Significant clinical differences but not outcomes between Klebsiella aerogenes and Enterobacter cloacae bloodstream infections: a comparative cohort study

Trends in Enterobacterales Bloodstream Infections in Children

OBJECTIVES

Enterobacterales bloodstream infections (E-BSI) cause a significant burden of disease in children and are associated with antimicrobial resistance. We assessed temporal changes in the population-based incidence of E-BSI in children in Queensland, Australia.

METHODS

We conducted a cohort study of incidents of E-BSI occurring in children in Queensland between 2000 and 2019, with a total population of 19.7 million child years. Infections were linked to clinical outcomes in hospital admissions and vital statistics databases. We estimated age- and sex-standardized E-BSI incidence rates over time. Secondary outcomes included the proportion of extended-spectrum β-lactamase phenotypes per year, hospital length of stay, and mortality.

RESULTS

We identified 1980 E-BSI in 1795 children. The overall age- and sex-standardized incidence rate was 9.9 cases per 100 000 child years, which increased from 7.3 to 12.9 over the period studied, an increase of 3.9% (95% confidence interval: 3.1-4.7) per year. There were 3.6 cases of E. coli bloodstream infection per 100 000 child years, increasing annually by 4.7% (3.5-5.9). The Salmonella sp. bloodstream infection incidence was 3.0 cases per 100 000 child years, which increased from 2013 by 13.7% (3.8-24.3) per year. The proportion of extended-spectrum β-lactamase E. coli increased over time. Mortality and length of stay were higher among children with comorbidities than those without (4.0% vs 0.3%, and 14 vs 4 days, respectively, P < .001).

CONCLUSIONS

The age- and sex-standardized incidence of E-BSI almost doubled in Queensland children over 2 decades, driven by increases in Salmonella sp. and E. coli. Increasing resistance of E. coli should prompt the inclusion of children in antimicrobial clinical trials.

Population genomics uncovers global distribution, antimicrobial resistance, and virulence genes of the opportunistic pathogen Klebsiella aerogenes

Klebsiella aerogenes is an understudied and clinically important pathogen. We therefore investigate its population structure by genome analysis aligned with metadata. We sequence 130 non-duplicated K. aerogenes clinical isolates and identify two inter-patient transmission events. We then retrieve all publicly available K. aerogenes genomes (n = 1,026, accessed by January 1, 2023) and analyze them with our 130 genomes. We develop a core-genome multi-locus sequence-typing scheme. We find that K. aerogenes is a species complex comprising four phylogroups undergoing evolutionary divergence, likely forming three species. We delineate remarkable clonal diversity and identify three worldwide-distributed carbapenemase-encoding clonal clusters, representing high-risk lineages. We uncover that K. aerogenes has an open genome equipped by a large arsenal of antimicrobial resistance genes. We identify two genetic regions specific for K. aerogenes, encoding a type VI secretion system and flagella/chemotaxis for motility, respectively, both contributing to the virulence. These results provide much-needed insights into the population structure and pan-genomes of K. aerogenes.

Diagnostic strategy of metagenomic next-generation sequencing for gram negative bacteria in respiratory infections

OBJECTIVE

This study aims to identify the most effective diagnostic method for distinguishing pathogenic and non-pathogenic Gram-negative bacteria (GNB) in suspected pneumonia cases using metagenomic next-generation sequencing (mNGS) on bronchoalveolar lavage fluid (BALF) samples.

METHODS

The effectiveness of mNGS was assessed on BALF samples collected from 583 patients, and the results were compared with those from microbiological culture and final clinical diagnosis. Three interpretational approaches were evaluated for diagnostic accuracy.

RESULTS

mNGS outperformed culture significantly. Among the interpretational approaches, Clinical Interpretation (CI) demonstrated the best diagnostic performance with a sensitivity of 87.3%, specificity of 100%, positive predictive value of 100%, and negative predictive value of 98.3%. CI's specificity was significantly higher than Simple Interpretation (SI) at 37.9%. Additionally, CI excluded some microorganisms identified as putative pathogens by SI, including Haemophilus parainfluenzae, Haemophilus parahaemolyticus, and Klebsiella aerogenes.

CONCLUSION

Proper interpretation of mNGS data is crucial for accurately diagnosing respiratory infections caused by GNB. CI is recommended for this purpose.

Economic and health impact modelling of a whole genome sequencing-led intervention strategy for bacterial healthcare-associated infections for England and for the USA

Bacterial healthcare-associated infections (HAIs) are a substantial source of global morbidity and mortality. The estimated cost associated with HAIs ranges from $35 to $45 billion in the USA alone. The costs and accessibility of whole genome sequencing (WGS) of bacteria and the lack of sufficiently accurate, high-resolution, scalable and accessible analysis for strain identification are being addressed. Thus, it is timely to determine the economic viability and impact of routine diagnostic bacterial genomics. The aim of this study was to model the economic impact of a WGS surveillance system that proactively detects and directs interventions for nosocomial infections and outbreaks compared to the current standard of care, without WGS. Using a synthesis of published models, inputs from national statistics, and peer-reviewed articles, the economic impacts of conducting a WGS-led surveillance system addressing the 11 most common nosocomial pathogen groups in England and the USA were modelled. This was followed by a series of sensitivity analyses. England was used to establish the baseline model because of the greater availability of underpinning data, and this was then modified using USA-specific parameters where available. The model for the NHS in England shows bacterial HAIs currently cost the NHS around £3 billion. WGS-based surveillance delivery is predicted to cost £61.1 million associated with the prevention of 74 408 HAIs and 1257 deaths. The net cost saving was £478.3 million, of which £65.8 million were from directly incurred savings (antibiotics, consumables, etc.) and £412.5 million from opportunity cost savings due to re-allocation of hospital beds and healthcare professionals. The USA model indicates that the bacterial HAI care baseline costs are around $18.3 billion. WGS surveillance costs $169.2 million, and resulted in a net saving of ca.$3.2 billion, while preventing 169 260 HAIs and 4862 deaths. From a 'return on investment' perspective, the model predicts a return to the hospitals of £7.83 per £1 invested in diagnostic WGS in the UK, and US$18.74 per $1 in the USA. Sensitivity analyses show that substantial savings are retained when inputs to the model are varied within a wide range of upper and lower limits. Modelling a proactive WGS system addressing HAI pathogens shows significant improvement in morbidity and mortality while simultaneously achieving substantial savings to healthcare facilities that more than offset the cost of implementing diagnostic genomics surveillance.