Highlights d Cities possess a consistent ''core'' set of non-human microbes d Urban microbiomes echo important features of cities and city-life d Antimicrobial resistance genes are widespread in cities d Cities contain many novel bacterial and viral species
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.
BackgroundEach year, nearly 2 million patients contract and are affected by healthcare-associated infections (HAIs) in the United States alone, resulting in nearly 100K deaths. According to the Centers for Disease Control and Prevention (CDC), more patients die from HAIs in the United States per year than all breast and prostate cancer cases combined (National Vital Statistics Report, 2016). In addition to the mortality burden, the financial impact of HAIs within the hospital ecosystem is estimated to total between $28–45 billion. However, no economic model has demonstrated how early effective identification and mitigation of infection clusters can result in cost savings for hospitals until now.MethodsAs there is no publicly available data for infection cluster rates, we based our analysis on anonymized real-world retrospective data spanning 18 months (November 2016 to June 2018) from two US-based academic tertiary hospitals with a combined total of about 1,700 beds, then normalized to 800 beds. A cloud-computing platform (Philips IntelliSpace Epidemiology) was used for whole-genome sequence analysis and cluster identification. We determined that an average 800-bed facility would have an occurrence of 46 genetically related infectious clusters involving 2 or more patients (mean of 7.9, median of 3), affecting 180 patients in total.ResultsGiven the average HAI treatment cost of $24,512 (average costs rescaled from literature to 2019 USD using PPI data), this represents a total cost of $4,412,160. If these clusters could have been limited to 2 patients, an additional 96 infections might have been prevented, representing a potentially avoidable economic burden of $2,353,152 for this 800-bed institution. Our data show that a 20% reduction in transmissions would drive a 3% overall reduction in HAIs, but results in savings of over $450,000.ConclusionActive, genomic-based surveillance can inform timely and precise preventative steps to help lower the size of infectious clusters. This health economic modeling shows that such measures can result in significant cost savings. As such, it recommends that prompt, dynamic detection of infectious clusters via genomics and active surveillance offers a relevant and timely strategy for cost savings within the healthcare ecosystem. Disclosures All authors: No reported disclosures.
Background: Infection prevention and control (IPC) workflows are often retrospective and manual. New tools, however, have entered the field to facilitate rapid prospective monitoring of infections in hospitals. Although artificial intelligence (AI)–enabled platforms facilitate timely, on-demand integration of clinical data feeds with pathogen whole-genome sequencing (WGS), a standardized workflow to fully harness the power of such tools is lacking. We report a novel, evidence-based workflow that promotes quicker infection surveillance via AI-assisted clinical and WGS data analysis. The algorithm suggests clusters based on a combination of similar minimum inhibitory concentration (MIC) data, timing of sample collection, and shared location stays between patients. It helps to proactively guide IPC professionals during investigation of infectious outbreaks and surveillance of multidrug-resistant organisms and healthcare-acquired infections. Methods: Our team established a 1-year workgroup comprised of IPC practitioners, clinical experts, and scientists in the field. We held weekly roundtables to study lessons learned in an ongoing surveillance effort at a tertiary care hospital—utilizing Philips IntelliSpace Epidemiology (ISEpi), an AI-powered system—to understand how such a tool can enhance practice. Based on real-time case discussions and evidence from the literature, a workflow guidance tool and checklist were codified. Results: In our workflow, data-informed clusters posed by ISEpi underwent triage and expert follow-up analysis to assess: (1) likelihood of transmission(s); (2) potential vector(s) identity; (3) need to request WGS; and (4) intervention(s) to be pursued, if warranted. In a representative sample (spanning October 17, 2019, to November 7, 2019) of 67 total isolates suggested for inclusion in 19 unique cluster investigations, we determined that 9 investigations merited follow-up. Collectively, these 9 investigations involved 21 patients and required 115 minutes to review in ISEpi and an additional 70 minutes of review outside of ISEpi. After review, 6 investigations were deemed unlikely to represent a transmission; the other 3 had potential to represent transmission for which interventions would be performed. Conclusions: This study offers an important framework for adaptation of existing infection control workflow strategies to leverage the utility of rapidly integrated clinical and WGS data. This workflow can also facilitate time-sensitive decisions regarding sequencing of specific pathogens given the preponderance of available clinical data supporting investigations. In this regard, our work sets a new standard of practice: precision infection prevention (PIP). Ongoing effort is aimed at development of AI-powered capabilities for enterprise-level quality and safety improvement initiatives.Funding: Philips Healthcare provided support for this study.Disclosures: Alan Doty and Juan Jose Carmona report salary from Philips Healthcare.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.