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

• Published in: European journal of clinical microbiology & infectious diseases Vol. 40; no. 5; pp. 1049 - 1061
• Main Authors: Lee, Alfred Lok Hang, To, Curtis Chun Kit, Lee, Angus Lang Sun, Chan, Ronald Cheong Kin, Wong, Janus Siu Him, Wong, Chun Wai, Chow, Viola Chi Ying, Lai, Raymond Wai Man
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2021; Springer Nature B.V
• Subjects: Algorithms; Antibiotics; Antiinfectives and antibacterials; Artificial neural networks; Bacteremia; Biomedical and Life Sciences; Biomedicine; Deep learning; E coli; Empirical analysis; Enterobacteriaceae; Internal Medicine; Klebsiella; Learning algorithms; Machine learning; Mathematical models; Medical Microbiology; Neural networks; Original Article; Prediction models; Regression models; Sensitivity; Statistical analysis; Statistical methods; Statistical models; β Lactamase
• ISSN: 0934-9723; 1435-4373
• DOI: 10.1007/s10096-020-04120-2

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.