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
Hauptverfasser: 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
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container_end_page 482
container_issue 3
container_start_page 476
container_title Clinical infectious diseases
container_volume 75
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
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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 &amp; 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. 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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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