray detections subsequently used as input data for the reconstruction. By reconstructing events from simulated sources of well-defined geometry, we show that large-area detectors are favourable. We investigate reconstruction results in dependence of the number of events. Finally, an end-to-end test for a realistic patient scenario is presented: starting with a treatment plan, the γ-ray emissions are calculated, the detector response is modelled, and the image reconstruction is performed. By this, the complexity of the system is shown, and requirements and limitations regarding precision and costs are determined.

From prompt gamma distribution to dose: a novel approach combining an evolutionary algorithm and filtering based on Gaussian-powerlaw convolutions.

Range verification and dose monitoring in proton therapy is considered as highly desirable. Different methods have been developed worldwide, like particle therapy positron emission tomography (PT-PET) and prompt gamma imaging (PGI). In general, these methods allow for a verification of the proton range. However, quantification of the dose from these measurements remains challenging. For the first time, we present an approach for estimating the dose from prompt γ-ray emission profiles. It combines a filtering procedure based on Gaussian-powerlaw convolution with an evolutionary algorithm. By means of convolving depth dose profiles with an appropriate filter kernel, prompt γ-ray depth profiles are obtained. In order to reverse this step, the evolutionary algorithm is applied. The feasibility of this approach is demonstrated for a spread-out Bragg-peak in a water target.