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Article: Deep learning model for prediction of extended-spectrum beta-lactamase (ESBL) production in community-onset Enterobacteriaceae bacteraemia from a high ESBL prevalence multi-centre cohort

TitleDeep learning model for prediction of extended-spectrum beta-lactamase (ESBL) production in community-onset Enterobacteriaceae bacteraemia from a high ESBL prevalence multi-centre cohort
Authors
Issue Date2021
PublisherSpringer Verlag. The Journal's web site is located at http://link.springer.de/link/service/journals/10096/index.htm
Citation
European Journal of Clinical Microbiology & Infectious Diseases, 2021, v. 40 n. 5, p. 1049-1061 How to Cite?
AbstractAdequate empirical antimicrobial coverage is instrumental in clinical management of community-onset Enterobacteriaceae bacteraemia in areas with high ESBL prevalence, while balancing the risk of carbapenem overuse and emergence of carbapenem-resistant organisms. It is unknown whether machine learning offers additional advantages to conventional statistical methods in prediction of ESBL production. To develop a validated model to predict ESBL production in Enterobacteriaceae causing community-onset bacteraemia. 5625 patients with community-onset bacteraemia caused by Escherichia coli, Klebsiella species and Proteus mirabilis during 1 January 2015–31 December 2019 from three regional hospitals in Hong Kong were included in the analysis, after exclusion of blood cultures obtained beyond 48 h of admission. The prevalence of ESBL-producing Enterobacteriaceae was 23.7% (1335/5625). Deep neural network and other machine learning algorithms were compared against conventional statistical model via multivariable logistic regression. Primary outcomes compared consisted of predictive model area under curve of receiver-operator characteristic curve (AUC), and macro-averaged F1 score. Secondary outcomes included sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Deep neural network yielded an AUC of 0.761 (95% CI 0.725–0.797) and F1 score of 0.661 (95% CI 0.633–0.689), which was superior to logistic regression (AUC 0.667 (95% CI 0.627–0.707), F1 score 0.596 (95% CI 0.567–0.625)). Deep neural network had a specificity of 91.5%, sensitivity of 37.5%, NPV of 82.5%, and PPV of 57.9%. Deep neural network is superior to logistic regression in predicting ESBL production in Enterobacteriaceae causing community-onset bacteraemia in high-ESBL prevalence area. Machine learning offers clinical utility in guiding judicious empirical antibiotics use.
Persistent Identifierhttp://hdl.handle.net/10722/306715
ISSN
2023 Impact Factor: 3.7
2023 SCImago Journal Rankings: 1.020
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLee, ALH-
dc.contributor.authorTo, CCK-
dc.contributor.authorLee, ALS-
dc.contributor.authorChan, RCK-
dc.contributor.authorWong, JSH-
dc.contributor.authorWong, CW-
dc.contributor.authorChow, VCY-
dc.contributor.authorLai, RWM-
dc.date.accessioned2021-10-22T07:38:34Z-
dc.date.available2021-10-22T07:38:34Z-
dc.date.issued2021-
dc.identifier.citationEuropean Journal of Clinical Microbiology & Infectious Diseases, 2021, v. 40 n. 5, p. 1049-1061-
dc.identifier.issn0934-9723-
dc.identifier.urihttp://hdl.handle.net/10722/306715-
dc.description.abstractAdequate empirical antimicrobial coverage is instrumental in clinical management of community-onset Enterobacteriaceae bacteraemia in areas with high ESBL prevalence, while balancing the risk of carbapenem overuse and emergence of carbapenem-resistant organisms. It is unknown whether machine learning offers additional advantages to conventional statistical methods in prediction of ESBL production. To develop a validated model to predict ESBL production in Enterobacteriaceae causing community-onset bacteraemia. 5625 patients with community-onset bacteraemia caused by Escherichia coli, Klebsiella species and Proteus mirabilis during 1 January 2015–31 December 2019 from three regional hospitals in Hong Kong were included in the analysis, after exclusion of blood cultures obtained beyond 48 h of admission. The prevalence of ESBL-producing Enterobacteriaceae was 23.7% (1335/5625). Deep neural network and other machine learning algorithms were compared against conventional statistical model via multivariable logistic regression. Primary outcomes compared consisted of predictive model area under curve of receiver-operator characteristic curve (AUC), and macro-averaged F1 score. Secondary outcomes included sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Deep neural network yielded an AUC of 0.761 (95% CI 0.725–0.797) and F1 score of 0.661 (95% CI 0.633–0.689), which was superior to logistic regression (AUC 0.667 (95% CI 0.627–0.707), F1 score 0.596 (95% CI 0.567–0.625)). Deep neural network had a specificity of 91.5%, sensitivity of 37.5%, NPV of 82.5%, and PPV of 57.9%. Deep neural network is superior to logistic regression in predicting ESBL production in Enterobacteriaceae causing community-onset bacteraemia in high-ESBL prevalence area. Machine learning offers clinical utility in guiding judicious empirical antibiotics use.-
dc.languageeng-
dc.publisherSpringer Verlag. The Journal's web site is located at http://link.springer.de/link/service/journals/10096/index.htm-
dc.relation.ispartofEuropean Journal of Clinical Microbiology & Infectious Diseases-
dc.rightsThis is a post-peer-review, pre-copyedit version of an article published in [insert journal title]. The final authenticated version is available online at: https://doi.org/[insert DOI]-
dc.titleDeep learning model for prediction of extended-spectrum beta-lactamase (ESBL) production in community-onset Enterobacteriaceae bacteraemia from a high ESBL prevalence multi-centre cohort-
dc.typeArticle-
dc.identifier.emailWong, JSH: januswong@connect.hku.hk-
dc.identifier.authorityWong, JSH=rp02525-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/s10096-020-04120-2-
dc.identifier.pmid33399979-
dc.identifier.scopuseid_2-s2.0-85098979613-
dc.identifier.hkuros328758-
dc.identifier.volume40-
dc.identifier.issue5-
dc.identifier.spage1049-
dc.identifier.epage1061-
dc.identifier.isiWOS:000605099300003-
dc.publisher.placeGermany-

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