Towards data-driven refinement of an automated cluster alert system: assessing the concordance of epidemiological expert assessment and genotyping
In brief
Genotyping confirmed clustering in 75% of expert-flagged hospital alerts
In this single-hospital retrospective study, genotyping found at least one clustering isolate in 87 of 116 alerts that infection-control experts had judged epidemiologically relevant. Concordance was higher in neonatology and for carbapenem-resistant Enterobacterales, but isolates were genotyped when transmission was suspected, so the results do not show how well alerts identify clusters across all patients.
- Journal
- Infection control and hospital epidemiology (Q1)
- Published
- 10 October 2026
- Study design
- Cohort / observational study
- Evidence level
- Level 3, Low (CEBM 3b)
- Authors
- Darius Ferenc Ruether, Marco Paul, Sandra Jendrossek, Nicholas Jakob Böhm, Brar Piening, Friederike Maechler, et al.
- PMID
- 42858456
- DOI
- 10.1017/ice.2026.10569
Why clinicians should know about it
- Picked for Health Informatics (top studies of the week, 11 October 2026): Concordance of CLAR alerts with genotyping
- Picked for Microbiology (medical) (top studies of the week, 11 October 2026): Cluster alert system concordance with 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. RESULTS: We identified 116 relevant CLAR alerts corresponding to first genotyping reports (n = 77 cgMLST, n = 39 rep-PCR) of epidemiological clusters. Overall, these alerts contained 253 genotyped isolates. Genotypic clustering of ≥1 CLAR alert isolate was confirmed for 87 CLAR alerts (75.0%). A higher proportion of concordance with genotyping was seen for relevant alerts in neonatology (93.8%) and with carbapenem-resistant Enterobacterales (94.4%), also confirmed by multivariable analysis. CONCLUSIONS: Expert-assessed epidemiological relevance of CLAR alerts showed promising concordance with genomic findings and may inform future machine learning-based predictions.
Abstract as published, via PubMed.
For healthcare professionals. The summary is generated by AI from the published abstract, and the evidence level is assigned automatically from the study design on the Oxford CEBM hierarchy. Neither is medical advice. Read the full paper before changing practice.