Hypervirulent Klebsiella pneumoniae strains (hvKp) can cause invasive community-acquired infections in healthy patients of all ages. In this study, the prevalence of putative hvKp in a German tertiary center was investigated and hvKp were characterized by phenotypic and molecular assays. All K. pneumoniae isolates in routine microbiological diagnostics from a single center were screened by string-testing over a period of 6 months. String-test positive (≥ 0.5 mm) isolates were re-evaluated on different media and under various conditions (aerobe, anaerobe). For string-test positive isolates, genes (magA, iutA, rmpA and rmpA2) associated with hypermucoviscosity and hypervirulence were amplified by multiplex PCR. PCR-positive isolates were subjected to whole-genome sequencing and sedimentation and biofilm formation assays. From 1310 screened K. pneumoniae isolates in clinical routine 100 isolates (7.6%) were string test positive. From these, 9% (n = 9) were defined as putative hvKp (string-test+/PCR+). Highest rate of string-test-positive isolates was observed on MacConkey agar under aerobic conditions. Amongst these nine putative hvKp isolates, the international lineage ST23 carrying hvKp-plasmid pKpVP-1 was the most common, but also a rare ST86 with pKpVP-2 was identified. All nine isolates showed hypermucoviscosity and weak biofilm formation. In conclusion, 9% of string-positive, respectively 0.69% of all K. pneumoniae isolates from routine were defined as putative hypervirulent. MacConkey agar was the best medium for hvKp screening.

The isolation of affected patients and intensified infection control measures are used to prevent nosocomial transmission of vancomycin-resistant enterococci (VRE), but early detection of VRE carriers is needed. However, there are still no standard screening criteria for VRE, which poses a significant threat to patient safety. Our study aimed to develop and evaluate an artificial intelligence (AI)-based approach for identifying and predicting of at-risk patients who could assist infection prevention and control staff through a human-in-the-loop approach. We used data from 8,372 patients, combining more than 125,000 movements within our hospital with patient-related information to create time-dependent graph sequences and applied graph neural networks (GNNs) to classify patients as VRE carriers or noncarriers. Our model achieves a macro F1 score of 0.880 on the task (sensitivity of 0.808, specificity of 0.942). The parameters with the strongest impact on the prediction are the codes for clinical diagnosis (ICD) and operations/procedures (OPS), which are integrated as high-dimensional patient node features in our model. We demonstrate that modeling a “living” hospital with a GNN is a promising approach for the early detection of potential VRE carriers. This proves that AI-based tools combining heterogeneous information types can predict VRE carriage with high sensitivity and could therefore serve as a promising basis for future automated infection prevention control systems. Such systems could help enhance patient safety and proactively reduce nosocomial transmission events through targeted, cost-efficient interventions. Moreover, they could enable a more effective approach to managing antimicrobial resistance.

Introduction: Vancomycin-resistant Enterococcus faecium (VREfm) isolates of sequence type (ST)1299 were described recently in south-eastern German hospitals and rapidly expanded from local to cross-border level. Aim: We describe the spread of the novel VREfm strain ST1299/vanA on a genetic, geographical and temporal level during the first 5 years after its detection. Methods: At University Hospital Regensburg (UHoR), routine VREfm surveillance is whole genome sequencing-based (≥ 1 VREfm per van-genotype, patient and year). In this observational cohort study, we analysed one VREfm ST1299 isolate from our database (2016–2022) per patient and year. Isolates were added from the Hospital of the Merciful Brothers Regensburg (MBR), the National Reference Centre for Staphylococci and Enterococci (NRC), and clinical isolates from Austria. Results: We identified 635 VREfm ST1299 isolates (100% vanA), including 504 from Regensburg, and 113 blood cultures. ST1299 isolates were first detected in 2018 simultaneously in Regensburg (n = 2) and southern Bavaria (n = 2), with local (UHoR) and regional numbers increasing rapidly from 2020, shifting to national scale in the same year. Genome data, analysed by cgMLST, showed a predominance of ST1299/CT1903 (315/504 isolates, 62.5%) and ST1299/CT3109 (127/504 isolates, 25.2%) isolates from Regensburg. By 2021, ST1299/CT1903 reached Upper Austria causing hospital outbreaks (n = 5). Phylogeny analysis suggests common ancestors with VREfm ST80, ST18 and ST17. Conclusion: Since their emergence in 2018, two highly transmissible subtypes of ST1299/vanA reached national, then cross-border scale. The observed outbreak tendency may explain the rapid and successful spread and the high clonality in our collection.

Citrobacter freundii is a nosocomial pathogen increasingly associated with multidrug resistance and hospital outbreaks. Despite its growing clinical relevance, no standardized core genome multilocus sequence typing (cgMLST) scheme has been available for high-resolution epidemiological analyses. Here, we developed and validated a robust cgMLST scheme for C. freundii comprising 3,250 target loci based on a curated data set of 825 globally distributed genomes representing extensive sequence type and geographic diversity. Validation against published outbreak data sets from hospitals in Finland and Belgium, as well as environmental and patient isolates from two German university hospitals, proved that the scheme possesses high target gene coverage (median 99.6%) and strong discriminatory power. Additionally, we developed a combined cgMLST scheme for C. freundii , C. portucalensis , C. braakii , and C. europaeus , based on 2,307 shared target loci and target gene coverages of ≥99.7%. This scheme proved suitable for cross-species outbreak analysis. Our analyses revealed environments, such as sinks, shower drains, and toilets, as likely reservoirs where Citrobacter species may persist in hospital settings. These findings suggest that environmental sources could play a significant role in transmission events involving patients, while allele-based cluster analyses indicated that direct patient-to-patient transmission was rare. Given the increasing prevalence of multidrug-resistant (MDR) Citrobacter strains, the level of discrimination achieved by these newly developed cgMLST schemes underscores the importance of accurate species identification and environmental screening in understanding the transmission dynamics of opportunistic healthcare-associated pathogens. Ultimately, this makes them valuable tools for genomic surveillance, outbreak investigation, and infection prevention. IMPORTANCE: Accurate identification and high-resolution typing of multidrug-resistant bacteria are essential for understanding their transmission dynamics in hospitals, particularly in light of the global spread of resistant strains and the role of environmental reservoirs. The newly developed cgMLST schemes presented here provide standardized, portable tools for both local and global scientific and clinical communities to conduct fine-scale genomic epidemiology across four Citrobacter species. These schemes support detailed outbreak reconstruction, source attribution, and cross-hospital comparisons, capabilities that are critical in an era of increasing antimicrobial resistance and international patient movement. By enabling consistent, species-specific surveillance and comparative analyses, cgMLST enhances infection control and public health responses, facilitating early detection and targeted intervention.

Klebsiella pneumoniae (K. pneumoniae) is an opportunistic Gram-negative pathogen known for the worldwide spread of clonal genetic lineages associated with various antibiotic resistances [1–3]. The ...

The importance of genomic surveillance strategies for pathogens has been particularly evident during the coronavirus disease 2019 (COVID-19) pandemic, as genomic data from the causative agent, severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), have guided public health decisions worldwide. Bayesian phylodynamic inference, integrating epidemiology and evolutionary biology, has become an essential tool in genomic epidemiological surveillance. It enables the estimation of epidemiological parameters, such as the reproductive number, from pathogen sequence data alone. Despite the phylodynamic approach being widely adopted, the abundance of phylodynamic models often makes it challenging to select the appropriate model for specific research questions. This article illustrates the application of phylodynamic birth-death-sampling models in public health using genomic data, with a focus on SARS-CoV-2. Targeting researchers less familiar with phylodynamics, it introduces a comprehensive workflow, including the conceptualisation of a research study and detailed steps for data preprocessing and postprocessing. In addition, we demonstrate the versatility of birth-death-sampling models through three case studies from Germany, utilising the BEAST2 software and its model implementations. Each case study addresses a distinct research question relevant not only to SARS-CoV-2 but also to other pathogens: Case study 1 finds traces of a superspreading event at the start of an early outbreak, exemplifying how simple models for genomic data can provide information that would otherwise only be accessible through extensive contact tracing. Case study 2 compares transmission dynamics in a nosocomial outbreak to community transmission, highlighting distinct dynamics through integrative analysis. Case study 3 investigates whether local transmission patterns align with national trends, demonstrating how phylodynamic models can disentangle complex population substructure with little additional information. For each case study, we emphasise critical points where model assumptions and data properties may misalign and outline appropriate validation assessments. Overall, we aim to provide researchers with examples on using birth-death-sampling models in genomic epidemiology, balancing theoretical and practical aspects.