Typing and cluster outbreak analysis of E. faecium
Vancomycin-resistant Enterococcus faecium (VREfm) is a major cause of healthcare-associated infections, yet genomic typing and outbreak analysis remain challenging, particularly for dominant lineages such as ST117/CT71. Here, we assess the discriminatory performance of cgMLST and SKA for resolving relatedness within ST117/CT71 and evaluate population-wide core-genome allelic diversity of E. faecium. Although cgMLST captures the broad population structure of ST117/CT71 isolates, it lacks the resolution to accurately delineate genomic relatedness within this lineage. This limitation is mitigated by split k-mer analysis (SKA), which provides substantially higher resolution and separates isolates that are indistinguishable by cgMLST. Analysis of the E. faecium core-genome allelic landscape of E. faecium revealed that cgMLST distances among isolates sharing the same CT formed a narrowly constrained distribution (0–37 alleles; peak ˜15), indicating intrinsically limited core-genome diversity across clade A1 E. faecium. The previously dominant lineage ST117/CT71 showed no reduced allelic variability compared with other hospital-adapted lineages, indicating that its perceived clonality reflects species-wide core-genome constraints rather than lineage-specific properties To examine allelic variation within defined outbreak events, we analyzed 138 outbreak clusters comprising 577 isolates from 96 hospitals. Outbreak-associated isolates showed sharply compressed allelic differences (median = 1), with 90% of all pairwise comparisons falling within =9 alleles. In contrast to the widely used =20-allele threshold, our findings show that such high cutoffs substantially overestimate genomic relatedness and risk misclassifying unrelated isolates as clusters. Based on these data, we propose lowering the cgMLST alert threshold to =9 alleles for initial cluster detection, followed by confirmatory high-resolution analyses such as SKA. This two-step approach improves the accuracy of outbreak detection while preserving cgMLST as a robust framework for population-level genomic surveillance.