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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.
- Source :
- European Journal of Clinical Microbiology & Infectious Diseases; May2021, Vol. 40 Issue 5, p1049-1061, 13p
- Publication Year :
- 2021
-
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. [ABSTRACT FROM AUTHOR]
- Subjects :
- DEEP learning
BACTEREMIA
ENTEROBACTERIACEAE
PREDICTION models
MACHINE learning
Subjects
Details
- Language :
- English
- ISSN :
- 09349723
- Volume :
- 40
- Issue :
- 5
- Database :
- Complementary Index
- Journal :
- European Journal of Clinical Microbiology & Infectious Diseases
- Publication Type :
- Academic Journal
- Accession number :
- 150064039
- Full Text :
- https://doi.org/10.1007/s10096-020-04120-2