{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T20:53:00Z","timestamp":1791665580995,"version":"4.3.4"},"reference-count":27,"publisher":"Cambridge University Press (CUP)","license":[{"start":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T00:00:00Z","timestamp":1791590400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["cambridge.org"],"crossmark-restriction":true},"short-container-title":["Infect. Control Hosp. Epidemiol."],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec id=\"S0899823X26105698_as1\">\n                    <jats:title>Objective:<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"S0899823X26105698_as2\">\n                    <jats:title>Design:<\/jats:title>\n                    <jats:p>Retrospective observational study<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"S0899823X26105698_as3\">\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>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 \u22651 genotypically clustering isolate and performed a multivariable regression analysis to identify parameters associated with a higher likelihood of concordance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"S0899823X26105698_as4\">\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>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 \u22651 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"S0899823X26105698_as5\">\n                    <jats:title>Conclusions:<\/jats:title>\n                    <jats:p>Expert-assessed epidemiological relevance of CLAR alerts showed promising concordance with genomic findings and may inform future machine learning-based predictions.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1017\/ice.2026.10569","type":"journal-article","created":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T20:46:47Z","timestamp":1791665207000},"page":"1-9","update-policy":"https:\/\/doi.org\/10.1017\/policypage","source":"Crossref","is-referenced-by-count":0,"title":["Towards data-driven refinement of an automated cluster alert system: assessing the concordance of epidemiological expert assessment and genotyping"],"prefix":"10.1017","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3783-380X","authenticated-orcid":false,"given":"Darius Ferenc","family":"Ruether","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/001w7jn25","id-type":"ROR","asserted-by":"publisher"}],"name":"Charit\u00e9 \u2013 University Medical Center Berlin, corporate member of Freie Universit\u00e4t Berlin and Humboldt-Universit\u00e4t zu Berlin"},{"id":[{"id":"https:\/\/ror.org\/0493xsw21","id-type":"ROR","asserted-by":"publisher"}],"name":"Berlin Institute of Health at Charit\u00e9 \u2013 University Medical Center Berlin, BIH Biomedical Innovation Academy, BIH Charit\u00e9 Digital Clinician Scientist Program"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marco","family":"Paul","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/001w7jn25","id-type":"ROR","asserted-by":"publisher"}],"name":"Charit\u00e9 \u2013 University Medical Center 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Published by Cambridge University Press on behalf of The Society for Healthcare Epidemiology of America","name":"copyright","label":"Copyright","group":{"name":"copyright_and_licensing","label":"Copyright and Licensing"}},{"value":"This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https:\/\/creativecommons.org\/licenses\/by\/4.0\/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.","name":"license","label":"License","group":{"name":"copyright_and_licensing","label":"Copyright and Licensing"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}