Whole-Genome Sequencing Surveillance and Machine Learning of the Electronic Health Record for Enhanced Healthcare Outbreak Detection
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 electro...
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Veröffentlicht in: | Clinical infectious diseases 2022-08, Vol.75 (3), p.476-482 |
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creator | Sundermann, Alexander J Chen, Jieshi Kumar, Praveen Ayres, Ashley M Cho, Shu Ting Ezeonwuka, Chinelo Griffith, Marissa P Miller, James K Mustapha, Mustapha M Pasculle, A William Saul, Melissa I Shutt, Kathleen A Srinivasa, Vatsala Waggle, Kady Snyder, Daniel J Cooper, Vaughn S Van Tyne, Daria Snyder, Graham M Marsh, Jane W Dubrawski, Artur Roberts, Mark S Harrison, Lee H |
description | 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 with traditional IP practice during the same period.
Results
Of 3165 isolates, there were 2752 unique patient isolates in 99 clusters involving 297 (10.8%) patient isolates identified by WGS; clusters ranged from 2–14 patients. At least 1 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.
Conclusions
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.
Whole-genome sequencing surveillance of bacterial pathogens and machine learning of the electronic health record finds previously undetected outbreaks and their transmission routes, which can increase patient safety and save costs. |
doi_str_mv | 10.1093/cid/ciab946 |
format | Article |
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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 with traditional IP practice during the same period.
Results
Of 3165 isolates, there were 2752 unique patient isolates in 99 clusters involving 297 (10.8%) patient isolates identified by WGS; clusters ranged from 2–14 patients. At least 1 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.
Conclusions
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.
Whole-genome sequencing surveillance of bacterial pathogens and machine learning of the electronic health record finds previously undetected outbreaks and their transmission routes, which can increase patient safety and save costs.</description><identifier>ISSN: 1058-4838</identifier><identifier>EISSN: 1537-6591</identifier><identifier>DOI: 10.1093/cid/ciab946</identifier><identifier>PMID: 34791136</identifier><language>eng</language><publisher>US: Oxford University Press</publisher><subject>Cross Infection - epidemiology ; Cross Infection - microbiology ; Cross Infection - prevention & control ; Delivery of Health Care ; Disease Outbreaks ; Electronic Health Records ; Genome, Bacterial ; Humans ; Machine Learning ; Major ; Whole Genome Sequencing - methods</subject><ispartof>Clinical infectious diseases, 2022-08, Vol.75 (3), p.476-482</ispartof><rights>The Author(s) 2021. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: journals.permissions@oup.com. 2021</rights><rights>The Author(s) 2021. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: journals.permissions@oup.com.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c412t-188f5c05efe319962c1132fb180a11c9d13686d5b31af3b792c003307a7015883</citedby><cites>FETCH-LOGICAL-c412t-188f5c05efe319962c1132fb180a11c9d13686d5b31af3b792c003307a7015883</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,776,780,881,1578,27901,27902</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34791136$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Sundermann, Alexander J</creatorcontrib><creatorcontrib>Chen, Jieshi</creatorcontrib><creatorcontrib>Kumar, Praveen</creatorcontrib><creatorcontrib>Ayres, Ashley M</creatorcontrib><creatorcontrib>Cho, Shu Ting</creatorcontrib><creatorcontrib>Ezeonwuka, Chinelo</creatorcontrib><creatorcontrib>Griffith, Marissa P</creatorcontrib><creatorcontrib>Miller, James K</creatorcontrib><creatorcontrib>Mustapha, Mustapha M</creatorcontrib><creatorcontrib>Pasculle, A William</creatorcontrib><creatorcontrib>Saul, Melissa I</creatorcontrib><creatorcontrib>Shutt, Kathleen A</creatorcontrib><creatorcontrib>Srinivasa, Vatsala</creatorcontrib><creatorcontrib>Waggle, Kady</creatorcontrib><creatorcontrib>Snyder, Daniel J</creatorcontrib><creatorcontrib>Cooper, Vaughn S</creatorcontrib><creatorcontrib>Van Tyne, Daria</creatorcontrib><creatorcontrib>Snyder, Graham M</creatorcontrib><creatorcontrib>Marsh, Jane W</creatorcontrib><creatorcontrib>Dubrawski, Artur</creatorcontrib><creatorcontrib>Roberts, Mark S</creatorcontrib><creatorcontrib>Harrison, Lee H</creatorcontrib><title>Whole-Genome Sequencing Surveillance and Machine Learning of the Electronic Health Record for Enhanced Healthcare Outbreak Detection</title><title>Clinical infectious diseases</title><addtitle>Clin Infect Dis</addtitle><description>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 with traditional IP practice during the same period.
Results
Of 3165 isolates, there were 2752 unique patient isolates in 99 clusters involving 297 (10.8%) patient isolates identified by WGS; clusters ranged from 2–14 patients. At least 1 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.
Conclusions
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.
Whole-genome sequencing surveillance of bacterial pathogens and machine learning of the electronic health record finds previously undetected outbreaks and their transmission routes, which can increase patient safety and save costs.</description><subject>Cross Infection - epidemiology</subject><subject>Cross Infection - microbiology</subject><subject>Cross Infection - prevention & control</subject><subject>Delivery of Health Care</subject><subject>Disease Outbreaks</subject><subject>Electronic Health Records</subject><subject>Genome, Bacterial</subject><subject>Humans</subject><subject>Machine Learning</subject><subject>Major</subject><subject>Whole Genome Sequencing - methods</subject><issn>1058-4838</issn><issn>1537-6591</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kc1P3DAQxa0KVD7aU-_IJ1QJhXriOLEvSBVsAWkRUmnVo-U4E2LI2ouTIHHvH15vd0Fw4WDZ0vz8Zt48Qr4AOwam-DfrmnRMrYryA9kFwausFAq20psJmRWSyx2yNwx3jAFIJj6SHV5UCoCXu-Tvny70mJ2jDwukN_gwobfO39KbKT6i63vjLVLjG3plbOc80jma6FdEaOnYIZ31aMcYvLP0Ak0_dvQn2hAb2oZIZ75bCTSbkjUR6fU01hHNPT3DMX11wX8i263pB_y8uffJ7x-zX6cX2fz6_PL0-zyzBeRjBlK2wjKBLXJQqsxt8pC3dTJlAKxqkiNZNqLmYFpeVyq3jHHOKlMxEFLyfXKy1l1O9QIbi36MptfL6BYmPulgnH5b8a7Tt-FRqyKvgBdJ4OtGIIa0qWHUCzdYXG0JwzToXCjF_pMJPVqjNoZhiNi-tAGmV7nplJve5Jbog9eTvbDPQSXgcA2Eafmu0j8h66NT</recordid><startdate>20220831</startdate><enddate>20220831</enddate><creator>Sundermann, Alexander J</creator><creator>Chen, Jieshi</creator><creator>Kumar, Praveen</creator><creator>Ayres, Ashley M</creator><creator>Cho, Shu Ting</creator><creator>Ezeonwuka, Chinelo</creator><creator>Griffith, Marissa P</creator><creator>Miller, James K</creator><creator>Mustapha, Mustapha M</creator><creator>Pasculle, A William</creator><creator>Saul, Melissa I</creator><creator>Shutt, Kathleen A</creator><creator>Srinivasa, Vatsala</creator><creator>Waggle, Kady</creator><creator>Snyder, Daniel J</creator><creator>Cooper, Vaughn S</creator><creator>Van Tyne, Daria</creator><creator>Snyder, Graham M</creator><creator>Marsh, Jane W</creator><creator>Dubrawski, Artur</creator><creator>Roberts, Mark S</creator><creator>Harrison, Lee H</creator><general>Oxford University Press</general><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>20220831</creationdate><title>Whole-Genome Sequencing Surveillance and Machine Learning of the Electronic Health Record for Enhanced Healthcare Outbreak Detection</title><author>Sundermann, Alexander J ; Chen, Jieshi ; Kumar, Praveen ; Ayres, Ashley M ; Cho, Shu Ting ; Ezeonwuka, Chinelo ; Griffith, Marissa P ; Miller, James K ; Mustapha, Mustapha M ; Pasculle, A William ; Saul, Melissa I ; Shutt, Kathleen A ; Srinivasa, Vatsala ; Waggle, Kady ; Snyder, Daniel J ; Cooper, Vaughn S ; Van Tyne, Daria ; Snyder, Graham M ; Marsh, Jane W ; Dubrawski, Artur ; Roberts, Mark S ; Harrison, Lee H</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c412t-188f5c05efe319962c1132fb180a11c9d13686d5b31af3b792c003307a7015883</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Cross Infection - epidemiology</topic><topic>Cross Infection - microbiology</topic><topic>Cross Infection - prevention & control</topic><topic>Delivery of Health Care</topic><topic>Disease Outbreaks</topic><topic>Electronic Health Records</topic><topic>Genome, Bacterial</topic><topic>Humans</topic><topic>Machine Learning</topic><topic>Major</topic><topic>Whole Genome Sequencing - methods</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Sundermann, Alexander J</creatorcontrib><creatorcontrib>Chen, Jieshi</creatorcontrib><creatorcontrib>Kumar, Praveen</creatorcontrib><creatorcontrib>Ayres, Ashley M</creatorcontrib><creatorcontrib>Cho, Shu Ting</creatorcontrib><creatorcontrib>Ezeonwuka, Chinelo</creatorcontrib><creatorcontrib>Griffith, Marissa P</creatorcontrib><creatorcontrib>Miller, James K</creatorcontrib><creatorcontrib>Mustapha, Mustapha M</creatorcontrib><creatorcontrib>Pasculle, A William</creatorcontrib><creatorcontrib>Saul, Melissa I</creatorcontrib><creatorcontrib>Shutt, Kathleen A</creatorcontrib><creatorcontrib>Srinivasa, Vatsala</creatorcontrib><creatorcontrib>Waggle, Kady</creatorcontrib><creatorcontrib>Snyder, Daniel J</creatorcontrib><creatorcontrib>Cooper, Vaughn S</creatorcontrib><creatorcontrib>Van Tyne, Daria</creatorcontrib><creatorcontrib>Snyder, Graham M</creatorcontrib><creatorcontrib>Marsh, Jane W</creatorcontrib><creatorcontrib>Dubrawski, Artur</creatorcontrib><creatorcontrib>Roberts, Mark S</creatorcontrib><creatorcontrib>Harrison, Lee H</creatorcontrib><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>Clinical infectious diseases</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Sundermann, Alexander J</au><au>Chen, Jieshi</au><au>Kumar, Praveen</au><au>Ayres, Ashley M</au><au>Cho, Shu Ting</au><au>Ezeonwuka, Chinelo</au><au>Griffith, Marissa P</au><au>Miller, James K</au><au>Mustapha, Mustapha M</au><au>Pasculle, A William</au><au>Saul, Melissa I</au><au>Shutt, Kathleen A</au><au>Srinivasa, Vatsala</au><au>Waggle, Kady</au><au>Snyder, Daniel J</au><au>Cooper, Vaughn S</au><au>Van Tyne, Daria</au><au>Snyder, Graham M</au><au>Marsh, Jane W</au><au>Dubrawski, Artur</au><au>Roberts, Mark S</au><au>Harrison, Lee H</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Whole-Genome Sequencing Surveillance and Machine Learning of the Electronic Health Record for Enhanced Healthcare Outbreak Detection</atitle><jtitle>Clinical infectious diseases</jtitle><addtitle>Clin Infect Dis</addtitle><date>2022-08-31</date><risdate>2022</risdate><volume>75</volume><issue>3</issue><spage>476</spage><epage>482</epage><pages>476-482</pages><issn>1058-4838</issn><eissn>1537-6591</eissn><abstract>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 with traditional IP practice during the same period.
Results
Of 3165 isolates, there were 2752 unique patient isolates in 99 clusters involving 297 (10.8%) patient isolates identified by WGS; clusters ranged from 2–14 patients. At least 1 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.
Conclusions
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.
Whole-genome sequencing surveillance of bacterial pathogens and machine learning of the electronic health record finds previously undetected outbreaks and their transmission routes, which can increase patient safety and save costs.</abstract><cop>US</cop><pub>Oxford University Press</pub><pmid>34791136</pmid><doi>10.1093/cid/ciab946</doi><tpages>7</tpages><oa>free_for_read</oa></addata></record> |
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source | Oxford University Press Journals All Titles (1996-Current); MEDLINE; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Alma/SFX Local Collection |
subjects | Cross Infection - epidemiology Cross Infection - microbiology Cross Infection - prevention & control Delivery of Health Care Disease Outbreaks Electronic Health Records Genome, Bacterial Humans Machine Learning Major Whole Genome Sequencing - methods |
title | Whole-Genome Sequencing Surveillance and Machine Learning of the Electronic Health Record for Enhanced Healthcare Outbreak Detection |
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