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Karunakaran S, Pless LL, Ayres AM, Ciccone C, Penzelik J, Sundermann AJ, Martin EM, Griffith MP, Waggle K, Hodges JC, Harrison LH, Snyder GM. Impact of discontinuation of contact precautions on surveillance- and whole genome sequencing-defined methicillin-resistant Staphylococcus aureus healthcare-associated infections. ANTIMICROBIAL STEWARDSHIP & HEALTHCARE EPIDEMIOLOGY : ASHE 2024; 4:e97. [PMID: 38836046 PMCID: PMC11149034 DOI: 10.1017/ash.2024.89] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Revised: 04/19/2024] [Accepted: 04/23/2024] [Indexed: 06/06/2024]
Abstract
Objective Prior studies evaluating the impact of discontinuation of contact precautions (DcCP) on methicillin-resistant Staphylococcus aureus (MRSA) outcomes have characterized all healthcare-associated infections (HAIs) rather than those likely preventable by contact precautions. We aimed to analyze the impact of DcCP on the rate of MRSA HAI including transmission events identified through whole genome sequencing (WGS) surveillance. Design Quasi experimental interrupted time series. Setting Acute care medical center. Participants Inpatients. Methods The effect of DcCP (use of gowns and gloves) for encounters among patients with MRSA carriage was evaluated using time series analysis of MRSA HAI rates from January 2019 through December 2022, compared to WGS-defined attributable transmission events before and after DcCP in December 2020. Results The MRSA HAI rate was 4.22/10,000 patient days before and 2.98/10,000 patient days after DcCP (incidence rate ratio [IRR] 0.71 [95% confidence interval 0.56-0.89]) with a significant immediate decrease (P = .001). There were 7 WGS-defined attributable transmission events before and 11 events after DcCP (incident rate ratio 0.90 [95% confidence interval 0.30-2.55]). Conclusions DcCP did not result in an increase in MRSA HAI or, in WGS-defined attributable transmission events. Comprehensive analyses of the effect of transmission prevention measures should include outcomes specifically measuring transmission-associated HAI.
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Affiliation(s)
- Sharon Karunakaran
- Division of Pediatric Infectious Diseases, Children’s Hospital of Pittsburgh, Pittsburgh, PA, USA
| | - Lora Lee Pless
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- Microbial Genomics Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ashley M. Ayres
- Department of Infection Prevention and Control, UPMC Presbyterian/Shadyside, Pittsburgh, PA, USA
| | - Carl Ciccone
- Department of Infection Prevention and Control, UPMC Presbyterian/Shadyside, Pittsburgh, PA, USA
| | - Joseph Penzelik
- Department of Infection Prevention and Control, UPMC Presbyterian/Shadyside, Pittsburgh, PA, USA
| | - Alexander J. Sundermann
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- Microbial Genomics Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Elise M. Martin
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- Veterans’ Affairs Pittsburgh Healthcare System, Pittsburgh, PA, USA
| | - Marissa P. Griffith
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- Microbial Genomics Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Kady Waggle
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- Microbial Genomics Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, Pittsburgh, PA, USA
| | | | - Lee H. Harrison
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- Microbial Genomics Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Graham M. Snyder
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- Department of Infection Prevention and Control, UPMC Presbyterian/Shadyside, Pittsburgh, PA, USA
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2
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Valek AL, Srinivasa VR, Ayres AM, Cheung S, Harrison LH, Snyder GM. Incidence and transmission associated with respiratory viruses in an acute care facility: An observational study. Infect Control Hosp Epidemiol 2024; 45:774-776. [PMID: 38351601 PMCID: PMC11102818 DOI: 10.1017/ice.2024.25] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 11/30/2023] [Accepted: 01/01/2024] [Indexed: 05/18/2024]
Abstract
We estimated the extent of respiratory virus transmission over three pre-COVID-19 seasons. Of 16,273 assays, 22.9% (3,726) detected ≥1 respiratory virus. The frequency of putatively hospital-acquired infection ranged from 6.9% (influenza A/B) to 24.7% (adenovirus). The 176 clusters were most commonly associated with rhinovirus/enterovirus (70) and influenza A/B (62).
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Affiliation(s)
- Abby L. Valek
- Department of Infection Prevention and Control, UPMC Presbyterian, Pittsburgh, PA, USA
| | - Vatsala Rangachar Srinivasa
- Division of Infectious Diseases, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ashley M. Ayres
- Department of Infection Prevention and Control, UPMC Presbyterian, Pittsburgh, PA, USA
| | - Steven Cheung
- School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Lee H. Harrison
- Division of Infectious Diseases, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Epidemiology, School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Graham M. Snyder
- Department of Infection Prevention and Control, UPMC Presbyterian, Pittsburgh, PA, USA
- Division of Infectious Diseases, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
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3
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Sundermann AJ, Rangachar Srinivasa V, Mills EG, Griffith MP, Evans E, Chen J, Waggle KD, Snyder GM, Pless LL, Harrison LH, Van Tyne D. Genomic sequencing surveillance of patients colonized with vancomycin-resistant Enterococcus (VRE) improves detection of hospital-associated transmission. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.01.24306710. [PMID: 38746387 PMCID: PMC11092704 DOI: 10.1101/2024.05.01.24306710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Background Vancomycin-resistant enterococcal (VRE) infections pose significant challenges in healthcare. Transmission dynamics of VRE are complex, often involving patient colonization and subsequent transmission through various healthcare-associated vectors. We utilized a whole genome sequencing (WGS) surveillance program at our institution to better understand the contribution of clinical and colonizing isolates to VRE transmission. Methods We performed whole genome sequencing on 352 VRE clinical isolates collected over 34 months and 891 rectal screening isolates collected over a 9-month nested period, and used single nucleotide polymorphisms to assess relatedness. We then performed a geo-temporal transmission analysis considering both clinical and rectal screening isolates compared with clinical isolates alone, and calculated 30-day outcomes of patients. Results VRE rectal carriage constituted 87.3% of VRE acquisition, with an average monthly acquisition rate of 7.6 per 1000 patient days. We identified 185 genetically related clusters containing 2-42 isolates and encompassing 69.6% of all isolates in the dataset. The inclusion of rectal swab isolates increased the detection of clinical isolate clusters (from 53% to 67%, P<0.01). Geo-temporal analysis identified hotspot locations of VRE transmission. Patients with clinical VRE isolates that were closely related to previously sampled rectal swab isolates experienced 30-day ICU admission (17.5%), hospital readmission (9.2%), and death (13.3%). Conclusions Our findings describe the high burden of VRE transmission at our hospital and shed light on the importance of using WGS surveillance of both clinical and rectal screening isolates to better understand the transmission of this pathogen. This study highlights the potential utility of incorporating WGS surveillance of VRE into routine hospital practice for improving infection prevention and patient safety.
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4
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Raabe NJ, Valek AL, Griffith MP, Mills E, Waggle K, Srinivasa VR, Ayres AM, Bradford C, Creager HM, Pless LL, Sundermann AJ, Van Tyne D, Snyder GM, Harrison LH. Real-time genomic epidemiologic investigation of a multispecies plasmid-associated hospital outbreak of NDM-5-producing Enterobacterales infections. Int J Infect Dis 2024; 142:106971. [PMID: 38373647 PMCID: PMC11055495 DOI: 10.1016/j.ijid.2024.02.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Revised: 02/05/2024] [Accepted: 02/14/2024] [Indexed: 02/21/2024] Open
Abstract
OBJECTIVES New Delhi metallo-β-lactamase (NDM) is an emergent mechanism of carbapenem resistance associated with high mortality and limited treatment options. Because the blaNDM resistance gene is often carried on plasmids, traditional infection prevention and control (IP&C) surveillance methods and reactive whole genome sequencing (WGS) may not detect plasmid transfer in multispecies outbreaks. METHODS Initial outbreak detection of NDM-producing Enterobacterales identified at an acute care hospital occurred via traditional IP&C methods and was supplemented by real-time WGS surveillance performed weekly. To resolve NDM-encoding plasmids, we performed long-read sequencing and constructed hybrid assemblies. WGS data for suspected outbreaks was shared with the IP&C team for assessment and intervention. RESULTS We observed a multispecies outbreak of NDM-5-producing Enterobacterales isolated from 15 patients between February 2021 and February 2023. The 19 clinical and surveillance isolates sequenced included 7 bacterial species encoding the same NDM-5 plasmid. WGS surveillance and epidemiologic investigation characterized 10 horizontal plasmid transfer events and 6 bacterial transmission events between patients in varying hospital units. CONCLUSIONS Our investigation revealed a complex, multispecies outbreak of NDM involving multiple plasmid transfer and bacterial transmission events. We highlight the utility of combining traditional IP&C and prospective genomic methods in identifying and containing plasmid-associated outbreaks.
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Affiliation(s)
- Nathan J Raabe
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, Pittsburgh, PA, USA; Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Department of Epidemiology, School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Abby L Valek
- Department of Infection Control and Hospital Epidemiology, UPMC Presbyterian, Pittsburgh, PA, USA
| | - Marissa P Griffith
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, Pittsburgh, PA, USA; Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Emma Mills
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Kady Waggle
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, Pittsburgh, PA, USA; Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Vatsala Rangachar Srinivasa
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, Pittsburgh, PA, USA; Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Department of Epidemiology, School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA
| | - Ashley M Ayres
- Department of Infection Control and Hospital Epidemiology, UPMC Presbyterian, Pittsburgh, PA, USA
| | - Claire Bradford
- Department of Infection Control and Hospital Epidemiology, UPMC Presbyterian, Pittsburgh, PA, USA
| | - Hannah M Creager
- Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA; Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Lora L Pless
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, Pittsburgh, PA, USA; Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Alexander J Sundermann
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, Pittsburgh, PA, USA; Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Daria Van Tyne
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Graham M Snyder
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Department of Infection Control and Hospital Epidemiology, UPMC Presbyterian, Pittsburgh, PA, USA
| | - Lee H Harrison
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, Pittsburgh, PA, USA; Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Department of Epidemiology, School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA.
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5
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Blane B, Raven KE, Brown NM, Harrison EM, Coll F, Thaxter R, Enoch DA, Gouliouris T, Leek D, Girgis ST, Akram A, Matuszewska M, Rhodes P, Parkhill J, Peacock SJ. Evaluating the impact of genomic epidemiology of methicillin-resistant Staphylococcus aureus (MRSA) on hospital infection prevention and control decisions. Microb Genom 2024; 10. [PMID: 38630616 DOI: 10.1099/mgen.0.001235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/19/2024] Open
Abstract
Genomic epidemiology enhances the ability to detect and refute methicillin-resistant Staphylococcus aureus (MRSA) outbreaks in healthcare settings, but its routine introduction requires further evidence of benefits for patients and resource utilization. We performed a 12 month prospective study at Cambridge University Hospitals NHS Foundation Trust in the UK to capture its impact on hospital infection prevention and control (IPC) decisions. MRSA-positive samples were identified via the hospital microbiology laboratory between November 2018 and November 2019. We included samples from in-patients, clinic out-patients, people reviewed in the Emergency Department and healthcare workers screened by Occupational Health. We sequenced the first MRSA isolate from 823 consecutive individuals, defined their pairwise genetic relatedness, and sought epidemiological links in the hospital and community. Genomic analysis of 823 MRSA isolates identified 72 genetic clusters of two or more isolates containing 339/823 (41 %) of the cases. Epidemiological links were identified between two or more cases for 190 (23 %) individuals in 34/72 clusters. Weekly genomic epidemiology updates were shared with the IPC team, culminating in 49 face-to-face meetings and 21 written communications. Seventeen clusters were identified that were consistent with hospital MRSA transmission, discussion of which led to additional IPC actions in 14 of these. Two outbreaks were also identified where transmission had occurred in the community prior to hospital presentation; these were escalated to relevant IPC teams. We identified 38 instances where two or more in-patients shared a ward location on overlapping dates but carried unrelated MRSA isolates (pseudo-outbreaks); research data led to de-escalation of investigations in six of these. Our findings provide further support for the routine use of genomic epidemiology to enhance and target IPC resources.
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Affiliation(s)
- Beth Blane
- Department of Medicine, University of Cambridge, Box 157 Addenbrooke's Hospital, Hills Road, Cambridge, UK
| | - Kathy E Raven
- Department of Medicine, University of Cambridge, Box 157 Addenbrooke's Hospital, Hills Road, Cambridge, UK
| | - Nicholas M Brown
- Clinical Microbiology and Public Health Laboratory, UK Health Security Agency, Addenbrooke's Hospital, Cambridge, UK
| | - Ewan M Harrison
- Department of Medicine, University of Cambridge, Box 157 Addenbrooke's Hospital, Hills Road, Cambridge, UK
- Wellcome Sanger Institute, Hinxton, Cambridge, UK
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Francesc Coll
- Wellcome Sanger Institute, Hinxton, Cambridge, UK
- Department of Infection Biology, Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine, Keppel Street, London WC1E 7HT, UK
| | - Rachel Thaxter
- Clinical Microbiology and Public Health Laboratory, UK Health Security Agency, Addenbrooke's Hospital, Cambridge, UK
| | - David A Enoch
- Clinical Microbiology and Public Health Laboratory, UK Health Security Agency, Addenbrooke's Hospital, Cambridge, UK
| | - Theodore Gouliouris
- Department of Medicine, University of Cambridge, Box 157 Addenbrooke's Hospital, Hills Road, Cambridge, UK
- Clinical Microbiology and Public Health Laboratory, UK Health Security Agency, Addenbrooke's Hospital, Cambridge, UK
| | - Danielle Leek
- Department of Medicine, University of Cambridge, Box 157 Addenbrooke's Hospital, Hills Road, Cambridge, UK
| | - Sophia T Girgis
- Department of Medicine, University of Cambridge, Box 157 Addenbrooke's Hospital, Hills Road, Cambridge, UK
| | - Asha Akram
- Department of Medicine, University of Cambridge, Box 157 Addenbrooke's Hospital, Hills Road, Cambridge, UK
| | - Marta Matuszewska
- Department of Medicine, University of Cambridge, Box 157 Addenbrooke's Hospital, Hills Road, Cambridge, UK
- Wellcome Sanger Institute, Hinxton, Cambridge, UK
| | - Paul Rhodes
- Next Gen Diagnostics, LLC, (NGD) Mountain View, CA, USA
- Broers Building, 21 JJ Thomson Ave., Cambridge, UK
| | - Julian Parkhill
- Department of Veterinary Medicine, University of Cambridge, Madingley Road, Cambridge, UK
| | - Sharon J Peacock
- Department of Medicine, University of Cambridge, Box 157 Addenbrooke's Hospital, Hills Road, Cambridge, UK
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6
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Rodino KG, Simner PJ. Status check: next-generation sequencing for infectious-disease diagnostics. J Clin Invest 2024; 134:e178003. [PMID: 38357923 PMCID: PMC10866643 DOI: 10.1172/jci178003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/16/2024] Open
Abstract
Next-generation sequencing (NGS) applications for the diagnostics of infectious diseases has demonstrated great potential with three distinct approaches: whole-genome sequencing (WGS), targeted NGS (tNGS), and metagenomic NGS (mNGS, also known as clinical metagenomics). These approaches provide several advantages over traditional microbiologic methods, though challenges still exist.
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Affiliation(s)
- Kyle G. Rodino
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Patricia J. Simner
- Department of Pathology, Johns Hopkins School of Medicine, Baltimore, Maryland, USA
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7
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Sundermann AJ, Javaid W. Whole-genome sequencing surveillance: Growing evidence for a future potential practice standard of infection prevention. Infect Control Hosp Epidemiol 2024; 45:135-136. [PMID: 38073562 DOI: 10.1017/ice.2023.261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/15/2024]
Affiliation(s)
- Alexander J Sundermann
- Division of Infectious Diseases, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Waleed Javaid
- Division of Infectious Diseases, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York
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8
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Simon SJ, Sater M, Herriott I, Huntley M, Briars E, Hollenbeck BL. Staphylococcus epidermidis joint isolates: Whole-genome sequencing demonstrates evidence of hospital transmission and common antimicrobial resistance. Infect Control Hosp Epidemiol 2024; 45:150-156. [PMID: 38099465 DOI: 10.1017/ice.2023.253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/15/2024]
Abstract
OBJECTIVE We investigated genetic, epidemiologic, and environmental factors contributing to positive Staphylococcus epidermidis joint cultures. DESIGN Retrospective cohort study with whole-genome sequencing (WGS). PATIENTS We identified S. epidermidis isolates from hip or knee cultures in patients with 1 or more prior corresponding intra-articular procedure at our hospital. METHODS WGS and single-nucleotide polymorphism-based clonality analyses were performed, including species identification, in silico multilocus sequence typing (MLST), phylogenomic analysis, and genotypic assessment of the prevalence of specific antibiotic resistance and virulence genes. Epidemiologic review was performed to compare cluster and noncluster cases. RESULTS In total, 60 phenotypically distinct S. epidermidis isolates were identified. After removal of duplicates and impure samples, 48 isolates were used for the phylogenomic analysis, and 45 (93.7%) isolates were included in the clonality analysis. Notably, 5 S. epidermidis strains (10.4%) showed phenotypic susceptibility to oxacillin yet harbored mecA, and 3 (6.2%) strains showed phenotypic resistance despite not having mecA. Smr was found in all isolates, and mupA positivity was not observed. We also identified 6 clonal clusters from the clonality analysis, which accounted for 14 (31.1%) of the 45 S. epidermidis isolates. Our epidemiologic investigation revealed ties to common aspirations or operative procedures, although no specific common source was identified. CONCLUSIONS Most S. epidermidis isolates from clinical joint samples are diverse in origin, but we identified an important subset of 31.1% that belonged to subclinical healthcare-associated clusters. Clusters appeared to resolve spontaneously over time, suggesting the benefit of routine hospital infection control and disinfection practices.
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Affiliation(s)
- Samantha J Simon
- Research Department, New England Baptist Hospital, Boston, Massachusetts
| | | | | | | | | | - Brian L Hollenbeck
- Research Department, New England Baptist Hospital, Boston, Massachusetts
- Infectious Diseases, New England Baptist Hospital, Boston, Massachusetts
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9
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Raabe NJ, Valek AL, Griffith MP, Mills E, Waggle K, Srinivasa VR, Ayres AM, Bradford C, Creager H, Pless LL, Sundermann AJ, Van Tyne D, Snyder GM, Harrison LH. Genomic Epidemiologic Investigation of a Multispecies Hospital Outbreak of NDM-5-Producing Enterobacterales Infections. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.08.31.23294545. [PMID: 37693518 PMCID: PMC10491379 DOI: 10.1101/2023.08.31.23294545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Background New Delhi metallo-β-lactamase (NDM) represents an emergent mechanism of carbapenem resistance associated with high mortality and limited antimicrobial treatment options. Because the blaNDM resistance gene is often carried on plasmids, traditional infection prevention and control (IP&C) surveillance methods like speciation, antimicrobial resistance testing, and reactive whole genome sequencing (WGS) may not detect plasmid transfer in multispecies outbreaks. Methods Initial outbreak detection of NDM-producing Enterobacterales identified at an acute care hospital occurred via traditional IP&C methods and was supplemented by real-time WGS surveillance, which was performed weekly using the Illumina platform. To resolve NDM-encoding plasmids, we performed long-read Oxford Nanopore sequencing and constructed hybrid assemblies using Illumina and Nanopore sequencing data. Reports of relatedness between NDM-producing organisms and reactive WGS for suspected outbreaks were shared with the IP&C team for assessment and intervention. Findings We observed a multispecies outbreak of NDM-5-producing Enterobacterales isolated from 15 patients between February 2021 and February 2023. The 19 clinical and surveillance isolates sequenced included seven bacterial species and each encoded the same NDM-5 plasmid, which showed high homology to NDM plasmids previously observed in Asia. WGS surveillance and epidemiologic investigation characterized ten horizontal plasmid transfer events and six bacterial transmission events between patients housed in varying hospital units. Transmission prevention focused on enhanced observation and adherence to basic infection prevention measures. Interpretation Our investigation revealed a complex, multispecies outbreak of NDM that involved multiple plasmid transfer and bacterial transmission events, increasing the complexity of outbreak identification and transmission prevention. Our investigation highlights the utility of combining traditional IP&C and prospective genomic methods in identifying and containing plasmid-associated outbreaks. Funding This work was funded in part by the National Institute of Allergy and Infectious Diseases, National Institutes of Health (NIH) (R01AI127472) (R21AI1783691).
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Affiliation(s)
- Nathan J. Raabe
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, 3507 Victoria Street, BST-10 E1000-4A, Pittsburgh, Pennsylvania 15213, USA
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, 3550 Terrace Street, 818 Scaife Hall, Pittsburgh, Pennsylvania 15261, USA
- Department of Epidemiology, School of Public Health, University of Pittsburgh, 130 De Soto Street, Pittsburgh, Pennsylvania 15261, USA
| | - Abby L. Valek
- Department of Infection Control and Hospital Epidemiology, UPMC Presbyterian, 200 Lothrop Street, Pittsburgh, Pennsylvania 15213, USA
| | - Marissa P. Griffith
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, 3507 Victoria Street, BST-10 E1000-4A, Pittsburgh, Pennsylvania 15213, USA
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, 3550 Terrace Street, 818 Scaife Hall, Pittsburgh, Pennsylvania 15261, USA
| | - Emma Mills
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, 3550 Terrace Street, 818 Scaife Hall, Pittsburgh, Pennsylvania 15261, USA
| | - Kady Waggle
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, 3507 Victoria Street, BST-10 E1000-4A, Pittsburgh, Pennsylvania 15213, USA
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, 3550 Terrace Street, 818 Scaife Hall, Pittsburgh, Pennsylvania 15261, USA
- Department of Epidemiology, School of Public Health, University of Pittsburgh, 130 De Soto Street, Pittsburgh, Pennsylvania 15261, USA
| | - Vatsala Rangachar Srinivasa
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, 3507 Victoria Street, BST-10 E1000-4A, Pittsburgh, Pennsylvania 15213, USA
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, 3550 Terrace Street, 818 Scaife Hall, Pittsburgh, Pennsylvania 15261, USA
| | - Ashley M. Ayres
- Department of Infection Control and Hospital Epidemiology, UPMC Presbyterian, 200 Lothrop Street, Pittsburgh, Pennsylvania 15213, USA
| | - Claire Bradford
- Department of Infection Control and Hospital Epidemiology, UPMC Presbyterian, 200 Lothrop Street, Pittsburgh, Pennsylvania 15213, USA
| | - Hannah Creager
- Department of Pathology, University of Pittsburgh Medical Center, 200 Lothrop Street Pittsburgh, PA 15213
- Department of Pathology, University of Pittsburgh School of Medicine, 200 Lothrop St, S-417 BST, Pittsburgh, PA 15261
| | - Lora L. Pless
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, 3507 Victoria Street, BST-10 E1000-4A, Pittsburgh, Pennsylvania 15213, USA
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, 3550 Terrace Street, 818 Scaife Hall, Pittsburgh, Pennsylvania 15261, USA
| | - Alexander J. Sundermann
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, 3507 Victoria Street, BST-10 E1000-4A, Pittsburgh, Pennsylvania 15213, USA
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, 3550 Terrace Street, 818 Scaife Hall, Pittsburgh, Pennsylvania 15261, USA
| | - Daria Van Tyne
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, 3550 Terrace Street, 818 Scaife Hall, Pittsburgh, Pennsylvania 15261, USA
| | - Graham M. Snyder
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, 3550 Terrace Street, 818 Scaife Hall, Pittsburgh, Pennsylvania 15261, USA
- Department of Infection Control and Hospital Epidemiology, UPMC Presbyterian, 200 Lothrop Street, Pittsburgh, Pennsylvania 15213, USA
| | - Lee H. Harrison
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, 3507 Victoria Street, BST-10 E1000-4A, Pittsburgh, Pennsylvania 15213, USA
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, 3550 Terrace Street, 818 Scaife Hall, Pittsburgh, Pennsylvania 15261, USA
- Department of Epidemiology, School of Public Health, University of Pittsburgh, 130 De Soto Street, Pittsburgh, Pennsylvania 15261, USA
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10
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Branch-Elliman W, Sundermann AJ, Wiens J, Shenoy ES. The future of automated infection detection: Innovation to transform practice (Part III/III). ANTIMICROBIAL STEWARDSHIP & HEALTHCARE EPIDEMIOLOGY : ASHE 2023; 3:e26. [PMID: 36865708 PMCID: PMC9972533 DOI: 10.1017/ash.2022.333] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 10/13/2022] [Indexed: 06/18/2023]
Abstract
Current methods of emergency-room-based syndromic surveillance were insufficient to detect early community spread of severe acute respiratory coronavirus virus 2 (SARS-CoV-2) in the United States, which slowed the infection prevention and control response to the novel pathogen. Emerging technologies and automated infection surveillance have the potential to improve upon current practice standards and to revolutionize the practice of infection detection, prevention and control both inside and outside of healthcare settings. Genomics, natural language processing, and machine learning can be leveraged to improve identification of transmission events and aid and evaluate outbreak response. In the near future, automated infection detection strategies can be used to advance a true "Learning Healthcare System" that will support near-real-time quality improvement efforts and advance the scientific basis for the practice of infection control.
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Affiliation(s)
- Westyn Branch-Elliman
- Section of Infectious Diseases, Department of Medicine, Veterans’ Affairs (VA) Boston Healthcare System, Boston, Massachusetts
- VA Boston Center for Healthcare Organization and Implementation Research (CHOIR), Boston, Massachusetts
- Harvard Medical School, Boston, Massachusetts
| | - Alexander J. Sundermann
- Division of Infectious Diseases, Department of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Jenna Wiens
- Division of Computer Science and Engineering, University of Michigan, Ann Arbor, Michigan
| | - Erica S. Shenoy
- Harvard Medical School, Boston, Massachusetts
- Infection Control Unit, Massachusetts General Hospital, Boston, Massachusetts
- Division of Infectious Diseases, Department of Medicine, Massachusetts General Hospital, Boston, Massachusetts
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