dc.contributor.author
Aghdassi, Seven Johannes Sam
dc.contributor.author
Kohlmorgen, Britta
dc.contributor.author
Schröder, Christin
dc.contributor.author
Peña Diaz, Luis Alberto
dc.contributor.author
Thoma, Norbert
dc.contributor.author
Rohde, Anna Maria
dc.contributor.author
Piening, Brar
dc.contributor.author
Gastmeier, Petra
dc.contributor.author
Behnke, Michael
dc.date.accessioned
2023-03-16T13:05:11Z
dc.date.available
2023-03-16T13:05:11Z
dc.identifier.uri
https://refubium.fu-berlin.de/handle/fub188/38421
dc.identifier.uri
http://dx.doi.org/10.17169/refubium-38139
dc.description.abstract
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 Charite 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.
en
dc.rights.uri
https://creativecommons.org/licenses/by/4.0/
dc.subject
Digitalization
en
dc.subject
Cluster alert system
en
dc.subject
Infection control
en
dc.subject
Hospital epidemiology
en
dc.subject.ddc
600 Technik, Medizin, angewandte Wissenschaften::610 Medizin und Gesundheit::610 Medizin und Gesundheit
dc.title
Implementation of an automated cluster alert system into the routine work of infection control and hospital epidemiology: experiences from a tertiary care university hospital
dc.type
Wissenschaftlicher Artikel
dcterms.bibliographicCitation.articlenumber
1075
dcterms.bibliographicCitation.doi
10.1186/s12879-021-06771-8
dcterms.bibliographicCitation.journaltitle
BMC Infectious Diseases
dcterms.bibliographicCitation.originalpublishername
Springer Nature
dcterms.bibliographicCitation.volume
21
refubium.affiliation
Charité - Universitätsmedizin Berlin
refubium.funding
Springer Nature DEAL
refubium.resourceType.isindependentpub
no
dcterms.accessRights.openaire
open access
dcterms.bibliographicCitation.pmid
34663246
dcterms.isPartOf.eissn
1471-2334