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Background: Infection prevention surveillance for cross transmission is often performed by manual review of microbiologic culture results to identify geotemporally related clusters. However, the sensitivity and specificity of this approach remains uncertain. Whole-genome sequencing (WGS) analysis can help provide a gold-standard for identifying cross-transmission events. Objective: We employed a published WGS program, the Philips IntelliSpace Epidemiology platform, to compare accuracy of two surveillance methods: (i.) a virtual infection practitioner (VIP) with perfect recall and automated analysis of antibiotic susceptibility testing (AST), sample collection timing, and patient location data and (ii) a novel clinical matching (CM) algorithm that provides cluster suggestions based on a nuanced weighted analysis of AST data, timing of sample collection, and shared location stays between patients. Methods: WGS was performed routinely on inpatient and emergency department isolates of Enterobacter cloacae, Enterococcus faecium, Klebsiella pneumoniae, and Pseudomonas aeruginosa at an academic medical center. Single-nucleotide variants (SNVs) were compared within core genome regions on a per-species basis to determine cross-transmission clusters. Moreover, one unique strain per patient was included within each analysis, and duplicates were excluded from the final results. Results: Between May 2018 and April 2019, clinical data from 121 patients were paired with WGS data from 28 E. cloacae, 21 E. faecium, 61 K. pneumoniae, and 46 P. aeruginosa isolates. Previously published SNV relatedness thresholds were applied to define genomically related isolates. Mapping of genomic relatedness defined clusters as follows: 4 patients in 2 E. faecium clusters and 2 patients in 1 P. aeruginosa cluster. The VIP method identified 12 potential clusters involving 28 patients, all of which were “pseudoclusters.” Importantly, the CM method identified 7 clusters consisting of 27 patients, which included 1 true E. faecium cluster of 2 patients with genomically related isolates. Conclusions: In light of the WGS data, all of the potential clusters identified by the VIP were pseudoclusters, lacking sufficient genomic relatedness. In contrast, the CM method showed increased sensitivity and specificity: it decreased the percentage of pseudoclusters by 14% and it identified a related genomic cluster of E. faecium. These findings suggest that integrating clinical data analytics and WGS is likely to benefit institutions in limiting expenditure of resources on pseudoclusters. Therefore, WGS combined with more sophisticated surveillance approaches, over standard methods as modeled by the VIP, are needed to better identify and address true cross-transmission events.
Funding: This study was supported by Philips Healthcare.
Decontamination of N95 respirators is being used by clinicians in the face of a global shortage of these devices. Some treatments for decontamination, such as some vaporized hydrogen peroxide methods or ultraviolet methods, had no impact on respiratory performance, while other treatments resulted in substantial damage to masks.
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.
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.
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.
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.
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.
In a recent discussion about how scientific knowledge might potentially change our understanding of the nature and extent of human genetic, cultural, or biological variation, the sociologist David Skinner identified two competing visions of the future: one that was decidedly dystopian, which conjured up a “re-racialized” future, and an opposing utopian future in which the potential for racialized thinking might be finally overcome. We can situate the ongoing debates about the congestive heart failure drug BiDil, approved by the Food and Drug Administration (FDA) for use only by African Americans, in relation to these differing future prospects.
When the FDA announced its approval of BiDil in June 2005, it located the drug, and perhaps the future of pharmaceutical development, within a particular vision of the future, heralding BiDil as “representing a step toward the promise of personalized medicine.” The discourse of “personalized medicine” can be characterized as part of a utopian future, one in which clinicians will be able to make increasingly individualized decisions based on each patient’s genetic makeup so that the drugs they take will be those that work best for them.
To assess the impact of outbreaks of Bordetella pertussis infection on a tertiary care medical system.
Academic tertiary care medical center and affiliated ambulatory care settings.
All patients and healthcare workers (HCWs) who were in close contact with patients with laboratory-confirmed cases of B. pertussis infection from October 1, 2003, through September 30, 2004.
Direct and indirect medical center costs were determined, including low and high estimates of time expended in the evaluation and management of exposed patients and HCWs during outbreak investigations of laboratory-confirmed cases of B. pertussis infection.
During this period, 20 primary and 3 secondary laboratory-confirmed cases of B. pertussis infection occurred, with 2 primary pertussis cases and 1 secondary case occurring in HCWs. Outbreak investigations prompted screening of 353 medical center employees. Probable or definitive exposure was identified for 296 HCWs, and 287 subsequently received treatment or prophylaxis for B. pertussis infection. Direct medical center costs for treatment and prophylaxis were $13,416 and costs for personnel time were $19,500-$31,190. Indirect medical center costs for time lost from work were $51,300-$52,300. The total cost of these investigations was estimated to be $85,066-$98,456.
Frequent B. pertussis exposures had a major impact on our facility. Given the impact of exposures on healthcare institutions, routine vaccination for HCWs may be beneficial.
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