Outbreak of Vancomycin-resistant Enterococcus faecium in Interventional Radiology: Detection Through Whole-genome Sequencing-based Surveillance.

Background: Vancomycin-resistant enterococci (VRE) are a major cause of hospital-acquired infections. The risk of infection from interventional radiology (IR) procedures is not well documented. Whole-genome sequencing (WGS) surveillance of clinical bacterial isolates among hospitalized patients can identify previously unrecognized outbreaks.

Use of online tools for antimicrobial resistance prediction by whole-genome sequencing in methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant enterococci (VRE).

Objectives: The antimicrobial resistance (AMR) crisis represents a serious threat to public health and has resulted in concentrated efforts to accelerate development of rapid molecular diagnostics for AMR. In combination with publicly available web-based AMR databases, whole-genome sequencing (WGS) offers the capacity for rapid detection of AMR genes. Here we studied the concordance between WGS-based resistance prediction and phenotypic susceptibility test results for methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant enterococci (VRE) clinical isolates using publicly available tools and databases.

Automated data mining of the electronic health record for investigation of healthcare-associated outbreaks.

Background: Identifying routes of transmission among hospitalized patients during a healthcare-associated outbreak can be tedious, particularly among patients with complex hospital stays and multiple exposures. Data mining of the electronic health record (EHR) has the potential to rapidly identify common exposures among patients suspected of being part of an outbreak.

Methods: We retrospectively analyzed 9 hospital outbreaks that occurred during 2011-2016 and that had previously been characterized both according to transmission route and by molecular characterization of the bacterial isolates.

Statistical outbreak detection by joining medical records and pathogen similarity.

We present a statistical inference model for the detection and characterization of outbreaks of hospital associated infection. The approach combines patient exposures, determined from electronic medical records, and pathogen similarity, determined by whole-genome sequencing, to simultaneously identify probable outbreaks and their root-causes. We show how our model can be used to target isolates for whole-genome sequencing, improving outbreak detection and characterization even without comprehensive sequencing.

Bronchoscope-associated clusters of multidrug-resistant Pseudomonas aeruginosa and carbapenem-resistant Klebsiella pneumoniae.

Objective: Recovery of multidrug-resistant (MDR) Pseudomonas aeruginosa and Klebsiella pneumoniae from a cluster of patients in the medical intensive care unit (MICU) prompted an epidemiologic investigation for a common exposure.

Methods: Clinical and microbiologic data from MICU patients were retrospectively reviewed, MICU bronchoscopes underwent culturing and borescopy, and bronchoscope reprocessing procedures were reviewed. Bronchoscope and clinical MDR isolates epidemiologically linked to the cluster underwent molecular typing using pulsed-field gel electrophoresis (PFGE) followed by whole-genome sequencing.