Risk factors for transmission of carbapenem-resistant Acinetobacter baumannii in outbreak situations: results of a case-control study.

BACKGROUND: An increase in patients with multidrug-resistant organisms and associated outbreaks during the COVID-19 pandemic have been reported in various settings, including low-endemic settings. Here, we report three distinct carbapenem-resistant Acinetobacter baumannii (CRAB) outbreaks in five intensive care units of a university hospital in Berlin, Germany during the COVID-19 pandemic. METHODS: A case-control study was conducted with the objective of identifying risk factors for CRAB acquisition in outbreak situations. Data utilized for the case-control study came from the investigation of three separate CRAB outbreaks during the COVID-19 pandemic (August 2020- March 2021). Cases were defined as outbreak patients with hospital-acquired CRAB. Controls did not have any CRAB positive microbiological findings and were hospitalized at the same ward and for a similar duration as the respective case. Control patients were matched retrospectively in a 2:1 ratio. Parameters routinely collected in the context of outbreak management and data obtained retrospectively specifically for the case-control study were included in the analysis. To analyze risk factors for CRAB acquisition, univariable and multivariable analyses to calculate odds ratios (OR) and 95% confidence intervals (CI) were performed using a conditional logistic regression model. RESULTS: The outbreaks contained 26 cases with hospital-acquired CRAB in five different intensive care units. Two exposures were identified to be independent risk factors for nosocomial CRAB acquisition by the multivariable regression analysis: Sharing a patient room with a CRAB patient before availability of the microbiological result was associated with a more than tenfold increase in the risk of nosocomial CRAB acquisition (OR: 10.7, CI: 2.3-50.9), while undergoing bronchoscopy increased the risk more than six times (OR: 6.9, CI: 1.3-38.1). CONCLUSIONS: The risk factors identified, sharing a patient room with a CRAB patient and undergoing bronchoscopy, could point to an underperformance of basic infection control measure, particularly hand hygiene compliance and handling of medical devices. Both findings reinforce the need for continued promotion of infection control measures. Given that the outbreaks occurred in the first year of the COVID-19 pandemic, our study serves as a reminder that a heightened focus on airborne precautions should not lead to a neglect of other transmission-based precautions.

Towards interoperability in infection control: a standard data model for microbiology.

The COVID-19 pandemic has made it clear: sharing and exchanging data among research institutions is crucial in order to efficiently respond to global health threats. This can be facilitated by defining health data models based on interoperability standards. In Germany, a national effort is in progress to create common data models using international healthcare IT standards. In this context, collaborative work on a data set module for microbiology is of particular importance as the WHO has declared antimicrobial resistance one of the top global public health threats that humanity is facing. In this article, we describe how we developed a common model for microbiology data in an interdisciplinary collaborative effort and how we make use of the standard HL7 FHIR and terminologies such as SNOMED CT or LOINC to ensure syntactic and semantic interoperability. The use of international healthcare standards qualifies our data model to be adopted beyond the environment where it was first developed and used at an international level.

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

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.

Lean back and wait for the alarm? Testing an automated alarm system for nosocomial outbreaks to provide support for infection control professionals.

INTRODUCTION: Outbreaks 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. METHODS: Our 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. RESULTS: During 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. CONCLUSION: AODS 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.