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. 2021 Oct 18;21(1):1075.
doi: 10.1186/s12879-021-06771-8.

Implementation of an automated cluster alert system into the routine work of infection control and hospital epidemiology: experiences from a tertiary care university hospital

Seven Johannes Sam Aghdassi  1   2 , Britta Kohlmorgen  3   4 , Christin Schröder  3   4 , Luis Alberto Peña Diaz  3   4 , Norbert Thoma  3   4 , Anna Maria Rohde  3   4 , Brar Piening  3   4 , Petra Gastmeier  3   4 , Michael Behnke  3   4
Affiliations

Implementation of an automated cluster alert system into the routine work of infection control and hospital epidemiology: experiences from a tertiary care university hospital

Seven Johannes Sam Aghdassi et al. BMC Infect Dis. .

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 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.

Keywords: Automation; Cluster alert system; Digitalization; Hospital epidemiology; Infection control; Outbreak.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Clusters detected by the automated cluster alert system in the year 2019. Stratification by pathogen and ward type. CNS coagulase-negative staphylococci, ENTB Enterobacter spp., ENTF Enterococcus faecium and Enterococcus faecalis, ESCOL Escherichia coli, ICU intensive care unit, KLEB Klebsiella spp., NEOPED neonatology and pediatrics, OTH other pathogens, PSEA Pseudomonas aeruginosa, SAU Staphylococcus aureus
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