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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Robillard DW, Sundermann AJ, Raux BR, Prinzi AM. Navigating the network: a narrative overview of AMR surveillance and data flow in the United States. Antimicrob Steward Healthc Epidemiol 2024; 4:e55. [PMID: 38655022 PMCID: PMC11036423 DOI: 10.1017/ash.2024.64] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Revised: 03/25/2024] [Accepted: 03/27/2024] [Indexed: 04/26/2024]
Abstract
The antimicrobial resistance (AMR) surveillance landscape in the United States consists of a data flow that starts in the clinical setting and is maintained by a network of national and state public health laboratories. These organizations are well established, with robust methodologies to test and confirm antimicrobial susceptibility. Still, the bridge that guides the flow of data is often one directional and caught in a constant state of rush hour that can only be refined with improvements to infrastructure and automation in the data flow. Moreover, there is an absence of information in the literature explaining the processes clinical laboratories use to coalesce and share susceptibility test data for AMR surveillance, further complicated by variability in testing procedures. This knowledge gap limits our understanding of what is needed to improve and streamline data sharing from clinical to public health laboratories. Successful models of AMR surveillance display attributes like 2-way communication between clinical and public health laboratories, centralized databases, standardized data, and the use of electronic health records or data systems, highlighting areas of opportunity and improvement. This article explores the roles and processes of the organizations involved in AMR surveillance in the United States and identifies current knowledge gaps and opportunities to improve communication between them through standardization, communication, and modernization of data flow.
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Affiliation(s)
- Darin W. Robillard
- Division of Public Health, University of Utah School of Medicine, Salt Lake City, UT, USA
- Corporate Program Management, bioMérieux, Salt Lake City, UT, USA
| | - Alexander J. Sundermann
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Brian R. Raux
- US Medical Affairs, bioMérieux, Salt Lake City, UT, USA
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Sundermann AJ, Rangachar Srinivasa V, Mills EG, Griffith MP, Waggle KD, Ayres AM, Pless L, Snyder GM, Harrison LH, Van Tyne D. Two Artificial Tears Outbreak-Associated Cases of Extensively Drug-Resistant Pseudomonas aeruginosa Detected Through Whole Genome Sequencing-Based Surveillance. J Infect Dis 2024; 229:517-521. [PMID: 37700467 PMCID: PMC10873170 DOI: 10.1093/infdis/jiad318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 08/03/2023] [Indexed: 09/14/2023] Open
Abstract
We describe 2 cases of extensively drug-resistant Pseudomonas aeruginosa infection caused by a strain of public health concern, as it was recently associated with a nationwide outbreak of contaminated artificial tears. Both cases were detected through database review of genomes in the Enhanced Detection System for Hospital-Associated Transmission (EDS-HAT), a routine genome sequencing-based surveillance program. We generated a high-quality reference genome for the outbreak strain from an isolate from our center and examined the mobile elements encoding blaVIM-80 and bla-GES-9 carbapenemases. We used publicly available Pseudomonas aeruginosa genomes to explore the genetic relatedness and antimicrobial resistance genes of the outbreak strain.
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Affiliation(s)
- Alexander J Sundermann
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh
- Division of Infectious Diseases, University of Pittsburgh School of Medicine
| | - Vatsala Rangachar Srinivasa
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh
- Division of Infectious Diseases, University of Pittsburgh School of Medicine
- Department of Epidemiology, School of Public Health, University of Pittsburgh
| | - Emma G Mills
- Division of Infectious Diseases, University of Pittsburgh School of Medicine
| | - Marissa P Griffith
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh
- Division of Infectious Diseases, University of Pittsburgh School of Medicine
- Department of Epidemiology, School of Public Health, University of Pittsburgh
| | - Kady D Waggle
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh
- Division of Infectious Diseases, University of Pittsburgh School of Medicine
- Department of Epidemiology, School of Public Health, University of Pittsburgh
| | - Ashley M Ayres
- Department of Infection Control and Hospital Epidemiology, University of Pittsburgh Medical Center–Presbyterian Hospital, Pittsburgh, Pennsylvania
| | - Lora Pless
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh
- Division of Infectious Diseases, University of Pittsburgh School of Medicine
| | - Graham M Snyder
- Division of Infectious Diseases, University of Pittsburgh School of Medicine
- Department of Infection Control and Hospital Epidemiology, University of Pittsburgh Medical Center–Presbyterian Hospital, Pittsburgh, Pennsylvania
| | - Lee H Harrison
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh
- Division of Infectious Diseases, University of Pittsburgh School of Medicine
- Department of Epidemiology, School of Public Health, University of Pittsburgh
| | - Daria Van Tyne
- Division of Infectious Diseases, University of Pittsburgh School of Medicine
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Sundermann AJ, Griffith MP, Srinivasa VR, Waggle K, Snyder GM, Van Tyne D, Pless L, Harrison LH. Prolonged bacterial carriage and hospital transmission detected by whole genome sequencing surveillance. Antimicrob Steward Healthc Epidemiol 2024; 4:e11. [PMID: 38415095 PMCID: PMC10897709 DOI: 10.1017/ash.2024.4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Revised: 12/14/2023] [Accepted: 12/20/2023] [Indexed: 02/29/2024]
Affiliation(s)
- 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
| | - 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
| | - 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
| | - 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
- Department of Epidemiology, School of Public Health, 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 Control and Hospital Epidemiology, UPMC Presbyterian, Pittsburgh, PA, USA
| | - Daria Van Tyne
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Lora 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
| | - 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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Sundermann AJ, Griffith M, Rangachar Srinivasa V, Ereifej D, Waggle K, Van Tyne D, Snyder GM, Pasculle AW, Bartholow T, Pless L, Harrison LH. Environmental contamination of postmortem blood cultures detected by whole-genome sequencing surveillance. Infect Control Hosp Epidemiol 2023; 44:2103-2105. [PMID: 37615108 PMCID: PMC10755148 DOI: 10.1017/ice.2023.192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 06/09/2023] [Accepted: 07/22/2023] [Indexed: 08/25/2023]
Affiliation(s)
- Alexander J. Sundermann
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, Pittsburgh, Pennsylvania
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Marissa Griffith
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, Pittsburgh, Pennsylvania
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Vatsala Rangachar Srinivasa
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, Pittsburgh, Pennsylvania
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
- Department of Epidemiology, School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Deena Ereifej
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, Pittsburgh, Pennsylvania
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
- Department of Epidemiology, School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Kady Waggle
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, Pittsburgh, Pennsylvania
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
- Department of Epidemiology, School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Daria Van Tyne
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Graham M. Snyder
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
- Department of Infection Control and Hospital Epidemiology, UPMC Presbyterian, Pittsburgh, Pennsylvania
| | - A. William Pasculle
- Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
- Clinical Microbiology Laboratory, UPMC Presbyterian, Pittsburgh, Pennsylvania
| | - Tanner Bartholow
- Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Lora Pless
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, Pittsburgh, Pennsylvania
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Lee H. Harrison
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, Pittsburgh, Pennsylvania
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
- Department of Epidemiology, School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania
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Sundermann AJ, Penzelik J, Ayres A, Snyder GM, Harrison LH. Sensitivity of National Healthcare Safety Network definitions to capture healthcare-associated transmission identified by whole-genome sequencing surveillance. Infect Control Hosp Epidemiol 2023; 44:1663-1665. [PMID: 36974518 PMCID: PMC10533730 DOI: 10.1017/ice.2023.52] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 02/15/2023] [Accepted: 02/20/2023] [Indexed: 03/29/2023]
Abstract
The National Healthcare Safety Network (NHSN) definitions are critical for standardizing healthcare-associated infection surveillance in US healthcare facilities. However, their use in accurately detecting healthcare-associated transmission (HAT) has not been measured. Using whole-genome sequencing surveillance data, we show that the NHSN has a sensitivity of 44.4% in detecting HAT.
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Affiliation(s)
- Alexander J Sundermann
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, Pittsburgh, Pennsylvania
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Joseph Penzelik
- Department of Infection Control and Hospital Epidemiology, UPMC Presbyterian, Pittsburgh, Pennsylvania
| | - Ashley Ayres
- Department of Infection Control and Hospital Epidemiology, UPMC Presbyterian, Pittsburgh, Pennsylvania
| | - Graham M Snyder
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
- Department of Infection Control and Hospital Epidemiology, UPMC Presbyterian, Pittsburgh, Pennsylvania
| | - Lee H Harrison
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, Pittsburgh, Pennsylvania
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
- Department of Epidemiology, School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania
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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 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] [What about the content of this article? (0)] [Affiliation(s)] [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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Sundermann AJ, Srinivasa VR, Mills EG, Griffith MP, Waggle KD, Ayres AM, Pless L, Snyder GM, Harrison LH, Van Tyne D. Two artificial tears outbreak-associated cases of XDR Pseudomonas aeruginosa detected through whole genome sequencing-based surveillance. medRxiv 2023:2023.04.11.23288417. [PMID: 37131775 PMCID: PMC10153325 DOI: 10.1101/2023.04.11.23288417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
We describe two cases of XDR Pseudomonas aeruginosa infection caused by a strain of public health concern recently associated with a nationwide outbreak of contaminated artificial tears. Both cases were detected through database review of genomes in the Enhanced Detection System for Hospital-Associated Transmission (EDS-HAT), a routine genome sequencing-based surveillance program. We generated a high-quality reference genome for the outbreak strain from one of the case isolates from our center and examined the mobile elements encoding bla VIM-80 and bla GES-9 carbapenemases. We then used publicly available P. aeruginosa genomes to explore the genetic relatedness and antimicrobial resistance genes of the outbreak strain.
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Affiliation(s)
- Alexander J. Sundermann
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Vatsala Rangachar Srinivasa
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
- Department of Epidemiology, School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Emma G. Mills
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Marissa P. Griffith
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
- Department of Epidemiology, School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Kady D. Waggle
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
- Department of Epidemiology, School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Ashley M. Ayres
- Department of Infection Control and Hospital Epidemiology, UPMC Presbyterian, Pittsburgh, Pennsylvania, USA
| | - Lora Pless
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Graham M. Snyder
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
- Department of Infection Control and Hospital Epidemiology, UPMC Presbyterian, Pittsburgh, Pennsylvania, USA
| | - Lee H. Harrison
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
- Department of Epidemiology, School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Daria Van Tyne
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
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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). Antimicrob Steward Healthc Epidemiol 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] [What about the content of this article? (0)] [Affiliation(s)] [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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11
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Sundermann AJ, Chen J, Miller JK, Martin EM, Snyder GM, Van Tyne D, Marsh JW, Dubrawski A, Harrison LH. Whole-genome sequencing surveillance and machine learning for healthcare outbreak detection and investigation: A systematic review and summary. Antimicrob Steward Healthc Epidemiol 2022; 2:e91. [PMID: 36483409 PMCID: PMC9726481 DOI: 10.1017/ash.2021.241] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Accepted: 11/04/2021] [Indexed: 06/17/2023]
Abstract
BACKGROUND Whole-genome sequencing (WGS) has traditionally been used in infection prevention to confirm or refute the presence of an outbreak after it has occurred. Due to decreasing costs of WGS, an increasing number of institutions have been utilizing WGS-based surveillance. Additionally, machine learning or statistical modeling to supplement infection prevention practice have also been used. We systematically reviewed the use of WGS surveillance and machine learning to detect and investigate outbreaks in healthcare settings. METHODS We performed a PubMed search using separate terms for WGS surveillance and/or machine-learning technologies for infection prevention through March 15, 2021. RESULTS Of 767 studies returned using the WGS search terms, 42 articles were included for review. Only 2 studies (4.8%) were performed in real time, and 39 (92.9%) studied only 1 pathogen. Nearly all studies (n = 41, 97.6%) found genetic relatedness between some isolates collected. Across all studies, 525 outbreaks were detected among 2,837 related isolates (average, 5.4 isolates per outbreak). Also, 35 studies (83.3%) only utilized geotemporal clustering to identify outbreak transmission routes. Of 21 studies identified using the machine-learning search terms, 4 were included for review. In each study, machine learning aided outbreak investigations by complementing methods to gather epidemiologic data and automating identification of transmission pathways. CONCLUSIONS WGS surveillance is an emerging method that can enhance outbreak detection. Machine learning has the potential to identify novel routes of pathogen transmission. Broader incorporation of WGS surveillance into infection prevention practice has the potential to transform the detection and control of healthcare outbreaks.
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Affiliation(s)
- Alexander J. Sundermann
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, Pittsburgh, Pennsylvania
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Jieshi Chen
- Auton Lab, Carnegie Mellon University, Pittsburgh, Pennsylvania
| | - James K. Miller
- Auton Lab, Carnegie Mellon University, Pittsburgh, Pennsylvania
| | - Elise M. Martin
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
- Department of Infection Prevention and Hospital Epidemiology, UPMC Presbyterian, Pittsburgh, Pennsylvania
| | - Graham M. Snyder
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
- Department of Infection Prevention and Hospital Epidemiology, UPMC Presbyterian, Pittsburgh, Pennsylvania
| | - Daria Van Tyne
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Jane W. Marsh
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, Pittsburgh, Pennsylvania
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | - Artur Dubrawski
- Auton Lab, Carnegie Mellon University, Pittsburgh, Pennsylvania
| | - Lee H. Harrison
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, Pittsburgh, Pennsylvania
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania
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12
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Sundermann AJ, Chen J, Kumar P, Ayres AM, Cho ST, Ezeonwuka C, Griffith MP, Miller JK, Mustapha MM, Pasculle AW, Saul MI, Shutt KA, Srinivasa V, Waggle K, Snyder DJ, Cooper VS, Van Tyne D, Snyder GM, Marsh JW, Dubrawski A, Roberts MS, Harrison LH. Whole Genome Sequencing Surveillance and Machine Learning of the Electronic Health Record for Enhanced Healthcare Outbreak Detection. Clin Infect Dis 2021; 75:476-482. [PMID: 34791136 PMCID: PMC9427134 DOI: 10.1093/cid/ciab946] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Most hospitals use traditional infection prevention (IP) methods for outbreak detection. We developed the Enhanced Detection System for Healthcare-Associated Transmission (EDS-HAT), which combines whole genome sequencing (WGS) surveillance and machine learning (ML) of the electronic health record (EHR) to identify undetected outbreaks and the responsible transmission routes, respectively. METHODS We performed WGS surveillance of healthcare-associated bacterial pathogens from November 2016 to November 2018. EHR ML was used to identify the transmission routes for WGS-detected outbreaks, which were investigated by an IP expert. Potential infections prevented were estimated and compared to traditional IP practice during the same period. RESULTS Of 3,165 isolates, there were 2,752 unique patient isolates in 99 clusters involving 297 (10.8%) patient isolates were identified by WGS; clusters ranged from 2-14 patients. At least one transmission route was detected for 65.7% of clusters. During the same time, traditional IP investigation prompted WGS for 15 suspected outbreaks involving 133 patients, for which transmission events were identified for 5 (3.8%). If EDS-HAT had been running in real-time, 25-63 transmissions could have been prevented. EDS-HAT was found to be cost-saving and more effective than traditional IP practice, with overall savings of $192,408 - $692,532. CONCLUSION EDS-HAT detected multiple outbreaks not identified using traditional IP methods, correctly identified the transmission routes for most outbreaks, and would save the hospital substantial costs. Traditional IP practice misidentified outbreaks for which transmission did not occur. WGS surveillance combined with EHR ML has the potential to save costs and enhance patient safety.
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Affiliation(s)
- Alexander J Sundermann
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA.,Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Jieshi Chen
- Auton Lab, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
| | - Praveen Kumar
- Department of Health Policy and Management, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Ashley M Ayres
- Department of Infection Control and Hospital Epidemiology, UPMC Presbyterian, Pittsburgh, Pennsylvania, USA
| | - Shu-Ting Cho
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Chinelo Ezeonwuka
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Marissa P Griffith
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - James K Miller
- Auton Lab, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
| | - Mustapha M Mustapha
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - A William Pasculle
- Department of Pathology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Melissa I Saul
- Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Kathleen A Shutt
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Vatsala Srinivasa
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Kady Waggle
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Daniel J Snyder
- Department of Microbiology and Molecular Genetics, and Center for Evolutionary Biology and Medicine, University of Pittsburgh School of Medicine, Pennsylvania, USA
| | - Vaughn S Cooper
- Department of Microbiology and Molecular Genetics, and Center for Evolutionary Biology and Medicine, University of Pittsburgh School of Medicine, Pennsylvania, USA
| | - Daria Van Tyne
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Graham M Snyder
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA.,Department of Infection Control and Hospital Epidemiology, UPMC Presbyterian, Pittsburgh, Pennsylvania, USA
| | - Jane W Marsh
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Artur Dubrawski
- Auton Lab, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
| | - Mark S Roberts
- Department of Health Policy and Management, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Lee H Harrison
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.,Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA.,Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
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13
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Sundermann AJ, Chen J, Miller JK, Saul MI, Shutt KA, Griffith MP, Mustapha MM, Ezeonwuka C, Waggle K, Srinivasa V, Kumar P, Pasculle AW, Ayres AM, Snyder GM, Cooper VS, Van Tyne D, Marsh JW, Dubrawski AW, Harrison LH. Outbreak of Pseudomonas aeruginosa Infections from a Contaminated Gastroscope Detected by Whole Genome Sequencing Surveillance. Clin Infect Dis 2021; 73:e638-e642. [PMID: 33367518 DOI: 10.1093/cid/ciaa1887] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND Traditional methods of outbreak investigations utilize reactive whole genome sequencing (WGS) to confirm or refute the outbreak. We have implemented WGS surveillance and a machine learning (ML) algorithm for the electronic health record (EHR) to retrospectively detect previously unidentified outbreaks and to determine the responsible transmission routes. METHODS We performed WGS surveillance to identify and characterize clusters of genetically-related Pseudomonas aeruginosa infections during a 24-month period. ML of the EHR was used to identify potential transmission routes. A manual review of the EHR was performed by an infection preventionist to determine the most likely route and results were compared to the ML algorithm. RESULTS We identified a cluster of 6 genetically related P. aeruginosa cases that occurred during a 7-month period. The ML algorithm identified gastroscopy as a potential transmission route for 4 of the 6 patients. Manual EHR review confirmed gastroscopy as the most likely route for 5 patients. This transmission route was confirmed by identification of a genetically-related P. aeruginosa incidentally cultured from a gastroscope used on 4of the 5 patients. Three infections, 2 of which were blood stream infections, could have been prevented if the ML algorithm had been running in real-time. CONCLUSIONS WGS surveillance combined with a ML algorithm of the EHR identified a previously undetected outbreak of gastroscope-associated P. aeruginosa infections. These results underscore the value of WGS surveillance and ML of the EHR for enhancing outbreak detection in hospitals and preventing serious infections.
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Affiliation(s)
- Alexander J Sundermann
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh.,Division of Infectious Diseases, University of Pittsburgh School of Medicine
| | - Jieshi Chen
- Anton Laboratory, Carnegie Mellon University
| | | | - Melissa I Saul
- Department of Medicine, University of Pittsburgh School of Medicine
| | - Kathleen A Shutt
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh.,Division of Infectious Diseases, University of Pittsburgh School of Medicine
| | - Marissa P Griffith
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh.,Division of Infectious Diseases, University of Pittsburgh School of Medicine
| | - Mustapha M Mustapha
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh.,Division of Infectious Diseases, University of Pittsburgh School of Medicine
| | - Chinelo Ezeonwuka
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh.,Division of Infectious Diseases, University of Pittsburgh School of Medicine
| | - Kady Waggle
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh.,Division of Infectious Diseases, University of Pittsburgh School of Medicine
| | - Vatsala Srinivasa
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh.,Division of Infectious Diseases, University of Pittsburgh School of Medicine
| | - Praveen Kumar
- Department of Health Policy and Management, Graduate School of Public Health, University of Pittsburgh
| | | | - Ashley M Ayres
- Department of Infection Prevention and Control, University of Pittsburgh Medical Center
| | - Graham M Snyder
- Division of Infectious Diseases, University of Pittsburgh School of Medicine.,Department of Infection Prevention and Control, University of Pittsburgh Medical Center
| | - Vaughn S Cooper
- Department of Microbiology and Molecular Genetics, and Center for Evolutionary Biology and Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Daria Van Tyne
- Division of Infectious Diseases, University of Pittsburgh School of Medicine
| | - Jane W Marsh
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh.,Division of Infectious Diseases, University of Pittsburgh School of Medicine
| | | | - Lee H Harrison
- Microbial Genomic Epidemiology Laboratory, Center for Genomic Epidemiology, University of Pittsburgh.,Division of Infectious Diseases, University of Pittsburgh School of Medicine
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14
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Sundermann AJ, Clancy CJ, Pasculle AW, Liu G, Cheng S, Cumbie RB, Driscoll E, Ayres A, Donahue L, Buck M, Streifel A, Muto CA, Nguyen MH. Remediation of Mucorales-contaminated Healthcare Linens at a Laundry Facility Following an Investigation of a Case Cluster of Hospital-acquired Mucormycosis. Clin Infect Dis 2021; 74:1401-1407. [PMID: 34282829 DOI: 10.1093/cid/ciab638] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND In an investigation of hospital-acquired mucormycosis cases among transplant recipients, healthcare linens (HCLs) delivered to our center were found to be contaminated with Mucorales. We describe an investigation and remediation of Mucorales contamination at the laundry supplying our center. METHODS We performed monthly RODAC cultures of HCLs upon hospital arrival, and conducted site inspections and surveillance cultures at the laundry facility. Remediation was designed and implemented by infection prevention and facility leadership teams. RESULTS Prior to remediation, 20% of HCLs were culture-positive for Mucorales upon hospital arrival. Laundry facility layout and processes were consistent with industry standards. Significant step-ups in Mucorales and mould culture-positivity of HCLs were detected at the post-dryer step (0% to 12% (p=0.04) and 5% to 29% (p=0.01), respectively). Further increases to 17% and 40% culture-positivity, respectively, were noted during pre-transport holding. Site inspection revealed heavy Mucorales-positive lint accumulation in rooftop air intake and exhaust vents that cooled driers; intake and exhaust vents that were facing each other; rooftop and plant-wide lint accumulation, including in the pre-transport clean room; uncovered carts with freshly-laundered HCLs. Following environmental remediation, quality assurance measures and education directed toward these sources, Mucorales culture-positivity of newly-delivered HCLs was reduced to 0.3% (p=0.0001); area of lint-contaminated rooftop decreased from 918 m 2 to 0 m 2 on satellite images. CONCLUSIONS Targeted laundry facility interventions guided by site inspections and step-wise culturing significantly reduced Mucorales-contaminated HCLs delivered to our hospital. Collaboration between infection prevention and laundry facility teams was crucial to successful remediation.
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Affiliation(s)
- Alexander J Sundermann
- University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.,University of Pittsburgh Graduate School of Public Health, PA, USA.,Contributed equally
| | - Cornelius J Clancy
- University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.,University of Pittsburgh Medical Center, Pittsburgh, PA, USA.,Contributed equally
| | | | - Guojun Liu
- University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Shaoji Cheng
- University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | | | - Eileen Driscoll
- University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Ashley Ayres
- University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Lisa Donahue
- University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Michael Buck
- University of Minnesota Department of Environmental Health and Safety, MN, USA
| | - Andrew Streifel
- University of Minnesota Department of Environmental Health and Safety, MN, USA
| | - Carlene A Muto
- University of Virginia Division of Infectious Diseases and International Health, VA, USA
| | - M Hong Nguyen
- University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.,University of Pittsburgh Medical Center, Pittsburgh, PA, USA
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15
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Kumar P, Sundermann AJ, Martin EM, Snyder GM, Marsh JW, Harrison LH, Roberts MS. Method for Economic Evaluation of Bacterial Whole Genome Sequencing Surveillance Compared to Standard of Care in Detecting Hospital Outbreaks. Clin Infect Dis 2021; 73:e9-e18. [PMID: 32367125 DOI: 10.1093/cid/ciaa512] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2020] [Accepted: 04/29/2020] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND Whole genome sequencing (WGS) surveillance and electronic health record data mining have the potential to greatly enhance the identification and control of hospital outbreaks. The objective was to develop methods for examining economic value of a WGS surveillance-based infection prevention (IP) program compared to standard of care (SoC). METHODS The economic value of a WGS surveillance-based IP program was assessed from a hospital's perspective using historical outbreaks from 2011-2016. We used transmission network of outbreaks to estimate incremental cost per transmission averted. The number of transmissions averted depended on the effectiveness of intervening against transmission routes, time from transmission to positive culture results and time taken to obtain WGS results and intervene on the transmission route identified. The total cost of an IP program included cost of staffing, WGS, and treating infections. RESULTS Approximately 41 out of 89 (46%) transmissions could have been averted under the WGS surveillance-based IP program, and it was found to be a less costly and more effective strategy than SoC. The results were most sensitive to the cost of performing WGS and the number of isolates sequenced per year under WGS surveillance. The probability of the WGS surveillance-based IP program being cost-effective was 80% if willingness to pay exceeded $2400 per transmission averted. CONCLUSIONS The proposed economic analysis is a useful tool to examine economic value of a WGS surveillance-based IP program. These methods will be applied to a prospective evaluation of WGS surveillance compared to SoC.
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Affiliation(s)
- Praveen Kumar
- Department of Health Policy and Management, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Alexander J Sundermann
- The Microbial Genomic Epidemiology Laboratory, University of Pittsburgh School of Medicine and Graduate School of Public Health, Pittsburgh, Pennsylvania, USA
| | - Elise M Martin
- Department of Infection Prevention and Control, University of Pittsburgh Medical Center Presbyterian Hospital, Pittsburgh, Pennsylvania, USA
| | - Graham M Snyder
- Department of Infection Prevention and Control, University of Pittsburgh Medical Center Presbyterian Hospital, Pittsburgh, Pennsylvania, USA
| | - Jane W Marsh
- The Microbial Genomic Epidemiology Laboratory, University of Pittsburgh School of Medicine and Graduate School of Public Health, Pittsburgh, Pennsylvania, USA.,Department of Infection Prevention and Control, University of Pittsburgh Medical Center Presbyterian Hospital, Pittsburgh, Pennsylvania, USA
| | - Lee H Harrison
- The Microbial Genomic Epidemiology Laboratory, University of Pittsburgh School of Medicine and Graduate School of Public Health, Pittsburgh, Pennsylvania, USA
| | - Mark S Roberts
- Department of Health Policy and Management, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
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16
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Sundermann AJ, Babiker A, Marsh JW, Shutt KA, Mustapha MM, Pasculle AW, Ezeonwuka C, Saul MI, Pacey MP, Van Tyne D, Ayres AM, Cooper VS, Snyder GM, Harrison LH. Outbreak of Vancomycin-resistant Enterococcus faecium in Interventional Radiology: Detection Through Whole-genome Sequencing-based Surveillance. Clin Infect Dis 2021; 70:2336-2343. [PMID: 31312842 DOI: 10.1093/cid/ciz666] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2019] [Accepted: 07/15/2019] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Vancomycin-resistant enterococci (VRE) are a major cause of hospital-acquired infections. The risk of infection from interventional radiology (IR) procedures is not well documented. Whole-genome sequencing (WGS) surveillance of clinical bacterial isolates among hospitalized patients can identify previously unrecognized outbreaks. METHODS We analyzed WGS surveillance data from November 2016 to November 2017 for evidence of VRE transmission. A previously unrecognized cluster of 10 genetically related VRE (Enterococcus faecium) infections was discovered. Electronic health record review identified IR procedures as a potential source. An outbreak investigation was conducted. RESULTS Of the 10 outbreak patients, 9 had undergone an IR procedure with intravenous (IV) contrast ≤22 days before infection. In a matched case-control study, preceding IR procedure and IR procedure with contrast were associated with VRE infection (matched odds ratio [MOR], 16.72; 95% confidence interval [CI], 2.01 to 138.73; P = .009 and MOR, 39.35; 95% CI, 7.85 to infinity; P < .001, respectively). Investigation of IR practices and review of the manufacturer's training video revealed sterility breaches in contrast preparation. Our investigation also supported possible transmission from an IR technician. Infection prevention interventions were implemented, and no further IR-associated VRE transmissions have been observed. CONCLUSIONS A prolonged outbreak of VRE infections related to IR procedures with IV contrast resulted from nonsterile preparation of injectable contrast. The fact that our VRE outbreak was discovered through WGS surveillance and the manufacturer's training video that demonstrated nonsterile technique raise the possibility that infections following invasive IR procedures may be more common than previously recognized.
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Affiliation(s)
- Alexander J Sundermann
- Microbial Genomic Epidemiology Laboratory, University of Pittsburgh School of Medicine and Graduate School of Public Health, Pennsylvania.,Department of Infection Control and Hospital Epidemiology, University of Pittsburgh Medical Center, Pennsylvania
| | - Ahmed Babiker
- Microbial Genomic Epidemiology Laboratory, University of Pittsburgh School of Medicine and Graduate School of Public Health, Pennsylvania.,Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pennsylvania
| | - Jane W Marsh
- Microbial Genomic Epidemiology Laboratory, University of Pittsburgh School of Medicine and Graduate School of Public Health, Pennsylvania.,Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pennsylvania
| | - Kathleen A Shutt
- Microbial Genomic Epidemiology Laboratory, University of Pittsburgh School of Medicine and Graduate School of Public Health, Pennsylvania.,Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pennsylvania
| | - Mustapha M Mustapha
- Microbial Genomic Epidemiology Laboratory, University of Pittsburgh School of Medicine and Graduate School of Public Health, Pennsylvania.,Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pennsylvania
| | | | - Chinelo Ezeonwuka
- Microbial Genomic Epidemiology Laboratory, University of Pittsburgh School of Medicine and Graduate School of Public Health, Pennsylvania.,Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pennsylvania
| | - Melissa I Saul
- Microbial Genomic Epidemiology Laboratory, University of Pittsburgh School of Medicine and Graduate School of Public Health, Pennsylvania
| | - Marissa P Pacey
- Microbial Genomic Epidemiology Laboratory, University of Pittsburgh School of Medicine and Graduate School of Public Health, Pennsylvania.,Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pennsylvania
| | - Daria Van Tyne
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pennsylvania
| | - Ashley M Ayres
- Department of Infection Control and Hospital Epidemiology, University of Pittsburgh Medical Center, Pennsylvania
| | - Vaughn S Cooper
- Department of Microbiology and Molecular Genetics, University of Pittsburgh School of Medicine, Pennsylvania
| | - Graham M Snyder
- Department of Infection Control and Hospital Epidemiology, University of Pittsburgh Medical Center, Pennsylvania.,Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pennsylvania
| | - Lee H Harrison
- Microbial Genomic Epidemiology Laboratory, University of Pittsburgh School of Medicine and Graduate School of Public Health, Pennsylvania.,Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pennsylvania
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17
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Nguyen MH, Kaul D, Muto C, Cheng SJ, Richter RA, Bruno VM, Liu G, Beyhan S, Sundermann AJ, Mounaud S, Pasculle AW, Nierman WC, Driscoll E, Cumbie R, Clancy CJ, Dupont CL. Genetic diversity of clinical and environmental Mucorales isolates obtained from an investigation of mucormycosis cases among solid organ transplant recipients. Microb Genom 2020; 6:mgen000473. [PMID: 33245689 PMCID: PMC8116672 DOI: 10.1099/mgen.0.000473] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Accepted: 10/27/2020] [Indexed: 12/20/2022] Open
Abstract
Mucormycoses are invasive infections by Rhizopus species and other Mucorales. Over 10 months, four solid organ transplant (SOT) recipients at our centre developed mucormycosis due to Rhizopus microsporus (n=2), R. arrhizus (n=1) or Lichtheimia corymbifera (n=1), at a median 31.5 days (range: 13-34) post-admission. We performed whole genome sequencing (WGS) on 72 Mucorales isolates (45 R. arrhizus, 19 R. delemar, six R. microsporus, two Lichtheimia species) from these patients, from five patients with community-acquired mucormycosis, and from hospital and regional environments. Isolates were compared by core protein phylogeny and global genomic features, including genome size, guanine-cytosine percentages, shared protein families and paralogue expansions. Patient isolates fell into six core phylogenetic lineages (clades). Phylogenetic and genomic similarities of R. microsporus isolates recovered 7 months apart from two SOT recipients in adjoining hospitals suggested a potential common source exposure. However, isolates from other patients and environmental sites had unique genomes. Many isolates that were indistinguishable by core phylogeny were distinct by one or more global genomic comparisons. Certain clades were recovered throughout the study period, whereas others were found at particular time points. In conclusion, mucormycosis cases could not be genetically linked to a definitive environmental source. Comprehensive genomic analyses eliminated false associations between Mucorales isolates that would have been assigned using core phylogenetic or less extensive genomic comparisons. The genomic diversity of Mucorales mandates that multiple isolates from individual patients and environmental sites undergo WGS during epidemiological investigations. However, exhaustive surveillance of fungal populations in a hospital and surrounding community is probably infeasible.
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Affiliation(s)
- M. Hong Nguyen
- University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | | | - Carlene Muto
- University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- University of Pittsburgh Medical Center, Pittsburgh, PA, USA
- Present address: Department of Medicine, University of Virginia, Charlottesville, VA, USA
| | - Shaoji J. Cheng
- University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | | | | | - Guojun Liu
- University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | | | - Alexander J. Sundermann
- University of Pittsburgh Medical Center, Pittsburgh, PA, USA
- University of Pittsburgh Graduate School of Public Health, Pittsburgh, PA, USA
| | | | - A. William Pasculle
- University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
- University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | | | - Eileen Driscoll
- University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Richard Cumbie
- University of Pittsburgh Medical Center, Pittsburgh, PA, USA
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18
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Leleck OM, Boles CL, Brewster RK, Sundermann AJ, Hallett LA, Egnot NS, Jones AD. Contamination of Medical Scrubs Worn in a Non-healthcare Environment: A Pilot Study. Am J Infect Control 2020. [DOI: 10.1016/j.ajic.2020.06.042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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19
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Evans DR, Griffith MP, Sundermann AJ, Shutt KA, Saul MI, Mustapha MM, Marsh JW, Cooper VS, Harrison LH, Van Tyne D. Systematic detection of horizontal gene transfer across genera among multidrug-resistant bacteria in a single hospital. eLife 2020; 9:53886. [PMID: 32285801 PMCID: PMC7156236 DOI: 10.7554/elife.53886] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2019] [Accepted: 03/14/2020] [Indexed: 12/16/2022] Open
Abstract
Multidrug-resistant bacteria pose a serious health threat, especially in hospitals. Horizontal gene transfer (HGT) of mobile genetic elements (MGEs) facilitates the spread of antibiotic resistance, virulence, and environmental persistence genes between nosocomial pathogens. We screened the genomes of 2173 bacterial isolates from healthcare-associated infections from a single hospital over 18 months, and identified identical nucleotide regions in bacteria belonging to distinct genera. To further resolve these shared sequences, we performed long-read sequencing on a subset of isolates and generated highly contiguous genomes. We then tracked the appearance of ten different plasmids in all 2173 genomes, and found evidence of plasmid transfer independent from bacterial transmission. Finally, we identified two instances of likely plasmid transfer within individual patients, including one plasmid that likely transferred to a second patient. This work expands our understanding of HGT in healthcare settings, and can inform efforts to limit the spread of drug-resistant pathogens in hospitals.
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Affiliation(s)
- Daniel R Evans
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, United States.,Department of Infectious Diseases and Microbiology, University of Pittsburgh Graduate School of Public Health, Pittsburgh, United States
| | - Marissa P Griffith
- Microbial Genomic Epidemiology Laboratory, Infectious Diseases Epidemiology Research Unit, University of Pittsburgh School of Medicine and Graduate School of Public Health, Pittsburgh, United States
| | - Alexander J Sundermann
- Microbial Genomic Epidemiology Laboratory, Infectious Diseases Epidemiology Research Unit, University of Pittsburgh School of Medicine and Graduate School of Public Health, Pittsburgh, United States
| | - Kathleen A Shutt
- Microbial Genomic Epidemiology Laboratory, Infectious Diseases Epidemiology Research Unit, University of Pittsburgh School of Medicine and Graduate School of Public Health, Pittsburgh, United States
| | - Melissa I Saul
- Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, United States
| | - Mustapha M Mustapha
- Microbial Genomic Epidemiology Laboratory, Infectious Diseases Epidemiology Research Unit, University of Pittsburgh School of Medicine and Graduate School of Public Health, Pittsburgh, United States
| | - Jane W Marsh
- Microbial Genomic Epidemiology Laboratory, Infectious Diseases Epidemiology Research Unit, University of Pittsburgh School of Medicine and Graduate School of Public Health, Pittsburgh, United States
| | - Vaughn S Cooper
- Department of Microbiology and Molecular Genetics, and Center for Evolutionary Biology and Medicine, University of Pittsburgh, Pittsburgh, United States
| | - Lee H Harrison
- Microbial Genomic Epidemiology Laboratory, Infectious Diseases Epidemiology Research Unit, University of Pittsburgh School of Medicine and Graduate School of Public Health, Pittsburgh, United States
| | - Daria Van Tyne
- Division of Infectious Diseases, University of Pittsburgh School of Medicine, Pittsburgh, United States
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20
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Sundermann AJ, Clancy CJ, Pasculle AW, Liu G, Cumbie RB, Driscoll E, Ayres A, Donahue L, Pergam SA, Abbo L, Andes DR, Chandrasekar P, Galdys AL, Hanson KE, Marr KA, Mayer J, Mehta S, Morris MI, Perfect J, Revankar SG, Smith B, Swaminathan S, Thompson GR, Varghese M, Vazquez J, Whimbey E, Wingard JR, Nguyen MH. How Clean Is the Linen at My Hospital? The Mucorales on Unclean Linen Discovery Study of Large United States Transplant and Cancer Centers. Clin Infect Dis 2020; 68:850-853. [PMID: 30299481 DOI: 10.1093/cid/ciy669] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2018] [Accepted: 09/18/2018] [Indexed: 02/01/2023] Open
Abstract
Mucormycosis outbreaks have been linked to contaminated linen. We performed fungal cultures on freshly-laundered linens at 15 transplant and cancer hospitals. At 33% of hospitals, the linens were visibly unclean. At 20%, Mucorales were recovered from >10% of linens. Studies are needed to understand the clinical significance of our findings.
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Affiliation(s)
- Alexander J Sundermann
- University of Pittsburgh Graduate School of Public Health, Pennsylvania.,University of Pittsburgh Medical Center, Pennsylvania
| | | | - A William Pasculle
- University of Pittsburgh Graduate School of Public Health, Pennsylvania.,University of Pittsburgh Medical Center, Pennsylvania
| | - Guojun Liu
- University of Pittsburgh School of Medicine, Pennsylvania
| | | | | | - Ashley Ayres
- University of Pittsburgh Medical Center, Pennsylvania
| | - Lisa Donahue
- University of Pittsburgh Medical Center, Pennsylvania
| | - Steven A Pergam
- Fred Hutchinson Cancer Research Center and University of Washington, Seattle
| | | | | | | | | | - Kimberly E Hanson
- University of Utah and Associated Regional and University Pathologists Laboratories, Salt Lake City
| | - Kieren A Marr
- Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Jeanmarie Mayer
- University of Utah Health Care Hospitals and Clinics, Salt Lake City
| | - Seema Mehta
- Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Michele I Morris
- University of Miami Hospital and Clinics/Sylvester Cancer Center, Florida
| | - John Perfect
- Duke University Medical Center, Durham, North Carolina
| | | | - Becky Smith
- Duke University Medical Center, Durham, North Carolina
| | | | | | | | - Jose Vazquez
- Medical College of Georgia at Augusta University
| | | | | | - M Hong Nguyen
- University of Pittsburgh School of Medicine, Pennsylvania
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21
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Sundermann AJ, Neill CW, Querry A, Galdys A, Rickens MJ, Muto CA. Optimization of Infectious Disease Exposure Notification to Emergency Response Employees in Accordance with Public Law 111-87 Section 2696. Am J Infect Control 2016. [DOI: 10.1016/j.ajic.2016.04.197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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