Antibiotic resistance information exchanges : interim guidance

Antibiotic resistance (AR) is a major clinical and public health threat with potential to unravel more than half a century of human health advances offered by modern medical care. Unfortunately, modern healthcare delivery is notably contributory to the spread of antibiotic-resistant organisms, as patients who have become colonized with resistant organisms often receive care across multiple healthcare settings (e.g., ambulatory care, acute care hospitals (ACHs), and various long-term care (LTC) settings, including long-term acute care hospitals (LTACHs) and skilled nursing facilities (SNFs)).Although the threat of antibiotic-resistant organism transmission from a colonized patient to physically proximate patients remains for the duration of colonization, the lack of information sharing between healthcare facilities often results in the colonized status of a patient being unknown to a receiving or admitting facility. When this occurs, the appropriate infection control precautions are less likely to be used from the start of patient care, which increases the likelihood that resistant organisms will spread to other patients.The need for improved AR situational awareness is a major challenge to the U.S. Centers for Disease Control and Prevention\u2019s (CDC\u2019s) strategy to contain the most threatening forms of resistance and the genes responsible for such phenotypes. To fulfill their central role in implementing the CDC\u2019s containment strategy, some state health departments have developed systems (Multidrug-Resistant Organism (MDRO) Registries or MDRO Alert Systems, referred to herein as AR Information Exchanges (ARIEs)) that track patients previously colonized or infected with specific MDROs and then alert healthcare providers when these patients are admitted to a facility. The term AR Information Exchange emphasizes the importance of multidirectional information flow amongst healthcare facilities and public health authorities, as opposed to unidirectional data collection and storage.This interim guidance is intended for operational use by individuals and organizations responsible for developing or enhancing an ARIE; however, it does not constitute legal advice. Public health agencies should follow applicable laws, statues, and/or regulations when developing ARIEs with questions about directed to the entity\u2019s legal counsel.CS 324851-AARIE-Interim-Guidance-508.pdf20211158

Determining antimicrobial susceptibility and antimicrobial resistance mechanisms in the clinical laboratory: where are we and where are we heading?

Since the discovery of penicillin, almost a century ago, the battle against resistant microbes has been fierce but uneven. Microbes have been proven quite adaptable and have developed many different antimicrobial resistance (AMR) mechanisms to evade antibiotics. These AMR mechanisms mainly regulate cephalosporin and carbapenem resistance for Gram-negative bacteria, methicillin resistance for Staphylococcus aureus and vancomycin resistance for Enterococci. Greece is endemic for most known multi-drug resistant organisms (MDRO) that often cause community and hospital-acquired infections, perplexing treatment, increasing length of stay in the hospital and relevant costs, and increasing morbidity and mortality. Detection and treatment of such infections in a timely and effective way is imperative. For the past few decades, scientists from different scientific fields have been developing technologies and methods to assist the early and reliable detection of AMR to optimize not only treatment but also infection control practices in an effort to restrain it. This review focuses on current practices to detect AMR and the corresponding resistance mechanisms. From the well-established classic diffusion antibiogram to the rapid automated tools that provide susceptibility profiles of bacteria within a few hours, and from the time-consuming phenotypic AMR detection methods to rapid molecular AMR mechanism detection directly from the sample, Microbiology has come a long way. Most microbiological laboratories are currently using a combination of phenotypic and molecular methods for AMR detection, in an effort to make the best out of both. Integrating novel technologies into the laboratory routine workflow has its challenges, with the financial burden being one of the most significant. However, if the progress in the field of Microbiology since the emergence of the first resistant microbe until now is any indication, the future holds many more adventures and scientific breakthroughs in the fight against AMR

contaminated sinks and contamination of ultra-filtrate bags as possible route of transmission?

Background We report on an outbreak in a surgical, interdisciplinary intensive care unit (ICU) of a tertiary care hospital. We detected a cluster of ICU patients colonized or infected with multidrug-resistant Pseudomonas aeruginosa. We established an outbreak investigation team, performed an exploratory epidemiological analysis and initiated an epidemiology-based intervention. Methods As part of the outbreak investigation, we performed microbiological examinations of the sinks in the patient rooms and a retrospective case-control study. All patients admitted to the outbreak ICU between January 2012 and February 2014 were included. Cases were patients colonized with the outbreak strain. Controls were patients with a different Pseudomonas aeruginosa strain. Risk factors were evaluated using multivariable conditional logistic regression analysis. Strain typing was performed using the repetitive element-based polymerase chain reaction (rep-PCR) DiversiLab system. Results The outbreak strain was found in the sinks of five (of 16) patient rooms. Altogether 21 cases and 21 (randomly selected) controls were included. In the univariate analysis, there was no significant difference in baseline data of the patients. In the multivariate analysis, stay in a room with a colonized sink (Odds Ratio[OR] 11.2, p = 0.007) and hemofiltration (OR 21.9, p = 0.020) were independently associated with an elevated risk for colonization or infection by the outbreak strain. In a subsequent evaluation of the work procedures associated with hemofiltration, we found that the ultra-filtrate bags had been on average five times per day emptied in the sinks of the patient rooms and were used multiple for the same patient. We exchanged the traps of the contaminated sinks and eliminated work procedures involving sinks in patient rooms by implementation of single use bags, which are emptied outside patient rooms to reduce splash water at the sinks. In the 20 month follow-up period, the outbreak strain was detected only once, which indicated that the outbreak had been ceased (incidence 0.75% vs. 0.04%, p < 0.001) Furthermore, the incidence of Pseudonomas aeruginosa overall was significantly decreased (2.5% vs. 1.5%, p < 0.001). Conclusion In ICUs, limiting work processes involving sinks results in reduced multidrug-resistant Pseudomonas aeruginosa rates. ICUs with high rates of Pseudomonas aeruginosa should consider eliminating work processes that involve sinks and potentially splash water in close proximity to patients. Trial registration All data were surveillance based data which were obtained within the German Law on Protection against Infection (“Infektionsschutzgesetz”). Therefore a trial registration was not required

A prospective multicentre screening study on multidrug-resistant organisms in intensive care units in the Dutch-German cross-border region, 2017 to 2018:the importance of healthcare structures

BACKGROUND: Antimicrobial resistance poses a risk for healthcare, both in the community and hospitals. The spread of multidrug-resistant organisms (MDROs) occurs mostly on a local and regional level, following movement of patients, but also occurs across national borders. AIM: The aim of this observational study was to determine the prevalence of MDROs in a European cross-border region to understand differences and improve infection prevention based on real-time routine data and workflows. METHODS: Between September 2017 and June 2018, 23 hospitals in the Dutch (NL)–German (DE) cross-border region (BR) participated in the study. During 8 consecutive weeks, patients were screened upon admission to intensive care units (ICUs) for nasal carriage of meticillin-resistant Staphylococcus aureus (MRSA) and rectal carriage of vancomycin-resistant Enterococcus faecium/E. faecalis (VRE), third-generation cephalosporin-resistant Enterobacteriaceae (3GCRE) and carbapenem-resistant Enterobacteriaceae (CRE). All samples were processed in the associated laboratories. RESULTS: A total of 3,365 patients were screened (median age: 68 years (IQR: 57–77); male/female ratio: 59.7/40.3; NL-BR: n = 1,202; DE-BR: n = 2,163). Median screening compliance was 60.4% (NL-BR: 56.9%; DE-BR: 62.9%). MDRO prevalence was higher in DE-BR than in NL-BR, namely 1.7% vs 0.6% for MRSA (p = 0.006), 2.7% vs 0.1% for VRE (p < 0.001) and 6.6% vs 3.6% for 3GCRE (p < 0.001), whereas CRE prevalence was comparable (0.2% in DE-BR vs 0.0% in NL-BR ICUs). CONCLUSIONS: This first prospective multicentre screening study in a European cross-border region shows high heterogenicity in MDRO carriage prevalence in NL-BR and DE-BR ICUs. This indicates that the prevalence is probably influenced by the different healthcare structures

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 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

Bacterial contamination of complex gastrointestinal endoscopes

The worldwide surge of duodenoscope-associated outbreaks since the new millennium show that current reprocessing practices do not guarantee adequately decontaminated endoscopes. To prevent future outbreaks, identification of risk factors contributing to outbreaks and endoscope contamination is essential. The discussion of this thesis is divided into three themes. First, it describes the background of reprocessing of duodenoscopes and linear echoendoscopes (DLE), the multiple parties that are involved, and potential pathways leading to an outbreak. Secondly, it gains insight in the true size of the underlying problem of DLE contamination by showing the enduring high prevalence of DLE contamination with digestive tract bacteria in Dutch hospitals. In the third part of this thesis we show the results of a study of a cleaning test as a potential marker to check for residue to lower the contamination rate

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