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.
Eur J Clin Microbiol Infect Dis
; 40(5): 1049-1061, 2021 May.
Article
en En
| MEDLINE
| ID: mdl-33399979
ABSTRACT
Adequate 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.
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Beta-Lactamasas
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Enterobacteriaceae
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Infecciones por Enterobacteriaceae
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Aprendizaje Profundo
Tipo de estudio:
Clinical_trials
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Etiology_studies
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Incidence_studies
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Observational_studies
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Prevalence_studies
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Prognostic_studies
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Risk_factors_studies
Límite:
Humans
País/Región como asunto:
Asia
Idioma:
En
Revista:
Eur J Clin Microbiol Infect Dis
Asunto de la revista:
DOENCAS TRANSMISSIVEIS
/
MICROBIOLOGIA
Año:
2021
Tipo del documento:
Article
País de afiliación:
Hong Kong