ArticlePloS one2020
Lean back and wait for the alarm? Testing an automated alarm system for nosocomial outbreaks to provide support for infection control professionals.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Hospital-wide pathogen transmission surveillance combining epidemiological, genetic, and spectroscopic approaches.Microbiology spectrum · 2026Article
- Antimicrobial activity and mechanistic insights into the tick peptide persulcatusin against vancomycin-susceptible and vancomycin-resistantFrontiers in microbiology · 2026Article
- An openEHR based infection control system to support monitoring of nosocomial bacterial clusters and contacts.NPJ digital medicine · 2025Article
- Comparing automated surveillance systems for detection of pathogen-related clusters in healthcare settings.Antimicrobial resistance and infection control · 2024Article
- Risk factors for transmission of carbapenem-resistant Acinetobacter baumannii in outbreak situations: results of a case-control study.BMC infectious diseases · 2024Article
- Innovative Techniques for Infection Control and Surveillance in Hospital Settings and Long-Term Care Facilities: A Scoping Review.Antibiotics (Basel, Switzerland) · 2024Article
- Three-Dimensional Disease Outbreak Surveillance System in a Tertiary Hospital in Singapore: A Proof of Concept.Mayo Clinic proceedings. Digital health · 2023Article
- Article
- Corticosteroids as risk factor for COVID-19-associated pulmonary aspergillosis in intensive care patients.Critical care (London, England) · 2022Article
- Article
- Implementation of an automated cluster alert system into the routine work of infection control and hospital epidemiology: experiences from a tertiary care university hospital.BMC infectious diseases · 2021Article
- Transformation of microbiology data into a standardised data representation using OpenEHR.Scientific reports · 2021Article
- An aberration detection-based approach for sentinel syndromic surveillance of COVID-19 and other novel influenza-like illnesses.Journal of biomedical informatics · 2021Article
- Prevalence and risk factors of colonisation with vancomycin-resistant Enterococci faecium upon admission to Germany's largest university hospital.GMS hygiene and infection control · 2021Article
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionOutbreaks of communicable diseases in hospitals need to be quickly detected in order to enable immediate control. The increasing digitalization of hospital data processing offers potential solutions for automated outbreak detection systems (AODS). Our goal was to assess a newly developed AODS.
methodsOur AODS was based on the diagnostic results of routine clinical microbiological examinations. The system prospectively counted detections per bacterial pathogen over time for the years 2016 and 2017. The baseline data covers data from 2013-2015. The comparative analysis was based on six different mathematical algorithms (normal/Poisson and score prediction intervals, the early aberration reporting system, negative binomial CUSUMs, and the Farrington algorithm). The clusters automatically detected were then compared with the results of our manual outbreak detection system.
resultsDuring the analysis period, 14 different hospital outbreaks were detected as a result of conventional manual outbreak detection. Based on the pathogens' overall incidence, outbreaks were divided into two categories: outbreaks with rarely detected pathogens (sporadic) and outbreaks with often detected pathogens (endemic). For outbreaks with sporadic pathogens, the detection rate of our AODS ranged from 83% to 100%. Every algorithm detected 6 of 7 outbreaks with a sporadic pathogen. The AODS identified outbreaks with an endemic pathogen were at a detection rate of 33% to 100%. For endemic pathogens, the results varied based on the epidemiological characteristics of each outbreak and pathogen.
conclusionAODS for hospitals based on routine microbiological data is feasible and can provide relevant benefits for infection control teams. It offers in-time automated notification of suspected pathogen clusters especially for sporadically occurring pathogens. However, outbreaks of endemically detected pathogens need further individual pathogen-specific and setting-specific adjustments.
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