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1000 Titel
  • Implementation of an automated cluster alert system into the routine work of infection control and hospital epidemiology: experiences from a tertiary care university hospital
1000 Autor/in
  1. Aghdassi, Seven Johannes Sam |
  2. Kohlmorgen, Britta |
  3. Schröder, Christin |
  4. Peña Diaz, Luis Alberto |
  5. Thoma, Norbert |
  6. Rohde, Anna Maria |
  7. Piening, Brar |
  8. Gastmeier, Petra |
  9. Behnke, Michael |
1000 Erscheinungsjahr 2021
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2021-10-18
1000 Erschienen in
1000 Quellenangabe
  • 21(1):1075
1000 Copyrightjahr
  • 2021
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.1186/s12879-021-06771-8 |
  • https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8522860/ |
1000 Publikationsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • Background!#!Early detection of clusters of pathogens is crucial for infection prevention and control (IPC) in hospitals. Conventional manual cluster detection is usually restricted to certain areas of the hospital and multidrug resistant organisms. Automation can increase the comprehensiveness of cluster surveillance without depleting human resources. We aimed to describe the application of an automated cluster alert system (CLAR) in the routine IPC work in a hospital. Additionally, we aimed to provide information on the clusters detected and their properties.!##!Methods!#!CLAR was continuously utilized during the year 2019 at Charité university hospital. CLAR analyzed microbiological and patient-related data to calculate a pathogen-baseline for every ward. Daily, this baseline was compared to data of the previous 14 days. If the baseline was exceeded, a cluster alert was generated and sent to the IPC team. From July 2019 onwards, alerts were systematically categorized as relevant or non-relevant at the discretion of the IPC physician in charge.!##!Results!#!In one year, CLAR detected 1,714 clusters. The median number of isolates per cluster was two. The most common cluster pathogens were Enterococcus faecium (n = 326, 19 %), Escherichia coli (n = 274, 16 %) and Enterococcus faecalis (n = 250, 15 %). The majority of clusters (n = 1,360, 79 %) comprised of susceptible organisms. For 906 alerts relevance assessment was performed, with 317 (35 %) alerts being classified as relevant.!##!Conclusions!#!CLAR demonstrated the capability of detecting small clusters and clusters of susceptible organisms. Future improvements must aim to reduce the number of non-relevant alerts without impeding detection of relevant clusters. Digital solutions to IPC represent a considerable potential for improved patient care. Systems such as CLAR could be adapted to other hospitals and healthcare settings, and thereby serve as a means to fulfill these potentials.
1000 Sacherschließung
lokal Cross Infection/prevention
lokal Automation
lokal Humans [MeSH]
lokal Outbreak
lokal Hospital epidemiology
lokal Cluster alert system
lokal Infection Control [MeSH]
lokal Tertiary Healthcare [MeSH]
lokal Enterococcus faecium [MeSH]
lokal Digitalization
lokal Hospitals, University [MeSH]
lokal Cross Infection/epidemiology [MeSH]
lokal Infection control
lokal Research Article
1000 Liste der Beteiligten
  1. https://orcid.org/0000-0002-8263-3455|https://frl.publisso.de/adhoc/uri/S29obG1vcmdlbiwgQnJpdHRh|https://frl.publisso.de/adhoc/uri/U2NocsO2ZGVyLCBDaHJpc3Rpbg==|https://frl.publisso.de/adhoc/uri/UGXDsWEgRGlheiwgTHVpcyBBbGJlcnRv|https://frl.publisso.de/adhoc/uri/VGhvbWEsIE5vcmJlcnQ=|https://frl.publisso.de/adhoc/uri/Um9oZGUsIEFubmEgTWFyaWE=|https://frl.publisso.de/adhoc/uri/UGllbmluZywgQnJhcg==|https://frl.publisso.de/adhoc/uri/R2FzdG1laWVyLCBQZXRyYQ==|https://frl.publisso.de/adhoc/uri/QmVobmtlLCBNaWNoYWVs
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