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.

Whole-genome sequencing is increasingly adopted in clinical settings to identify pathogen transmissions, though largely as a retrospective tool. Prospective monitoring, in which samples are continuously added and compared to previous samples, can generate more actionable information. To enable prospective pathogen comparison, genomic relatedness metrics based on single-nucleotide differences must be consistent across time, efficient to compute and reliable for a large variety of samples. The choice of genomic regions to compare, i.e ., the core genome , is critical to obtain a good metric. We propose a novel core genome method that selects conserved sequences in the reference genome by comparing its k-mer content to that of publicly available genome assemblies. The conserved-sequence genome is sample set-independent, which enables prospective pathogen monitoring. Based on clinical data sets of 3436 S. aureus , 1362 K. pneumoniae and 348 E. faecium samples, ROC curves demonstrate that the conserved-sequence genome disambiguates same-patient samples better than a core genome consisting of conserved genes. The conserved-sequence genome confirms outbreak samples with high sensitivity: in a set of 2335 S. aureus samples, it correctly identifies 44 out of 44 known outbreak samples, whereas the conserved-gene method confirms 38 known outbreak samples.

BackgroundTypical Infection Prevention to detect pathogen transmission in hospitals has relied on observation of (1) uncommon pathogen phenotypes or (2) greater than expected number of pathogen phenotypes in a given timeframe and/or location. Genome sequencing of targeted organisms in conjunction with routine patient geo-temporal information and antibiotic susceptibility data holds promise in identifying transmissions with greater sensitivity and specificity, saving time and effort in reviewing for transmission events.MethodsIn an on-going genomic sequencing surveillance effort in a tertiary care hospital, drug-resistant clinical isolates from the “ESKAPE” pathogens were routinely sequenced in 2017. In parallel, potential clusters were identified for 2017 through conventional Infection Prevention approaches. Groups identified by their genetic distances along with visualizations on antimicrobial susceptibilities, and patient location histories and dates were displayed in an interactive interface, Philips IntelliSpace Epidemiology (PIE), and reviewed by Infection Prevention.ResultsAmong 656 patients, 1,239 drug-resistant ESKAPE samples were sequenced. Thirty-eight genetically related groups involving 196 patients were identified. Groups ranged in size from two to 44 patients, primarily consisting of VRE and MRSA. Notably, a review of the 38 groups identified 20 groups where the information at hand suggested a concern for transmission. 16 of the 20 were not previously identified by Infection Prevention. Using PIE to review all 38 groups identified from 1 year’s worth of data required 3 hours of time by an Infection Prevention professional, averaging less than 5 minutes per cluster, less than 1 minute per patient, and 11 minutes of review time per actionable opportunity. By conventional means, approximately 23 hours would have been required to review the genomic groups without the aid of the PIE tool.ConclusionThe use of PIE’s genomic-defined groups, along with the integrated clinical data platform, allows for a greater ability, certainty, and speed to detect clusters of organisms representing transmission in the hospital setting. Applied prospectively, PIE can detect transmissions sooner than by conventional means for potential patient safety gains and cost savings.Disclosures D. Chen, Philips: Scientific Advisor, Consulting fee. M. Fortunato-Habib, Philips Healthcare: Collaborator and Employee, Salary. A. Hoss, Philips: Employee, Salary. R. Kolde, Philips: Employee, Salary. A. Dhand, Merck: Speaker’s Bureau, Speaker honorarium. Astellas: Scientific Advisor, Consulting fee. R. Sussner, Philips: Scientific Advisor, Consulting fee. J. Carmona, Philips Healthcare: Employee, Salary. B. Gross, Philips Healthcare: Employee, Investigator, Research Contractor, Scientific Advisor and Shareholder, Salary. J. Fallon, Philips Healthcare: Investigator, Research support.

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BackgroundWhole-genome sequencing (WGS) of bacteria is becoming a routine tool within microbiology, yet its utility to help guide infection control (IC) practice longitudinally is underexplored. As with any technology adopted in the hospital, the integration of WGS into IC practice must be carefully managed and considered. We qualitatively report an evidence-based implementation workflow that considers WGS to help proactively guide IC professionals during investigation of infectious outbreaks.MethodsWe built upon lessons learned in an ongoing surveillance effort at a tertiary care hospital—utilizing retrospective WGS data within the Philips IntelliSpace Epidemiology system—to understand facilitators and barriers to the use of bacterial WGS longitudinally to inform IC workflow. Our team established a 9-month workgroup to study the practical aspects of implementing WGS in routine IC practice. From expert opinion collected via the workgroup, in addition to evidence from the literature, a workflow guidance document and checklist were codified. New ideas included incorporating education to promote the establishment of an IC triage process.ResultsFacilitators to implementation included ability to display genomic relatedness alongside relevant patient data to enable clinical actionability, ability to pivot time and resources rapidly when infections are a pseudo outbreak (false positive) or missed outbreak (false negative), opportunities for nuanced staff education, and willingness to be a first-of-kind adopter. Barriers were communication of genomic concepts to IC professionals and relevant institutional stakeholders, maintaining sharable notes of active investigations to promote data-sharing practices, and timing and review of relevant interventions into the facility workflow. Strategies to address these issues are considered.ConclusionThis study provides a novel framework for adaptation of existing IC workflow strategies to leverage the utility of bacterial WGS, and it presents a schema to effectively engage relevant stakeholders, based on an analysis of the unique challenges inherent within IC practice. It also offers an innovative model for the development and implementation of IC workflows to account for, and adapt to, site-specific conditions.Disclosures All authors: No reported disclosures.

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.Disclosures: None

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.