Clinical Prediction Score for Community-Onset Bloodstream Infections Caused by Extended-Spectrum Beta-Lactamase-Producing Escherichia coli and Klebsiella Species

Clinical scoring model for predicting cefotaxime-resistance in Klebsiella pneumoniae bacteremia: development and validation based on portal of entry

We developed a prediction model for cefotaxime resistance in patients with K. pneumoniae bacteremia. Adult patients with K. pneumoniae bacteremia were grouped into derivation (from March 2018 to December 2019) and validation (from January 2020 to August 2020) cohorts. The prediction scoring system was based on factors associated with cefotaxime resistance identified by the logistic regression model. A total of 358 patients were enrolled (256 for derivation, 102 for validation). In the multivariable analysis, age ≥65 years, hospital-acquired infection, prior antimicrobial use, and an updated Charlson comorbidity index ≥3 points were associated with cefotaxime resistance in the derivation cohort. When each variable was counted as 1 point, the values of the area under the curve were 0.761 in the derivation and 0.781 in the validation cohorts. The best cutoff value using the Youden index was ≥2 with 73.6% sensitivity and 67.5% specificity. Our simple scoring system favorably predicted cefotaxime resistance.

From Pathophysiological Hypotheses to Case–Control Study Design: Resistance from Antibiotic Exposure in Community-Onset Infections

Antimicrobial resistance is a global public health concern, at least partly due to the misuse of antibiotics. The increasing prevalence of antibiotic-resistant infections in the community has shifted at-risk populations into the general population. Numerous case–control studies attempt to better understand the link between antibiotic use and antibiotic-resistant community-onset infections. We review the designs of such studies, focusing on community-onset bloodstream and urinary tract infections. We highlight their methodological heterogeneity in the key points related to the antibiotic exposure, the population and design. We show the impact of this heterogeneity on study results, through the example of extended-spectrum β-lactamases producing Enterobacteriaceae. Finally, we emphasize the need for the greater standardization of such studies and discuss how the definition of a pathophysiological hypothesis specific to the bacteria–resistance pair studied is an important prerequisite to clarify the design of future studies.

A clinical prediction tool for extended-spectrum β-lactamase-producing Enterobacteriaceae urinary tract infection

Background

Prevalence of extended-spectrum beta-lactamase-producing-Enterobacteriaceae (ESBL-E) has risen in patients with urinary tract infections. The objective of this study was to determine explore the risk factors of ESBL-E infection in hospitalized patients and establish a predictive model.

Methods

This retrospective study included all patients with an Enterobacteriaceae-positive urine sample at the first affiliated hospital of Jinan university from January 2018 to December 2019. Antimicrobial susceptibility patterns of ESBL-E were analyzed, and multivariate analysis of related factors was performed. From these, a nomogram was established to predict the possibility of ESBL-E infection. Simultaneously, susceptibility testing of a broad array of carbapenem antibiotics was performed on ESBL-E cultures to explore possible alternative treatment options.

Results

Of the total 874 patients with urinary tract infections (UTIs), 272 (31.1%) were ESBL-E positive. In the predictive analysis, five variables were identified as independent risk factors for ESBL-E infection: male gender (OR = 1.607, 95% CI 1.066–2.416), older age (OR = 4.100, 95% CI 1.678–12.343), a hospital stay in preceding 3 months (OR = 1.872, 95% CI 1.141–3.067), invasive urological procedure (OR = 1.810, 95% CI 1.197–2.729), and antibiotic use within the previous 3 months (OR = 1.833, 95% CI 1.055–3.188). In multivariate analysis, the data set was divided into a training set of 611 patients and a validation set of 263 patients The model developed to predict ESBL-E infection was effective, with the AuROC of 0.650 (95% CI 0.577–0.725). Among the antibiotics tested, several showed very high effectiveness against ESBL-E: amikacin (85.7%), carbapenems (83.8%), tigecycline (97.1%) and polymyxin (98.2%).

Conclusions

The nomogram is useful for estimating a UTI patient’s likelihood of infection with ESBL-E. It could improve clinical decision making and enable more efficient empirical treatment. Empirical treatment may be informed by the results of the antibiotic susceptibility testing.

Comparison of antimicrobial resistances and clinical features in community-onset Escherichia coli and Klebsiella pneumoniae bacteremia

BACKGROUND/AIMS

The aim of this study was to compare antimicrobial resistance, clinical features, and outcomes of community-onset Escherichia coli (COEC) and Klebsiella pneumoniae (COKP) bacteremia.

METHODS

The medical records of patients diagnosed with E. coli or K. pneumoniae bacteremia in the emergency department of a 750-bed secondary care hospital in Daegu, Korea from January 2010 to December 2016 were retrospectively reviewed.

RESULTS

A total of 866 patients with COEC bacteremia and 299 with COKP bacteremia were enrolled. COEC bacteremia, compared to COKP bacteremia, had higher rates of 3rd generation cephalosporin (3GC) (18.8% vs. 8.4%, p < 0.001) and f luoroquinolone (FQ) (30.4% vs. 8.0%, p < 0.001) resistance. The patients with COKP bacteremia had higher Charlson comorbidity indices (CCI) (1.8 ± 2.0 vs. 1.5 ± 1.8, p = 0.035), Pittsburgh bacteremia scores (PBS) (2.0 ± 2.6 vs. 1.3 ± 1.8, p < 0.001), and 30-day mortality (14.44% vs. 8.8%, p = 0.008) than the patients with COEC bacteremia. Age younger than 70 years, male sex, polymicrobial infections, pneumonia, intra-abdominal infection, PBS ≥ 2, and Foley catheter insertion were independent predictive factors for COKP bacteremia compared to COEC bacteremia in the multivariate analysis. CCI, PBS, and intensive care unit admission were independent risk factors for 30-day mortality in the multivariate analysis.

CONCLUSION

3GCs and FQs are still useful for the empirical treatment of patients with probable COKP bacteremia. The patients with COKP bacteremia had worse outcomes because of its greater severity and more frequent underlying comorbidities.

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.