Multiresistant Bacterial Pathogens, Virulent Bacterial Pathogens
Conditions
Keywords
Epidemiology, surveillance, bacterial pathogens, Database, Genotyping
Brief summary
Hypervirulent and multidrug-resistant infections are associated with significant health care costs, substantial morbidity and mortality. Therefore, the rapid recognition of outbreaks and transmissions with hypervirulent and multi-drug resistant pathogen is a key priority for infection control and public health.The main goal is to implement a shared database, connecting human and veterinary microbiology laboratories, which would allow near real-time molecular epidemiology with high spatiotemporal resolution of bacterial pathogens such as transmission and outbreak surveillance between different compartments including humans, animals and the environment in Switzerland. Investigator aims to analyze already collected encoded retrospective datasets of various pathogens by combining epidemiological data and whole genome sequences from pathogens.
Detailed description
Hypervirulent and multidrug-resistant infections are associated with significant health care costs, substantial morbidity and mortality. Therefore, the rapid recognition of outbreaks and transmissions with hypervirulent and multi-drug resistant pathogen is a key priority for infection control and public health. For hospital epidemiologist, infectious disease and public health experts, and microbiologists the identification of an outbreak source is a first important step to establish effective counter-measurements. In Switzerland, the burden of pathogen transmission between humans, animals and the environment is substantial. The main goal is to implement a shared database, connecting human and veterinary microbiology laboratories, which would allow near real-time molecular epidemiology with high spatiotemporal resolution of bacterial pathogens such as transmission and outbreak surveillance between different compartments including humans, animals and the environment in Switzerland. Investigator aims to analyze already collected encoded retrospective datasets of various pathogens by combining epidemiological data and whole genome sequences from pathogens.
Interventions
DIAGNOSTIC_TESTAnalysis of Bacterial Genome
genome assembly; prediction of sequence type (MLST); core genome MLST tree to rapidly compare strains within a project; core genome single nucleotide polymorphism (SNP) tree to compare all Swiss Pathogen Surveillance Platform (SPSP) strains belonging to a same species; whole genome SNP tree to compare all SPSP strains within the same species and ST;; prediction of resistance and virulence factors within pathogen submitted genomes; time trees and calculation of transmission rates, including basic reproduction number; analysis of classical epidemiological data with advanced statistical methods including machine learning.
Sponsors
Swiss National Science Foundation
University Hospital, Basel, Switzerland
Study design
Observational model
COHORT
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Healthy volunteers
No
Inclusion criteria
* All patients with either colonisations or infections with either a bacterial or a viral pathogen, where whole genome sequencing data and available minimal epidemiological, demographic and clinical data * Pathogens included into analysis are: Multidrug-resistant bacteria include: methicillin resistant Staphylococcus aureus (MRSA), Carbapenemase- and/or extended spectrum betalactamase (ESBL)-producing Enterobacteriaceae and non-fermenting bacteria including Pseudomonas aeruginosa and Acinetobacter baumannii, Vancomycin resistant Enterococcus faecium, and others; virulent bacteria include: Neisseria meningitidis, Neisseria gonorrhoeae, Mycobacterium tuberculosis, Campylobacter spp., Salmonella spp., Legionella pneumophila, Listeria monocytogenes, and Streptococcus pneumoniae, and others; Viruses include: Influenza viruses, Measles virus, Enterovirus E68, Respiratory Syncytial Virus and others.
Exclusion criteria
* Decline to sign a general consent or any other declining statement against using data for research purposes.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Identification of transmission clusters based on genetic similarity. | Onetime identification at baseline | Identification of transmission clusters based on genetic similarity. With focus on whole genome sequencing. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Detection of Genotypic Resistance | Onetime identification at baseline | Detection of Genotypic Resistance (in-vitro antibiotic susceptibility test (AST) based on measuring minimum inhibitory concentration (MIC) or zone diameter (ZD)) |
Countries
Switzerland
Contacts
Primary ContactHans Hirsch, Prof. Dr.
Backup ContactAitana Lebrand, Dr.
Outcome results
None listed