An easy-to-use scoring system for predicting bacteraemia with third-generation cephalosporin-resistant Enterobacterales in a low-resistance setting

Extended-spectrum beta-lactamase-producing Enterobacterales in patients with suspected sepsis in an acute care setting in Skåne, Sweden: a cohort study

INTRODUCTION

Epidemiological data on extended-spectrum beta-lactamase (ESBL)-producing Enterobacterales are most often based on microbiological laboratory isolates and do not consider important clinical data such as infection or colonisation, treatment, and outcome. This study aimed to assess prevalence of ESBL-producing Enterobacterales as the cause of infection in patients with suspected sepsis in the emergency department based on clinical data. It also examined the number of patients with suspected sepsis who had ESBL-producing pathogens, comparing estimates that were based on laboratory data versus a combination of laboratory and clinical data.

METHODS

Patients with suspected sepsis in the emergency department at Skåne University Hospital, Lund, Sweden were included consecutively. Data were collected retrospectively from medical records.

RESULTS

Of the 764 included patients, 223 patients had growth of Enterobacterales in any specimen (i.e. colonisation or infection according to laboratory data), while 191 patients had Enterobacterales detected in the blood or in the suspected focus of infection (i.e. an infection according to clinical and laboratory data). Eighteen patients had ESBL-producing Enterobacterales in any clinical specimen, 11 of whom had an infection with ESBL-producing Enterobacterales, resulting in a prevalence of infections with ESBL-producing Enterobacterales in infected patients with suspected sepsis of 1.8%. The number of patients with ESBL-producing Enterobacterales was not significantly different when infection was defined using laboratory data alone versus a combination of laboratory and clinical data [18/223 (8.1%) vs 11/191 (5.8%), p = 0.36].

CONCLUSIONS

The prevalence of ESBL-producing Enterobacterales infections among patients with suspected sepsis is low in an acute care setting in Sweden.

Predicting Extended-Spectrum Beta-Lactamase and Carbapenem Resistance in Enterobacteriaceae Bacteremia: A Diagnostic Model Systematic Review and Meta-Analysis

Enterobacteriaceae bacteremia, particularly when associated with antimicrobial resistance, can result in increased mortality, emphasizing the need for timely effective therapy. Clinical risk prediction models are promising tools, stratifying patients based on their risk of resistance due to ESBL and carbapenemase-producing Enterobacteriaceae in bloodstream infections (BSIs) and, thereby, improving therapeutic decisions. This systematic review and meta-analysis synthesized the literature on the performance of these models. Searches of PubMed and EMBASE led to the identification of 10 relevant studies with 6106 unique patient encounters. Nine studies concerned ESBL prediction, and one focused on the prediction of carbapenemases. For the two ESBL model derivation studies, the discrimination performance showed sensitivities of 53-85% and specificities of 93-95%. Among the four ESBL model derivation and validation studies, the sensitivities were 43-88%, and the specificities were 77-99%. The sensitivity and specificity for the subsequent external validation studies were 7-37% and 88-96%, respectively. For the three external validation studies, only two models were evaluated across multiple studies, with a pooled AUROC of 65-71%, with one study omitting the sensitivity/specificity. Only two studies measured clinical utility through hypothetical therapy assessments. Given the limited evidence on their interventional application, it would be beneficial to further assess these or future models, to better understand their clinical utility and ensure their safe and impactful implementation.

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

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.