Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2026 Mar 27.
Published in final edited form as: Infect Control Hosp Epidemiol. 2025 Jun 3;46(8):797–804. doi: 10.1017/ice.2025.68

Whole-genome sequencing surveillance of vancomycin-resistant Enterococcus faecium (VRE) detects hospital outbreaks and identifies the post-anesthesia care unit as a transmission locus

Sarah M Schrader 1,2, Meghan A Baker 3,4, Chanu Rhee 3,4, Michael Klompas 3,4, Samantha Taffner 5, Zachary Pearson 1, Jay Worley 1,6, Lynn Bry 1, Sanjat Kanjilal 3,4, Manfred Brigl 1, Nicole Pecora 1
PMCID: PMC13019461  NIHMSID: NIHMS2152860  PMID: 40457770

Abstract

Objective:

Vancomycin-resistant enterococci (VRE) can cause serious healthcare-associated infections. Patients can become colonized and infected through contact with healthcare workers, hospital surfaces, equipment, and other patients. We evaluated the utility of broadly applied whole-genome sequencing (WGS) surveillance of vancomycin-resistant Enterococcus faecium (VREfm) for detection of hospital-based transmission.

Design:

Retrospective genomic and epidemiologic analysis of clinical VREfm isolates

Setting:

Brigham and Women’s Hospital, an 800-bed tertiary care center in Boston, MA, USA

Methods:

VREfm was isolated from patient screening and diagnostic specimens. We sequenced the genomes of 156 VREfm isolates, 12 at the request of infection control and 144 as a convenience sample, and used single nucleotide polymorphism (SNP) differences to assess relatedness. For isolate pairs separated by 15 or fewer SNPs by two orthogonal comparison methods, we mapped epidemiologic connections to identify putative transmission clusters.

Results:

We found evidence for 16 putative transmission clusters comprising between two and four isolates each and involving 41/156 isolates (26.3%). Our analysis discovered 14 clusters that were missed by traditional surveillance methods and additional members of two clusters that were detected by traditional methods. Patients in four transmission clusters were linked only by exposure to the post-anesthesia care unit.

Conclusions:

We show that WGS surveillance for VREfm can support infection control investigations and detect transmission events missed by routine surveillance methods. We identify the post-anesthesia care unit as a locus for VREfm transmission, which demonstrates how WGS surveillance could inform targeted interventions to prevent the spread of VREfm.

INTRODUCTION

Vancomycin-resistant enterococci (VRE) are a major cause of healthcare-associated infections, responsible for an estimated 54,500 infections associated with 5,400 deaths annually among hospitalized patients in the United States.1 Mortality, length of stay, and costs of care are higher for patients with VRE infections compared to patients infected with vancomycin-susceptible enterococci.2,3

Patients can become asymptomatically colonized with VRE. Colonized individuals are at increased risk of VRE infection4,5 and can spread VRE to others. In the hospital setting, patients can become infected or colonized through contact with other patients, healthcare workers, or hospital surfaces and equipment6; VRE can survive on dry surfaces for months,7–9 and removal requires aggressive disinfection regimens.10,11

Detection of suspected transmission events has generally relied on uncovering epidemiologic links among patients from whom organisms of the same species with phenotypic similarities—typically antibiotic resistance profiles—are isolated, often by means of prospective screening and culturing (“traditional surveillance methods”). However, these methods are often insufficient for VRE given the substantial spatiotemporal gaps that frequently characterize transmission events.12,13 In addition, such investigations cannot distinguish between true transmission events and cases in patients with genetically unrelated strains.

Recent infection control (IC) efforts have focused on applying whole-genome sequencing (WGS) to characterize relatedness of clinical isolates, including VRE.14–22 These efforts have demonstrated the utility of WGS in investigating suspected outbreaks. Emerging studies also suggest that prospective WGS applied to all diagnostic and screening isolates can detect otherwise unrecognized transmission events and facilitate targeted interventions.14,20 In this study, we evaluated the utility of WGS for VRE surveillance among oncology and ICU patients in an academic medical center.

METHODS

Study setting and isolate recovery

We conducted this study at Brigham and Women’s Hospital (BWH) in Boston, MA. BWH serves primarily adult patients with approximately 50,000 inpatient visits per year across over 800 inpatient beds and includes programs for cancer care, solid organ transplant, and stem cell transplant. The Mass General Brigham IRB approved this study.

Vancomycin-resistant Enterococcus faecium (VREfm) strains were isolated in the BWH clinical microbiology laboratory between June 2022 and September 2023 from screening and diagnostic specimens, all from patients without a known history of VRE. Per BWH IC policy, all patients admitted to intensive care and oncology units (Table s1) are screened for VRE on admission and weekly thereafter; for additional details, see the supplementary methods. Diagnostic specimens included urine, blood, wounds, fluids, and tissue. All isolates were from unique patients and were assigned numbers in order of sequencing.

Sequencing and genomic analysis

Genomic DNA was extracted using the MasterPure Gram Positive DNA Purification Kit (Epicentre, Madison, WI). Libraries were prepared using the Illumina DNA Prep Kit and sequenced on the Illumina Miseq (Illumina, Inc., San Diego, CA).

Genomic sequence assembly was performed using an in-house bioinformatics pipeline. Quality control was performed on raw reads using Trimmomatic v0.3923 and FastQC v0.11.9 (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Draft assemblies were generated using SPAdes v3.15.3.24 Assembled genome quality was assessed using QUAST v5.0.3 (Table s2).25 Sequences with N50 < 25 kb were excluded. Genus and species were assigned using Kraken v2.1.2 (01/31/2023) and StrainSeeker v1.526. Multilocus sequence typing (MLST) was performed using open-source code (https://github.com/tseemann/mlst) and stringMLST v0.6.3.27 Minimum spanning trees were generated using SeqSphere+ (Ridom GmbH, Muenster, Germany). All sequences were deposited in the National Center for Biotechnology Information’s (NCBI) Pathogen Detection database (https://www.ncbi.nlm.nih.gov/pathogens/) under BioProject PRJNA278886.

SNP calling (Table s3) was performed using two methods. For whole-genome reference-based alignment, the U.S. Food and Drug Administration Center for Food Safety and Nutrition (CFSAN) SNP pipeline v2.2.128 was used with reference strain ZY11 (CP038995.1). For split k-mer analysis (SKA), SKA v1.029 was used to create k-mer files (k = 15, fastq subcommand, default thresholds for all variables) and calculate whole-genome pairwise SNP distances (ska distance function, default parameters).

Epidemiologic data collection and cluster identification

CFSAN and SKA have both been applied to pairwise SNP calling in VREfm with SNP thresholds to trigger investigation for transmission ranging from ≤ 6 to ≤ 24 (median ≤ 10, interquartile range ≤ 7–15).14,16–22 We selected the upper limit of the interquartile range (≤ 15 SNPs) as an empiric threshold. For all patients whose isolates were ≤ 15 SNPs from another isolate by both methods, we collected information on patient unit assignment for the admission during which the isolate was recovered plus additional admissions as needed. We then identified epidemiologic relationships and sorted them into six categories: “same bed”; “same unit, same time”; “same service, same time”; “same unit, different time”; “same service, different time”; and “same outside facility, same time”. “Unit” refers to a set of rooms in the same physical location within the hospital; patients on a unit typically share the same nursing staff and some equipment. “Service” refers to the admitting service, which determines the patient’s clinician care team. Patients on a single unit might be admitted to different services, and vice versa. No patient pairs in the “same unit, same time” category shared the same room. Patients admitted to both the same unit and same service were classified as “same unit, same time” if concurrent and “same unit, different time” if not concurrent. We defined putative transmission clusters as sets of two or more isolates wherein each had a SNP distance of ≤ 15 from at least one other isolate and wherein each patient had an identifiable epidemiologic relationship with at least one other patient. We prepared color-coded maps to visualize the connections. Statistical tests were performed in GraphPad Prism 10 except the Marascuilo procedure for multiple comparisons, which was performed manually following a significant chi-square test.

RESULTS

Description of the isolates and putative transmission clusters

We analyzed the genomes of 156 VREfm isolates (Table s2). Most (142, 91.0%) were obtained from screening specimens, while the remaining 14 (9.0%) were from diagnostic specimens (Figure 1A). Most (143, 91.7%) were collected between June 2022 and January 2023, including 129 from screening specimens, which represented 60.8% of the total screening isolates recovered from unique patients during that time. MLST sequence types (ST) 203 (35.3%), 117 (30.1%), and 736 (14.1%) were most common (Figure 1B, Figure 2A).

Figure 1. Analysis of genomic relatedness and epidemiologic connections among 156 vancomycin-resistant Enterococcus faecium (VREfm) clinical isolates identifies 16 putative transmission clusters.

Figure 1.

Open in a new tab

A) VREfm isolate source. Screening isolates were obtained from rectal swabs or stool samples to screen for VRE colonization, while diagnostic isolates were obtained from other specimen types sent to the microbiology laboratory for culture. B) Sequence type distribution of all isolates (N = 156) and isolates involved in putative transmission clusters (N = 41). C) Histograms depicting single nucleotide polymorphism (SNP) distribution of isolate pairs calculated by reference-based alignment (CFSAN) and split k-mer analysis (SKA). The left panel shows comparisons between all isolate pairs except isolate 1416, which was separated from all other isolates by 19,109–20,018 SNPs by CFSAN and 16,738–18,206 SNPs by SKA (asterisk at top of y axis). Three prominent peaks are evident. The peak around 3,000 SNPs primarily represents comparisons between isolates from ST203 or ST117 and isolates from ST736, while the peak around 2,300 SNPs primarily represents comparisons between isolates from ST203 and ST117. The peak centered around 25–50 SNPs is comprised of comparisons between isolates of the same ST. Bins are 50 SNPs in width, and tick marks on the y axis represent the center of each bin. The right panel is an expansion of the smallest SNP distance bin on the left panel, which contains pairs with SNP distances < 50. The bracket indicates pairs with SNP distances of 15 or fewer, which are quantified in the Venn diagram. Epidemiologic data was collected from the electronic medical record for all patients involved in the 82 pairs that met the SNP threshold by both CFSAN and SKA to probe for epidemiologic evidence of transmission. Bins are 1 SNP in width. D) and E) SNP distance distributions of isolate pairs for which epidemiologic connections were identified (n = 33, dark blue) compared to those for which connections were not identified (n = 49, light blue). Each circle represents an isolate pair. Black lines behind the dots represent the median and interquartile range. The distributions were compared using the Mann-Whitney test (U = 481.5 for D and 591.5 for E). Exact two-tailed p values are shown. F) and G) The 82 isolate pairs separated by 15 SNPs or fewer by both CFSAN and SKA were organized into four bins of equal width (4 SNPs). The distribution of isolates with epidemiologic connections identified (dark blue) was assessed by the chi-square test (chi-square = 8.575 for F and 5.632 for G, both with three degrees of freedom). A chi-square test for trend was used to test for a linear relationship between SNP distance and the proportion of isolates with epidemiologic connections (chi-square = 8.022 for F and 3.742 for G, both with one degree of freedom). H) Number of isolates in the 16 putative transmission clusters identified based on genomic relatedness and epidemiologic connections. Graphs in parts A and C-H generated using GraphPad Prism 10.

Figure 2. Minimum spanning tree (MST) prepared using core genome multilocus sequence typing (cgMLST) shows relationships among the 156 vancomycin-resistant Enterococcus faecium isolates.

Figure 2.

Open in a new tab

The two panels show the same MST color coded by A) sequence type or B) putative transmission cluster as determined by the single nucleotide polymorphism (SNP) distance and epidemiologic connection analysis described in the text. Each circle represents one isolate, or occasionally multiple isolates that share the same cgMLST. Circles are labeled with the isolate number(s). The MST was generated by Ridom SeqSphere+ as a 3D visualization in which the length of the lines connecting isolate circles are linearly correlated to the core allele difference between them. The figure is a 2D flattening of the 3D structure; thus, lengths of the flattened lines do not correlate directly with core allele difference. All core allele differences > 10 are labeled in panel A. Distances are based on 1,062 columns from E. faecium cgMLST with no missing values and a clustering distance of 20. cgMLST is a method of pairwise comparison that is related to but distinct from CFSAN and SKA; rather than detecting SNPs across the entire genome, cgMLST types isolates based on the alleles present at > 1,000 genes. Overall, the cgMLST relationships depicted in the MST align well with those indicated by pairwise SNP distances with some minor discrepancies. For example, isolate 406 has the same cgMLST sequence type as isolate 1363 but not isolate 1433. However, by pairwise SNP comparison (CFSAN, reference-based alignment; SKA, split k-mer analysis), isolate 406 is more closely related to isolate 1433 (5 SNPs different by CFSAN and SKA) than isolate 1363 (7 SNPs different by CFSAN, 9 by SKA). Although closely related to isolates 1363 and 1433, isolate 406 is not included in cluster 4 due to lack of identifiable epidemiologic links. For the same reason, isolate 1434 is not included in cluster 2 and isolate 1313 is not included in cluster 3.

We identified 82 isolate pairs separated by ≤ 15 SNPs by both CFSAN and SKA (Figure 1C) and found 33 (40.2%) with epidemiologic links (Figure 1D). The double threshold resulted in flagging of 34 fewer pairs compared to CFSAN alone and one fewer compared to SKA alone and slightly enriched for pairs with identifiable epidemiologic links (Figure 1C, Figure s1). The number of pairs with epidemiologic links generally decreased with increasing SNP distance (Figure 1D–E). Among pairs separated by ≤ 15 SNPs, CFSAN and SKA SNP distance distributions were significantly different for pairs with epidemiologic links vs pairs without (medians four vs six by CFSAN and five vs eight by SKA) (Figure 1D–E). A chi-square test for trend performed on pairs separated into four SNP distance bins demonstrated a significant linear relationship between CFSAN SNP distance and proportion of pairs with epidemiologic links (p = 0.0046; Figure 1F) with a similar but nonsignificant trend for SKA (p = 0.0531; Figure 1G).

Epidemiologic analysis placed the 33 pairs with epidemiologic links into 16 putative transmission clusters of two to four isolates each (Figure 1H, Figure 2B). These clusters involved 41/156 isolates (26.3%) with a similar sequence type distribution to that of all isolates (Figure 1B, Figure 2).

VREfm WGS supports infection control investigations

Twelve isolates comprising four sets of three were sequenced at IC’s request as part of outbreak investigations (reactive WGS). For IC sets one and two, WGS ruled out clonal relationships (Figure 3B). For IC sets three and four, WGS supported a clonal relationship between two of the isolates (Set 3: 1176 and 1178, Set 4: 1174 and 1177) with the third more distantly related. In IC set three (Figure 3C), the third isolate (1194) belonged to a different ST, while in IC set four (Figure 3D), the third isolate (1175) belonged to the same ST but was separated from the other two isolates by > 70 SNPs.

Figure 3. Reactive and surveillance whole-genome sequencing (WGS) of vancomycin-resistant Enterococcus faecium (VREfm) supports infection control investigations.

Figure 3.

Open in a new tab

A) Key to maps of putative transmission clusters shown in Figures 3–6 and Figure s2. In each map, the lefthand side shows the single nucleotide polymorphism distances between each member of the cluster (CFSAN, reference-based alignment; SKA, split k-mer anlaysis), while the righthand side shows the epidemiologic links between the patients from which the isolates were recovered. Each square represents one day, and sets of contiguous squares represent admissions. The color of each square represents the category of epidemiologic (epi) connection between or among patients in the cluster on that day. Squares in some maps are broken up by sets of five dots against a white background, which represent continuous stretches of time without notable changes to patient location. Patient location during each admission is shown in white rectangles above the square(s) with the admitting service in parenthesis. Units were assigned arbitrary numbers. Vertical lines marked with day numbers demarcate location changes or vancomycin-resistant enterococci (VRE) screens/isolation events and connect to the relevant patient’s timeline. For VRE screen or isolation events, circles above the vertical line represent the sample type. Sequenced VREfm isolates are indicated by red outlines on the sample type circle, red day numbers, and red vertical lines. B) Infection control (IC) sets 1 and 2. Each set contained three isolates for which sequencing was requested by IC due to suspected involvement in a transmission cluster, and surveillance linked one member of each set to another isolate. C) and D) Maps for IC set 3/transmission cluster 1 and IC set 4/transmission cluster 2. In each set, reactive WGS supported a clonal relationship between two isolates who were admitted to the same unit at the same time and ruled out involvement of a third isolate (patient ID with gray background), while surveillance WGS identified an additional transmission cluster member. GI surg, gastrointestinal surgery. Int, intensive care. NS, neurosurgery. Onc, oncology. PACU, post-anesthesia care unit. E) and F) Maps for the reactively sequenced isolates from IC sets 1 and 2 that were linked to other isolates sequenced for surveillance. Card surg, cardiac surgery. ED, emergency department. ES, endoscopy. IR, interventional radiology. Rehab, Brigham and Women’s Hospital (BWH)-affiliated inpatient rehabilitation facility (units numbered separately from BWH units). Thorac surg, thoracic surgery. Vent, ventilator service.

Surveillance WGS combined with epidemiologic analysis identified additional members of IC sets three and four that would have otherwise been missed, likely due to spatiotemporal separation from the other patients (Figures 3C, 3D). One isolate from each of IC sets one (1445) and two (1363) were also placed into separate transmission clusters (Figure 3B, E, F).

Surveillance WGS of VREfm detects transmission clusters missed by traditional surveillance measures

Surveillance WGS detected 14 putative transmission clusters missed by traditional surveillance measures (Figures 3E–F, 4, 5; Fig S2). Two clusters demonstrate putative transmission between patients admitted to the same service at the same time (Figure 4). The others involve more complicated epidemiologic relationships. For example, the two patients involved in cluster 7 (Figure 5A) were on the same unit at different times (patient 1133 was admitted to unit 21 seven days after patient 1140 had left) and also concurrently on the same services (intensive care then medicine from days 15–23) while on different units. VREfm could have been transmitted between or to the patients from a common provider or piece of equipment in a chain involving an unsampled third patient or the unit 21 environment.

Figure 4. Surveillance whole-genome sequencing of vancomycin-resistant Enterococcus faecium detects transmission between patients admitted to the same service at the same time.

Figure 4.

Open in a new tab

Refer to Figure 3A for key to transmission cluster maps. ED, emergency department. Int, intensive care. Onc, oncology. Thorac surg, thoracic surgery.

Figure 5. Surveillance whole-genome sequencing of vancomycin-resistant Enterococcus faecium detects transmission among patients with complex epidemiologic relationships.

Figure 5.

Open in a new tab

Refer to Figure 3A for key to transmission cluster maps. AH, BWH-affiliated partner hospital (units numbered separately from BWH units). BMT, bone marrow transplant. Burn, burn service. Card, cardiology. ED, emergency department. Int, intensive care. Med, medicine. PACU, post-anesthesia care unit. VS, vascular surgery. In C, “outside hospital” refers to the same facility. See Figure s2 for additional transmission cluster maps.

Other clusters detected by surveillance WGS included cases where the positive VREfm result for one of the patients occurred during an admission following the one in which transmission likely occurred. In Figure 5B, patients 1134 and 1376 were both admitted to the bone marrow transplant service from days 7–15 and to oncology unit 1 from days 17 to 21, when patient 1376 was discharged. Patient 1376 was readmitted on day 57, and their VRE screen returned positive, possibly reflecting colonization acquired during one of their two previous admissions via contact with patient 1134, common hospital staff or equipment, or the oncology unit environment.

Many putative transmission clusters demonstrated extended temporal separation between isolate recovery. For example, isolate 1424 (Figure 3D) was recovered more than three months before the other two members of cluster 2. In Figure 3E, nearly seven months separate recovery of the two isolates with multiple epidemiologic links between the corresponding patients. This is consistent with reports describing prolonged VRE survival on environmental surfaces.7–9

Finally, one cluster of two isolates likely involved transmission at a common outside facility from which both patients were transferred before VRE screening cultures returned positive at our hospital (Figure 5C). Although the patients were concurrently admitted to BWH, they had no clear epidemiologic links during that period. Similarly, in Figure 5D, transmission might have occurred when the three patients were admitted to the same unit at an outside institution, although these patients had additional links during their subsequent overlapping BWH admissions.

Surveillance WGS of VREfm identifies the post anesthesia care unit (PACU) as a transmission locus

A previous report used VRE WGS to uncover a VREfm transmission locus.20 Our study identified PACU exposure as an epidemiologic link for six pairs of patients across four clusters. In cluster 2 (Figure 3D), a surgical tissue culture from patient 1424 grew VREfm. Three months later, two closely related isolates were recovered from patient 1174, who had visited the PACU twice prior to VREfm isolation, and patient 1177, who had no identifiable epidemiologic relationship to patient 1424 but was on the same inpatient unit (oncology unit 10) and service (oncology) as patient 1177. A potential transmission route was from 1424 to 1174 via the PACU and then from 1174 to 1177 via the oncology unit environment or a shared provider.

In another example (Figure 6A), a plausible chain involves contamination of the PACU by patient 1419 on day three with an isolate that was then picked up by patient 621 on day 18. In a third PACU-associated cluster (Figure 6B), patient 538 visited the PACU on day 15, the same day the patient’s VRE screen returned positive, and day 42. Meanwhile, patient 605 visited the PACU on day 43, likely resulting in colonization with VREfm (detected on day 51) from patient 538. Finally, in cluster 13 (Figure 6C), patient 1272 might have become colonized with VREfm left behind in the PACU several months prior by patient 1195, while patient 1387 could have picked up VREfm from either patient 1195 or 1272.

Figure 6. Surveillance whole-genome sequencing of vancomycin-resistant Enterococcus faecium (VREfm) identifies the post-anesthesia care unit (PACU) as a hot spot for transmission.

Figure 6.

Open in a new tab

Refer to Figure 3A for key to transmission cluster maps. BMT, bone marrow transplant. Card surg, cardiac surgery. ED, emergency department. ES, endoscopy. Heme, hematology. Med, medicine. Neuro, neurology. NS, neurosurgery. Onc, oncology. Ortho, orthopedic surgery. Thorac surg, thoracic surgery. VS, vascular surgery.

DISCUSSION

Building on previous reports,14,19,21 our analysis shows that WGS for VREfm surveillance is an effective tool for identifying transmission events in the hospital setting. Fourteen of the 16 transmission clusters we identified were not detected by traditional methods, and surveillance WGS uncovered an additional member of the two recognized clusters. In addition, 24.3% of 144 isolates sequenced for surveillance were part of a cluster, indicating undetected transmission events are common; since only 60.8% of total screening isolates were available for inclusion, this likely underestimates the true extent of transmission. Factors that made these transmission events difficult to detect via traditional methods include temporal separation of up to several months between isolate recovery; recovery during an admission following the transmission event; and transmission in areas not previously suspected to pose a high risk of spreading VREfm, namely the PACU.

The PACU services both outpatients and inpatients and is a high-volume, high-turnover area with dedicated anesthesiology and nursing teams. Although the potential for spread of infectious diseases in the PACU is recognized,30 to our knowledge VRE transmission in the PACU has not previously been reported. Many patients requiring surgery are vulnerable to VRE colonization or infection due to severe underlying illness and/or perioperative antibiotic treatment. Features of the PACU that promote transmission among vulnerable patients include high turnover rates, which complicate consistent disinfection between patients, and receipt of care in open bays separated only by curtains with inconsistent availability of hand hygiene supplies, potentially hampering adherence to hand hygiene guidelines. Our findings of multiple VREfm transmission events linked to the PACU could inform targeted interventions, such as increased monitoring of hand hygiene and equipment disinfection adherence rates, more frequent environmental disinfection, and possibly pre-operative VRE screening to trigger contact precautions in the PACU.

Limitations of this study include incomplete sampling and lack of environmental sampling. Sequencing only 60.8% of screening isolates during the main collection period likely limited detection of transmission events and raises the possibility that some of the identified transmission clusters—for example, those with long intervals between cases—are incomplete. Furthermore, while the goal of this study was not to assess risk factors for VRE colonization, the gaps in sampling limited our ability to identify patient factors that might facilitate transmission events; such information could help prioritize patients for genomic screening. In addition, our epidemiologic analysis was restricted to manual analysis of high-level data comprising patient service, unit, and bed assignments during admission to hospitals within our network. Inclusion of more detailed information, such as provider identities and dates of interaction and dates of imaging and bedside procedures involving reusable equipment, could improve our ability to identify clusters. Automated tools to mine this information from the EMR31,32 could facilitate these investigations.

An additional limitation is the retrospective nature of the study, which precluded communication of the results to IC and clinical teams in real time for immediate intervention. However, the findings provide impetus to engage in prospective surveillance to inform and assess the efficacy of interventions. Newer sequencing platforms, such as the Oxford Nanopore, and automated tools to evaluate epidemiologic linkage could reduce turnaround time for transmission chain identification.

Other challenges include selecting a method for assessing genomic relatedness and determining the degree of genomic relatedness that warrants epidemiologic investigation. This can be particularly challenging for pathogens like VREfm that comprise epidemic healthcare-associated clones15,22,33; the highly homogeneous population structures of such clones can be explored using NCBI’s Pathogen Detection database (https://www.ncbi.nlm.nih.gov/pathogens/), which shows that the isolates from our study are most closely related to other isolates from BWH and another institution in Boston. Both CFSAN and SKA have been used for comparison of VREfm genomes, the former more commonly. Here, we implemented both methods with an empiric double threshold of ≤ 15 SNPs. As only one pair was flagged by SKA but not CFSAN, the double threshold was effectively equivalent to a SKA threshold alone. Higgs and colleagues found that compared to CFSAN SNP distances, SKA SNP distances correlated more closely with those calculated by a gold-standard method (pairwise comparison using de novo references).22. Thus, SKA SNP distances might better reflect genomic relatedness in VREfm, and pairwise comparison by SKA could enrich pairs flagged for investigation for true transmission events.34

Importantly, our SNP threshold of 15 is meant not to indicate definite evidence of transmission but to identify pairs warranting further epidemiologic investigation. We demonstrated a linear relationship between SNP distance and proportion of epidemiologically linked pairs, suggesting that establishing SNP ranges that correlate with different probabilities of epidemiologic linkage could be a practical strategy to triage pairs for epidemiologic investigation in WGS surveillance programs. Our initial data suggest a clear divide between 0–11 SNPs and 12–15 SNPs by CFSAN, with pairs in the latter category less likely to have identifiable epidemiologic connections. A larger dataset collected via a comprehensive WGS VRE surveillance program could help further refine these risk categories, including for SNP distances calculated by SKA.

Finally, cost remains a barrier to implementation of hospital-based pathogen genomic surveillance. While a comprehensive cost-benefit analysis is outside the scope of this study, Kumar and colleagues have proposed that bacterial WGS surveillance programs have an 80% chance of being cost effective if hospital systems are willing to pay $2,400 per averted transmission.35 One Canadian study estimates that an averted transmission could save about $16,000 on average,36 reflecting the costs of longer hospital stays, higher mortality, antibiotic treatment, and implementation of contact precautions. Other recent analyses also support a positive cost-to-benefit ratio.35,37–39

Overall, our results demonstrate that WGS of diagnostic and screening VREfm isolates can detect transmission clusters missed by traditional IC methods. If obtained prospectively, such information could spur interventions to halt ongoing outbreaks and inform changes to hospital IC strategy to better protect patients from the harmful consequences of VRE colonization and infection.

Supplementary Material

Table s2
Table s1
Table s3
Supplementary methods, table legends, and figures

ACKNOWLEDGEMENTS

The authors would like to thank the hard-working staff in the BWH microbiology laboratory who recovered the isolates included in this study.

Financial support.

This study was funded by the BWH Department of Pathology. The work of Jay Worley was supported by the National Center for Biotechnology Information of the National Library of Medicine, and the National Institute of Allergy and Infectious Diseases, National Institutes of Health.

Footnotes

Potential conflicts of interest. All authors report no conflicts of interest relevant to this article.

Competing interests. The authors declare none.

REFERENCES

  • 1.CDC. Vancomycin-resistant Enterococci (VRE) Basics. Vancomycin-resistant Enterococci (VRE). June 28, 2024. Accessed August 28, 2024. https://www.cdc.gov/vre/about/index.html [Google Scholar]
  • 2.Puchter L, Chaberny IF, Schwab F, Vonberg RP, Bange FC, Ebadi E. Economic burden of nosocomial infections caused by vancomycin-resistant enterococci. Antimicrob Resist Infect Control. 2018;7:1. doi: 10.1186/s13756-017-0291-z [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Prematunge C, MacDougall C, Johnstone J, et al. VRE and VSE Bacteremia Outcomes in the Era of Effective VRE Therapy: A Systematic Review and Meta-analysis. Infect Control Hosp Epidemiol. 2016;37(1):26–35. doi: 10.1017/ice.2015.228 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Meschiari M, Kaleci S, Monte MD, et al. Vancomycin resistant enterococcus risk factors for hospital colonization in hematological patients: a matched case-control study. Antimicrob Resist Infect Control. 2023;12:126. doi: 10.1186/s13756-023-01332-x [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Alevizakos M, Gaitanidis A, Nasioudis D, Tori K, Flokas ME, Mylonakis E. Colonization With Vancomycin-Resistant Enterococci and Risk for Bloodstream Infection Among Patients With Malignancy: A Systematic Review and Meta-Analysis. Open Forum Infectious Diseases. 2017;4(1):ofw246. doi: 10.1093/ofid/ofw246 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Arias CA, Murray BE. The rise of the Enterococcus: beyond vancomycin resistance. Nat Rev Microbiol. 2012;10(4):266–278. doi: 10.1038/nrmicro2761 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Wendt C, Wiesenthal B, Dietz E, Rüden H. Survival of Vancomycin-Resistant and Vancomycin-Susceptible Enterococci on Dry Surfaces. J Clin Microbiol. 1998;36(12):3734–3736. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Neely AN, Maley MP. Survival of Enterococci and Staphylococci on Hospital Fabrics and Plastic. J Clin Microbiol. 2000;38(2):724–726. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Porter L, Sultan O, Mitchell BG, et al. How long do nosocomial pathogens persist on inanimate surfaces? A scoping review. Journal of Hospital Infection. 2024;147:25–31. doi: 10.1016/j.jhin.2024.01.023 [DOI] [PubMed] [Google Scholar]
  • 10.Byers KE, Durbin LJ, Simonton BM, Anglim AM, Adal KA, Farr BM. Disinfection of hospital rooms contaminated with vancomycin-resistant Enterococcus faecium. Infect Control Hosp Epidemiol. 1998;19(4):261–264. doi: 10.1086/647806 [DOI] [PubMed] [Google Scholar]
  • 11.Tan C, Linkenheld-Struk A, Williams V, et al. The role of the environment in transmission of vancomycin-resistant Enterococcus: A proof-of-concept study. Antimicrob Steward Healthc Epidemiol. 2022;2(1):e178. doi: 10.1017/ash.2022.318 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Permana B, Harris PNA, Runnegar N, et al. Using Genomics To Investigate an Outbreak of Vancomycin-Resistant Enterococcus faecium ST78 at a Large Tertiary Hospital in Queensland. Microbiol Spectr. 2023;11(3):e0420422. doi: 10.1128/spectrum.04204-22 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Abdelbary MHH, Senn L, Greub G, Chaillou G, Moulin E, Blanc DS. Whole-genome sequencing revealed independent emergence of vancomycin-resistant Enterococcus faecium causing sequential outbreaks over 3 years in a tertiary care hospital. Eur J Clin Microbiol Infect Dis. 2019;38(6):1163–1170. doi: 10.1007/s10096-019-03524-z [DOI] [PubMed] [Google Scholar]
  • 14.Sherry NL, Gorrie CL, Kwong JC, et al. Multi-site implementation of whole genome sequencing for hospital infection control: A prospective genomic epidemiological analysis. Lancet Reg Health West Pac. 2022;23:100446. doi: 10.1016/j.lanwpc.2022.100446 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Pinholt M, Larner-Svensson H, Littauer P, et al. Multiple hospital outbreaks of vanA Enterococcus faecium in Denmark, 2012–13, investigated by WGS, MLST and PFGE. J Antimicrob Chemother. 2015;70(9):2474–2482. doi: 10.1093/jac/dkv142 [DOI] [PubMed] [Google Scholar]
  • 16.Brodrick HJ, Raven KE, Harrison EM, et al. Whole-genome sequencing reveals transmission of vancomycin-resistant Enterococcus faecium in a healthcare network. Genome Med. 2016;8(1):4. doi: 10.1186/s13073-015-0259-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Ward DV, Hoss AG, Kolde R, et al. Integration of genomic and clinical data augments surveillance of healthcare-acquired infections. Infect Control Hosp Epidemiol. 2019;40(6):649–655. doi: 10.1017/ice.2019.75 [DOI] [PubMed] [Google Scholar]
  • 18.Eichel V, Klein S, Bootsveld C, et al. Challenges in interpretation of WGS and epidemiological data to investigate nosocomial transmission of vancomycin-resistant Enterococcus faecium in an endemic region: incorporation of patient movement network and admission screening. J Antimicrob Chemother. 2020;75(7):1716–1721. doi: 10.1093/jac/dkaa122 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Sundermann AJ, Srinivasa VR, Mills EG, et al. Genomic sequencing surveillance of patients colonized with vancomycin-resistant Enterococcus (VRE) improves detection of hospital-associated transmission. Published online May 3, 2024:2024.05.01.24306710. doi: 10.1101/2024.05.01.24306710 [DOI] [Google Scholar]
  • 20.Sundermann AJ, Babiker A, Marsh JW, et al. Outbreak of Vancomycin-resistant Enterococcus faecium in Interventional Radiology: Detection Through Whole-genome Sequencing-based Surveillance. Clin Infect Dis. 2020;70(11):2336–2343. doi: 10.1093/cid/ciz666 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Gouliouris T, Coll F, Ludden C, et al. Quantifying acquisition and transmission of Enterococcus faecium using genomic surveillance. Nat Microbiol. 2021;6(1):103–111. doi: 10.1038/s41564-020-00806-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Higgs C, Sherry NL, Seemann T, et al. Optimising genomic approaches for identifying vancomycin-resistant Enterococcus faecium transmission in healthcare settings. Nat Commun. 2022;13(1):509. doi: 10.1038/s41467-022-28156-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30(15):2114–2120. doi: 10.1093/bioinformatics/btu170 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Bankevich A, Nurk S, Antipov D, et al. SPAdes: A New Genome Assembly Algorithm and Its Applications to Single-Cell Sequencing. Journal of Computational Biology. 2012;19(5):455–477. doi: 10.1089/cmb.2012.0021 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Gurevich A, Saveliev V, Vyahhi N, Tesler G. QUAST: quality assessment tool for genome assemblies. Bioinformatics. 2013;29(8):1072–1075. doi: 10.1093/bioinformatics/btt086 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Roosaare M, Vaher M, Kaplinski L, et al. StrainSeeker: fast identification of bacterial strains from raw sequencing reads using user-provided guide trees. PeerJ. 2017;5:e3353. doi: 10.7717/peerj.3353 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Gupta A, Jordan IK, Rishishwar L. stringMLST: a fast k-mer based tool for multilocus sequence typing. Bioinformatics. 2017;33(1):119–121. doi: 10.1093/bioinformatics/btw586 [DOI] [PubMed] [Google Scholar]
  • 28.Davis S, Pettengill JB, Luo Y, et al. CFSAN SNP Pipeline: an automated method for constructing SNP matrices from next-generation sequence data. PeerJ Comput Sci. 2015;1:e20. doi: 10.7717/peerj-cs.20 [DOI] [Google Scholar]
  • 29.Harris SR. SKA: Split Kmer Analysis Toolkit for Bacterial Genomic Epidemiology. Published online October 25, 2018:453142. doi: 10.1101/453142 [DOI] [Google Scholar]
  • 30.Badgwell JM. The postanesthesia care unit: a high-risk environment for bloodborne and infectious respiratory pathogens. J Post Anesth Nurs. 1996;11(2):66–70. doi: 10.1016/s1089-9472(06)80004-3 [DOI] [PubMed] [Google Scholar]
  • 31.Miller JK, Chen J, Sundermann A, et al. Statistical outbreak detection by joining medical records and pathogen similarity. Journal of Biomedical Informatics. 2019;91:103126. doi: 10.1016/j.jbi.2019.103126 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Kanjilal S The modern alchemy of clinical pathology: turning the output of microbiology laboratory operations into gold. Journal of Clinical Microbiology. 2024;62(5):e01709–22. doi: 10.1128/jcm.01709-22 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Pecora N, Holzbauer S, Wang X, et al. Genomic Analysis of Clostridioides difficile in 2 Regions of the United States Reveals a Diversity of Strains and Limited Transmission. J Infect Dis. 2022;225(1):121–129. doi: 10.1093/infdis/jiab294 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Maechler F, Weber A, Schwengers O, et al. Split k-mer analysis compared to cgMLST and SNP-based core genome analysis for detecting transmission of vancomycin-resistant enterococci: results from routine outbreak analyses across different hospitals and hospitals networks in Berlin, Germany. Microbial Genomics. 2023;9(1):000937. doi: 10.1099/mgen.0.000937 [DOI] [Google Scholar]
  • 35.Kumar P, Sundermann AJ, Martin EM, et al. Method for Economic Evaluation of Bacterial Whole Genome Sequencing Surveillance Compared to Standard of Care in Detecting Hospital Outbreaks. Clinical Infectious Diseases. 2021;73(1):e9–e18. doi: 10.1093/cid/ciaa512 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Lloyd-Smith P, Younger J, Lloyd-Smith E, Green H, Leung V, Romney MG. Economic analysis of vancomycin-resistant enterococci at a Canadian hospital: assessing attributable cost and length of stay. Journal of Hospital Infection. 2013;85(1):54–59. doi: 10.1016/j.jhin.2013.06.016 [DOI] [PubMed] [Google Scholar]
  • 37.Price V, Ngwira LG, Lewis JM, et al. A systematic review of economic evaluations of whole-genome sequencing for the surveillance of bacterial pathogens. Microb Genom. 2023;9(2):mgen000947. doi: 10.1099/mgen.0.000947 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Gordon LG, Elliott TM, Forde B, et al. Budget impact analysis of routinely using whole-genomic sequencing of six multidrug-resistant bacterial pathogens in Queensland, Australia. BMJ Open. 2021;11(2):e041968. doi: 10.1136/bmjopen-2020-041968 [DOI] [Google Scholar]
  • 39.Mellmann A, Bletz S, Böking T, et al. Real-Time Genome Sequencing of Resistant Bacteria Provides Precision Infection Control in an Institutional Setting. Journal of Clinical Microbiology. 2016;54(12):2874–2881. doi: 10.1128/jcm.00790-16 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Table s2
NIHMS2152860-supplement-Table_s2.xlsx (28.1KB, xlsx)
Table s1
NIHMS2152860-supplement-Table_s1.xlsx (31.4KB, xlsx)
Table s3
NIHMS2152860-supplement-Table_s3.xlsx (309.8KB, xlsx)
Supplementary methods, table legends, and figures
NIHMS2152860-supplement-Supplementary_methods__table_legends__and_figures.docx (2.2MB, docx)

RESOURCES