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Threshold-free genomic cluster detection to track transmission pathways in health-care settings: a genomic epidemiology analysis
A crucial barrier to the routine application of whole-genome sequencing (WGS) for infection prevention is the insufficient criteria for determining whether a genomic linkage is consistent with transmission within the facility. We evaluated the use of single-nucleotide variant (SNV) thresholds, as we...
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Published in: | The Lancet. Microbe 2022-09, Vol.3 (9), p.e652-e662 |
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description | A crucial barrier to the routine application of whole-genome sequencing (WGS) for infection prevention is the insufficient criteria for determining whether a genomic linkage is consistent with transmission within the facility. We evaluated the use of single-nucleotide variant (SNV) thresholds, as well as a novel threshold-free approach, for inferring transmission linkages in a high-transmission setting.
We did a retrospective genomic epidemiology analysis of samples previously collected in the context of an intervention study at a long-term acute care hospital in the USA. We performed WGS on 435 isolates of Klebsiella pneumoniae harbouring the blaKPC carbapenemase (KPC-Kp) collected from 256 patients through admission and surveillance culturing (once every 2 weeks) of almost every patient who was admitted to hospital over a 1-year period.
Our analysis showed that the standard approach of using an SNV threshold to define transmission would lead to false-positive and false-negative inferences. False-positive inferences were driven by the frequent importation of closely related strains, which were presumably linked via transmission at connected health-care facilities. False-negative inferences stemmed from the diversity of colonising populations that were spread among patients, with multiple examples of hypermutator strain emergence within patients and, as a result, putative transmission links separated by large genetic distances. Motivated by limitations of an SNV threshold, we implemented a novel threshold-free transmission cluster inference approach, in which each of the acquired KPC-Kp isolates were linked back to the imported KPC-Kp isolate with which it shared the most variants. This approach yielded clusters that varied in levels of genetic diversity but where 105 (81%) of 129 unique strain acquisition events were associated with epidemiological links in the hospital. Of 100 patients who acquired KPC-Kp isolates that were included in a cluster, 47 could be linked to a single patient who was positive for KPC-Kp at admission, compared with 31 and 25 using 10 SNV and 20 SNV thresholds, respectively. Holistic examination of clusters highlighted extensive variation in the magnitude of onward transmission stemming from more than 100 importation events and revealed patterns in cluster propagation that could inform improvements to infection prevention strategies.
Our results show how the integration of culture surveillance data into genomic analyses can overco |
doi_str_mv | 10.1016/S2666-5247(22)00115-X |
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We did a retrospective genomic epidemiology analysis of samples previously collected in the context of an intervention study at a long-term acute care hospital in the USA. We performed WGS on 435 isolates of Klebsiella pneumoniae harbouring the blaKPC carbapenemase (KPC-Kp) collected from 256 patients through admission and surveillance culturing (once every 2 weeks) of almost every patient who was admitted to hospital over a 1-year period.
Our analysis showed that the standard approach of using an SNV threshold to define transmission would lead to false-positive and false-negative inferences. False-positive inferences were driven by the frequent importation of closely related strains, which were presumably linked via transmission at connected health-care facilities. False-negative inferences stemmed from the diversity of colonising populations that were spread among patients, with multiple examples of hypermutator strain emergence within patients and, as a result, putative transmission links separated by large genetic distances. Motivated by limitations of an SNV threshold, we implemented a novel threshold-free transmission cluster inference approach, in which each of the acquired KPC-Kp isolates were linked back to the imported KPC-Kp isolate with which it shared the most variants. This approach yielded clusters that varied in levels of genetic diversity but where 105 (81%) of 129 unique strain acquisition events were associated with epidemiological links in the hospital. Of 100 patients who acquired KPC-Kp isolates that were included in a cluster, 47 could be linked to a single patient who was positive for KPC-Kp at admission, compared with 31 and 25 using 10 SNV and 20 SNV thresholds, respectively. Holistic examination of clusters highlighted extensive variation in the magnitude of onward transmission stemming from more than 100 importation events and revealed patterns in cluster propagation that could inform improvements to infection prevention strategies.
Our results show how the integration of culture surveillance data into genomic analyses can overcome limitations of cluster detection based on SNV-thresholds and improve the ability to track pathways of pathogen transmission in health-care settings.
US Center for Disease Control and Prevention and University of Michigan.</description><identifier>ISSN: 2666-5247</identifier><identifier>EISSN: 2666-5247</identifier><identifier>DOI: 10.1016/S2666-5247(22)00115-X</identifier><identifier>PMID: 35803292</identifier><language>eng</language><publisher>England: Elsevier Ltd</publisher><subject>Disease Outbreaks ; Genomics ; Humans ; Klebsiella Infections - epidemiology ; Klebsiella pneumoniae - genetics ; Retrospective Studies</subject><ispartof>The Lancet. Microbe, 2022-09, Vol.3 (9), p.e652-e662</ispartof><rights>2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license</rights><rights>Copyright © 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license. Published by Elsevier Ltd.. All rights reserved.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c397t-5eebb67ee49c57055b7b4056151adcd740900f2fb0be26380e639505a8699c313</citedby><cites>FETCH-LOGICAL-c397t-5eebb67ee49c57055b7b4056151adcd740900f2fb0be26380e639505a8699c313</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S266652472200115X$$EHTML$$P50$$Gelsevier$$Hfree_for_read</linktohtml><link.rule.ids>230,314,780,784,885,3547,27923,27924,45779</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35803292$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Hawken, Shawn E</creatorcontrib><creatorcontrib>Yelin, Rachel D</creatorcontrib><creatorcontrib>Lolans, Karen</creatorcontrib><creatorcontrib>Pirani, Ali</creatorcontrib><creatorcontrib>Weinstein, Robert A</creatorcontrib><creatorcontrib>Lin, Michael Y</creatorcontrib><creatorcontrib>Hayden, Mary K</creatorcontrib><creatorcontrib>Snitkin, Evan S</creatorcontrib><creatorcontrib>CDC Prevention Epicenter Program</creatorcontrib><title>Threshold-free genomic cluster detection to track transmission pathways in health-care settings: a genomic epidemiology analysis</title><title>The Lancet. Microbe</title><addtitle>Lancet Microbe</addtitle><description>A crucial barrier to the routine application of whole-genome sequencing (WGS) for infection prevention is the insufficient criteria for determining whether a genomic linkage is consistent with transmission within the facility. We evaluated the use of single-nucleotide variant (SNV) thresholds, as well as a novel threshold-free approach, for inferring transmission linkages in a high-transmission setting.
We did a retrospective genomic epidemiology analysis of samples previously collected in the context of an intervention study at a long-term acute care hospital in the USA. We performed WGS on 435 isolates of Klebsiella pneumoniae harbouring the blaKPC carbapenemase (KPC-Kp) collected from 256 patients through admission and surveillance culturing (once every 2 weeks) of almost every patient who was admitted to hospital over a 1-year period.
Our analysis showed that the standard approach of using an SNV threshold to define transmission would lead to false-positive and false-negative inferences. False-positive inferences were driven by the frequent importation of closely related strains, which were presumably linked via transmission at connected health-care facilities. False-negative inferences stemmed from the diversity of colonising populations that were spread among patients, with multiple examples of hypermutator strain emergence within patients and, as a result, putative transmission links separated by large genetic distances. Motivated by limitations of an SNV threshold, we implemented a novel threshold-free transmission cluster inference approach, in which each of the acquired KPC-Kp isolates were linked back to the imported KPC-Kp isolate with which it shared the most variants. This approach yielded clusters that varied in levels of genetic diversity but where 105 (81%) of 129 unique strain acquisition events were associated with epidemiological links in the hospital. Of 100 patients who acquired KPC-Kp isolates that were included in a cluster, 47 could be linked to a single patient who was positive for KPC-Kp at admission, compared with 31 and 25 using 10 SNV and 20 SNV thresholds, respectively. Holistic examination of clusters highlighted extensive variation in the magnitude of onward transmission stemming from more than 100 importation events and revealed patterns in cluster propagation that could inform improvements to infection prevention strategies.
Our results show how the integration of culture surveillance data into genomic analyses can overcome limitations of cluster detection based on SNV-thresholds and improve the ability to track pathways of pathogen transmission in health-care settings.
US Center for Disease Control and Prevention and University of Michigan.</description><subject>Disease Outbreaks</subject><subject>Genomics</subject><subject>Humans</subject><subject>Klebsiella Infections - epidemiology</subject><subject>Klebsiella pneumoniae - genetics</subject><subject>Retrospective Studies</subject><issn>2666-5247</issn><issn>2666-5247</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><recordid>eNqFkU9v3CAQxVHVqomSfIRWHNOD2wEbbPfQKor6J1KkHpJIuSGMx2taDFtgU-0tHz12Nl2lp14ADW_eg_kR8obBewZMfrjiUspC8Ko-5fwdAGOiuH1BDvfll8_OB-QkpZ8AwAXjTIjX5KAUDZS85Yfk_nqMmMbg-mKIiHSFPkzWUOM2KWOkPWY02QZPc6A5avNrWX2abEpLda3z-EdvE7WejqhdHgujI9KEOVu_Sh-p3nvi2vY42eDCaku1126bbDomrwbtEp487Ufk5uuX6_PvxeWPbxfnZ5eFKds6FwKx62SNWLVG1CBEV3cVCMkE073p6wpagIEPHXTIZdkAyrIVIHQj29aUrDwin3a-6003YW_Qz_9wah3tpONWBW3VvzfejmoV7lQ7O5QVzAanTwYx_N5gymqegUHntMewSYrLpq45r5slS-ykJoaUIg77GAZqAageAaqFjuJcPQJUt3Pf2-dv3Hf9xTULPu8EOE_qzmJUyVj0BnsbZ0yqD_Y_EQ-9Gq6x</recordid><startdate>20220901</startdate><enddate>20220901</enddate><creator>Hawken, Shawn E</creator><creator>Yelin, Rachel D</creator><creator>Lolans, Karen</creator><creator>Pirani, Ali</creator><creator>Weinstein, Robert A</creator><creator>Lin, Michael Y</creator><creator>Hayden, Mary K</creator><creator>Snitkin, Evan S</creator><general>Elsevier Ltd</general><scope>6I.</scope><scope>AAFTH</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20220901</creationdate><title>Threshold-free genomic cluster detection to track transmission pathways in health-care settings: a genomic epidemiology analysis</title><author>Hawken, Shawn E ; Yelin, Rachel D ; Lolans, Karen ; Pirani, Ali ; Weinstein, Robert A ; Lin, Michael Y ; Hayden, Mary K ; Snitkin, Evan S</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c397t-5eebb67ee49c57055b7b4056151adcd740900f2fb0be26380e639505a8699c313</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Disease Outbreaks</topic><topic>Genomics</topic><topic>Humans</topic><topic>Klebsiella Infections - epidemiology</topic><topic>Klebsiella pneumoniae - genetics</topic><topic>Retrospective Studies</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hawken, Shawn E</creatorcontrib><creatorcontrib>Yelin, Rachel D</creatorcontrib><creatorcontrib>Lolans, Karen</creatorcontrib><creatorcontrib>Pirani, Ali</creatorcontrib><creatorcontrib>Weinstein, Robert A</creatorcontrib><creatorcontrib>Lin, Michael Y</creatorcontrib><creatorcontrib>Hayden, Mary K</creatorcontrib><creatorcontrib>Snitkin, Evan S</creatorcontrib><creatorcontrib>CDC Prevention Epicenter Program</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>The Lancet. Microbe</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Hawken, Shawn E</au><au>Yelin, Rachel D</au><au>Lolans, Karen</au><au>Pirani, Ali</au><au>Weinstein, Robert A</au><au>Lin, Michael Y</au><au>Hayden, Mary K</au><au>Snitkin, Evan S</au><aucorp>CDC Prevention Epicenter Program</aucorp><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Threshold-free genomic cluster detection to track transmission pathways in health-care settings: a genomic epidemiology analysis</atitle><jtitle>The Lancet. Microbe</jtitle><addtitle>Lancet Microbe</addtitle><date>2022-09-01</date><risdate>2022</risdate><volume>3</volume><issue>9</issue><spage>e652</spage><epage>e662</epage><pages>e652-e662</pages><issn>2666-5247</issn><eissn>2666-5247</eissn><abstract>A crucial barrier to the routine application of whole-genome sequencing (WGS) for infection prevention is the insufficient criteria for determining whether a genomic linkage is consistent with transmission within the facility. We evaluated the use of single-nucleotide variant (SNV) thresholds, as well as a novel threshold-free approach, for inferring transmission linkages in a high-transmission setting.
We did a retrospective genomic epidemiology analysis of samples previously collected in the context of an intervention study at a long-term acute care hospital in the USA. We performed WGS on 435 isolates of Klebsiella pneumoniae harbouring the blaKPC carbapenemase (KPC-Kp) collected from 256 patients through admission and surveillance culturing (once every 2 weeks) of almost every patient who was admitted to hospital over a 1-year period.
Our analysis showed that the standard approach of using an SNV threshold to define transmission would lead to false-positive and false-negative inferences. False-positive inferences were driven by the frequent importation of closely related strains, which were presumably linked via transmission at connected health-care facilities. False-negative inferences stemmed from the diversity of colonising populations that were spread among patients, with multiple examples of hypermutator strain emergence within patients and, as a result, putative transmission links separated by large genetic distances. Motivated by limitations of an SNV threshold, we implemented a novel threshold-free transmission cluster inference approach, in which each of the acquired KPC-Kp isolates were linked back to the imported KPC-Kp isolate with which it shared the most variants. This approach yielded clusters that varied in levels of genetic diversity but where 105 (81%) of 129 unique strain acquisition events were associated with epidemiological links in the hospital. Of 100 patients who acquired KPC-Kp isolates that were included in a cluster, 47 could be linked to a single patient who was positive for KPC-Kp at admission, compared with 31 and 25 using 10 SNV and 20 SNV thresholds, respectively. Holistic examination of clusters highlighted extensive variation in the magnitude of onward transmission stemming from more than 100 importation events and revealed patterns in cluster propagation that could inform improvements to infection prevention strategies.
Our results show how the integration of culture surveillance data into genomic analyses can overcome limitations of cluster detection based on SNV-thresholds and improve the ability to track pathways of pathogen transmission in health-care settings.
US Center for Disease Control and Prevention and University of Michigan.</abstract><cop>England</cop><pub>Elsevier Ltd</pub><pmid>35803292</pmid><doi>10.1016/S2666-5247(22)00115-X</doi><oa>free_for_read</oa></addata></record> |
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subjects | Disease Outbreaks Genomics Humans Klebsiella Infections - epidemiology Klebsiella pneumoniae - genetics Retrospective Studies |
title | Threshold-free genomic cluster detection to track transmission pathways in health-care settings: a genomic epidemiology analysis |
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