Towards data-driven refinement of an automated cluster alert system: assessing the concordance of epidemiological expert assessment and genotyping
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Published on: June 21, 2018
Darius Ferenc Ruether1,2, Marco Paul1,3, Sandra Jendrossek1
1Institute of Hygiene and Environmental Medicine, Charité - University Medical Center Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
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.
