Lieke Van Alphen (MUMC+)

Summary of presentation:
Introduction
The identification of clonal clusters of pathogens is essential to support the epidemiological investigation of nosocomial outbreaks, which is typically conducted independently by hospitals. However, patient transfer within a region putatively spreads antimicrobial resistant (AMR) isolates between institutions. We set up a regional network for real time data sharing of whole genome sequence (WGS) data in the Netherlands to gain a better understanding of the AMR pathogen transmission between hospitals.

Methods
The 4 participating hospitals in the region send in isolates to the central typing facility for WGS using Illumina technology. From 2023, all Vancomycin-resistant Enterococcus faecium (VRE) isolates were prospectively typed. WGS data with limited metadata was uploaded in the cloud-based platform of 1928 Diagnostics, where each participating institution could access their data and compare with the regional data. In this platform, identification of AMR genes and analysis of intra- and inter-hospital clusters using cgMLST were performed.

Results
From 2021 onwards, 1483 isolates were submitted to the regional typing lab, with 252 VRE isolates since 2023. Prospective typing of VRE isolates demonstrated small genomic clusters in 4 hospitals in the region, influx of a new type (ST1299) within the region and spread of several distinct ST80 clusters within two hospitals. The data sharing platform proved user-friendly for regional partners, enabling rapid access to AMR gene and cgMLST data for analysis of local isolates in a regional context. It also facilitated WGS implementation in non-academic regional hospitals.

Conclusion

Regional sharing of WGS data was feasible, with rapid data availability for each hospital. Phylogenetic analysis provided an overview of the population diversity of nosocomial isolates at the regional level, demonstrating VRE clusters within but not between hospitals. Implementation of this project resulted in regional WGS capacity building and enabled hospitals to detect potential (inter-)hospital outbreaks using a user-friendly data-sharing platform.