Towards data-driven refinement of an automated cluster alert system: assessing the concordance of epidemiological expert assessment and genotyping

Abstract Objective: An automated CLuster AleRt system (CLAR) is employed at our hospital to detect clusters of relevant microorganisms. Alerts are subsequently assessed by infection prevention and control (IPC) experts to determine their epidemiological relevance. This study compares expert assessment of CLAR alerts with genotyping results.

Design

Retrospective observational study Methods: We analyzed all CLAR alerts and genotyping reports generated during routine IPC activities between 08/2019 and 12/2024. CLAR alerts were classified relevant/not relevant based on epidemiological assessment by IPC physicians. Selected isolates were genotyped when transmission was suspected. Where multiple alerts and genotyping reports were available for an epidemiological cluster, only the first alert with genotyped isolates and the first corresponding genotyping report were considered, in order to emulate conditions at the onset of potential outbreaks. Genotyping was performed either by whole-genome sequencing (cgMLST) or repetitive-sequence-based polymerase chain reaction (rep-PCR). We calculated the proportion of CLAR alerts with ≥1 genotypically clustering isolate and performed a multivariable regression analysis to identify parameters associated with a higher likelihood of concordance.