No abstract

The implementation of a standard catheter bundle combined with chlorhexidine dressings, minocycline/rifampin catheters, and other behavioral changes was associated with a sustained reduction in CLABSIs.

Candida auris is a yeast that can be transmitted from human to human, has caused multiple outbreaks in health care settings in recent years, and can be difficult to eradicate. 1 The fungus was first isolated from the external ear canal of a patient in Japan in 2009; multiple different clades have since been identified worldwide. 2 The yeast is typically fluconazole resistant and often multidrug resistant, with 40% of isolates resistant to agents in at least 2 antifungal classes. 1 Patients can become colonized for long periods, some with high burden, and there are no known effective decolonization methods. 1 The yeast can form biofilms and has been shown to spread efficiently within health care settings. 3 To date, relatively little has been published about the epidemiology and clinical manifestations of C auris infections in

Background:Determining infectious cross-transmission events in healthcare settings involves manual surveillance of case clusters by infection control personnel, followed by strain typing of clinical/environmental isolates suspected in said clusters. Recent advances in genomic sequencing and cloud computing now allow for the rapid molecular typing of infecting isolates.Objective:To facilitate rapid recognition of transmission clusters, we aimed to assess infection control surveillance using whole-genome sequencing (WGS) of microbial pathogens to identify cross-transmission events for epidemiologic review.Methods:Clinical isolates of Staphylococcus aureus, Enterococcus faecium, Pseudomonas aeruginosa, and Klebsiella pneumoniae were obtained prospectively at an academic medical center, from September 1, 2016, to September 30, 2017. Isolate genomes were sequenced, followed by single-nucleotide variant analysis; a cloud-computing platform was used for whole-genome sequence analysis and cluster identification.Results:Most strains of the 4 studied pathogens were unrelated, and 34 potential transmission clusters were present. The characteristics of the potential clusters were complex and likely not identifiable by traditional surveillance alone. Notably, only 1 cluster had been suspected by routine manual surveillance.Conclusions:Our work supports the assertion that integration of genomic and clinical epidemiologic data can augment infection control surveillance for both the identification of cross-transmission events and the inclusion of missed and exclusion of misidentified outbreaks (ie, false alarms). The integration of clinical data is essential to prioritize suspect clusters for investigation, and for existing infections, a timely review of both the clinical and WGS results can hold promise to reduce HAIs. A richer understanding of cross-transmission events within healthcare settings will require the expansion of current surveillance approaches.