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Rose R, Feehan A, Lain BN, Ashcraft D, Nolan DJ, Velez-Climent L, Huston C, LaFleur T, Rosenthal S, Fogel GB, Miele L, Pankey G, Garcia-Diaz J, Lamers SL. Whole-genome sequencing of carbapenem-resistant Enterobacterales isolates in southeast Louisiana reveals persistent genetic clusters spanning multiple locations. J Infect Public Health 2023; 16:1911-1917. [PMID: 37866269 DOI: 10.1016/j.jiph.2023.10.013] [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] [Received: 05/01/2023] [Revised: 10/02/2023] [Accepted: 10/08/2023] [Indexed: 10/24/2023] Open
Abstract
BACKGROUND We investigated 51 g-negative carbapenem-resistant Enterobacterales (CRE) isolates collected from 22 patients over a five-year period from six health care institutions in the Ochsner Health network in southeast Louisiana. METHODS Short genomic reads were generated using Illumina sequencing and assembled for each isolate. Isolates were classified as Enterobacter spp. (n = 20), Klebsiella spp. (n = 30), and Escherichia coli (n = 1) and grouped into 19 different multi-locus sequence types (MLST). Species and patient-specific core genomes were constructed representing ∼50% of the chromosomal genome. RESULTS We identified two sets of patients with genetically related infections; in both cases, the related isolates were collected > 6 months apart, and in one case, the isolates were collected in different locations. On the other hand, we identified four sets of patients with isolates of the same species collected within 21 days from the same location; however, none had genetically related infections. Genes associated with resistance to carbapenem drugs (blaKPC and/or blaCTX-M-15) were found in 76% of the isolates. We found three blaKPC variants (blaKPC-2, blaKPC-3, and blaKPC-4) associated with four different Enterobacter MLST variants, and two blaKPC variants (blaKPC-2, blaKPC-3) associated with seven different Klebsiella MLST variants. CONCLUSIONS Molecular surveillance is increasingly becoming a powerful tool to understand bacterial spread in both community and clinical settings. This study provides evidence that genetically related infections in clinical settings do not necessarily reflect temporal associations, and vice versa. Our results also highlight the regional genomic and resistance diversity within related bacterial lineages.
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Affiliation(s)
- Rebecca Rose
- BioInfoExperts, LLC, Thibodaux, LA, USA; FoxSeq, LLC, Thibodaux, LA, USA.
| | - Amy Feehan
- Infectious Disease Clinical Research, Ochsner Clinic Foundation, New Orleans, LA, USA
| | | | - Deborah Ashcraft
- Infectious Disease Translational Research, Ochsner Clinic Foundation, New Orleans, LA, USA
| | | | | | | | | | | | | | - Lucio Miele
- Translational Science and Genetics at LSU Health Science Center, New Orleans, LA, USA
| | - George Pankey
- Infectious Disease Translational Research, Ochsner Clinic Foundation, New Orleans, LA, USA
| | - Julia Garcia-Diaz
- Infectious Disease Clinical Research, Ochsner Clinic Foundation, New Orleans, LA, USA
| | - Susanna L Lamers
- BioInfoExperts, LLC, Thibodaux, LA, USA; FoxSeq, LLC, Thibodaux, LA, USA
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2
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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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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. ANTIMICROBIAL STEWARDSHIP & HEALTHCARE EPIDEMIOLOGY : ASHE 2022; 2:e91. [PMID: 36483409 PMCID: PMC9726481 DOI: 10.1017/ash.2021.241] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [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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4
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SARS-CoV-2 genomic surveillance as an evidence-based infection control approach in an offshore petroleum employee population. Am J Infect Control 2022; 50:1013-1019. [PMID: 35598812 PMCID: PMC9119720 DOI: 10.1016/j.ajic.2022.05.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 05/11/2022] [Accepted: 05/13/2022] [Indexed: 11/21/2022]
Abstract
BACKGROUND Industrial hygienists (IH) in the oil and gas business instituted an extraordinary number of safety protocols to limit spread of SARS-CoV-2 onto offshore platforms in the Gulf of Mexico. We used genomic surveillance to provide actionable information concerning the efficacy of their efforts. METHODS Over 6 months, employees at a single company were serology and PCR tested during a 1-5 day predeployment quarantine and when postdeployment symptoms were reported. From each positive test (n = 49), SARS-CoV-2 genomes were sequenced. Phylogenetic analysis was used to investigate the epidemiology of transmissions. RESULTS Genomic surveillance confirmed 2 viral strains were infecting 18 offshore workers. Genomic data combined with epidemiological data suggested that a change in quarantine protocols contributed to these outbreaks. A pre-deployment outbreak involved a WHO variant of interest (Theta) that had infected 4 international workers. Two additional predeployment clusters of infections were identified. CONCLUSIONS Our findings support that IH quarantine/testing protocols limited viral transmissions, halted offshore outbreaks, and stopped the spread of a variant of interest. The study demonstrates how genomic data can be used to understand viral transmission dynamics in employee populations and evaluate safety protocols in the offshore oil and gas industry.
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5
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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: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [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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