bluuebunny/crossref_metadata_2025_split huggingface dataset:
Use of a Biological Tracer to Investigate Microbial Aerosols Generated by Heater-Cooler Units
Authors: Moore Ginny , Parks Simon , Bennett Allan 
Published: November 2020

Background: A multinational outbreak of Mycobacterium chimaera endocarditis following cardiac surgery has been attributed to the use of heater-cooler units (HCUs) during cardiopulmonary bypass. It is hypothesized that mycobacteria can be transmitted to the surgical site via the aerosolization of contaminated water from within the unit. In the United Kingdom, M. chimaera infections have been linked to 1 specific make and model of HCU, which was shown to generate microbial aerosols when circulating water. The manufacturer has since modified this HCU and claims that the dispersal of aerosols has now been prevented. M. chimaera is a common contaminant of HCUs, regardless of make, model, and manufacturer. To help inform local decision making, hospitals require evidence that this modified HCU and/or alternative heater-cooler systems can reduce the risk of mycobacterial infection by incorporating design features that prevent the generation of microbial aerosols external to the device. The time required to culture M. chimaera means investigations focusing on naturally or artificially contaminated HCUs are problematic. Instead, specialist aerobiological techniques incorporating a nonpathogenic, aerostable, biological tracer (Bacillus atrophaeus) were used to investigate microbial aerosols generated and released from brand-new and ‘upgraded’ HCUs. Methods: 4 HCUs (A–D), supplied directly by the manufacturers, were filled with filtered tap water, and high numbers of B. atrophaeus (109 CFU/L) were added to the tanks. High-volume cyclone samplers were used to sample the air when each HCU was switched off and during different operational phases. Samplers were operated for 5 minutes and the collecting fluid cultured for B. atrophaeus. The number of colonies was converted to CFU per cubic meter of air. Results: Under controlled experimental conditions, HCU-A released a small but significant level of aerosol during operational phases (eg, cooling) that resulted in increased pressure within the tank. The filler flap was identified as the principal area of aerosol release. The circulation of water within HCU-B and HCU-C was shown to generate an aerosol but, when connected to an ‘aerosol collection set,’ this aerosol was not released. However, it is essential that effective and sufficient vacuum is maintained. There was no aerosol release from HCU-D. Conclusions: A specialist in aerobiology using a biological tracer can determine the level of aerosol released from an HCU and its location. However, transmission of M. chimaera could occur via aerosolization of contaminated water, but it is not the only possible route of infection. The efficacy of recommended decontamination procedures must also be assured.Funding: NoneDisclosures: None
Using a Critical Safety Behavior Scoring Tool for N95 Respirator Use to Evaluate Training Interventions
Authors: Beam Elizabeth , Kupzyk Kevin , Herstein Jocelyn , Gibbs Shawn 
Published: November 2020

Background: Hospitals struggle nationally to educate healthcare workers on the safe use of N95 respirators as part of their respiratory protection programs. Practical and effective interventions are needed to improve this clinical behavior, which is critical to healthcare worker safety in airborne precautions, hazardous drug administration, and pandemic response. Objective: In this analysis, we specifically investigated 2 just-in-time training interventions that would be practical to implement in a hospital setting. Methods: A simulation approach was used to evaluate 2 interventions for N95 respirator use at a Midwestern Academy Hospital system (n = 62 respirators: 32 control and 30 treatment). Healthcare workers were asked to don and doff an N95 respirator while being video-recorded in an empty hospital corridor and room. After a randomized intervention was applied, they repeated the respirator donning and doffing while being video-recorded. One intervention used an instructional video alone, and the other used the same instructional video but added a video reflection intervention. The video reflection intervention asked the participant to review and score their first performance of N95 donning and doffing using the Critical Safety Behavior Scoring Tool (CSBST). The research team used the same CSBST to score all performances of donning and doffing for comparison and evaluation. Result: The critical safety behaviors at the pretest and posttest for the 2 intervention groups reveal the impact of the 2 types of just-in-time training on demonstrated N95 respirator skills. The video alone and video reflection scores were not significantly different at pretest between the 2 groups. Scores were significantly higher on the posttest for the reflective practice intervention. Findings related to demographic information included years in healthcare, frequency of use, history of needlestick, and fatigue. Conclusions: Video reflection may improve compliance with critical safety behaviors for just-in-time training on N95 respirator use. Further research should examine the video-recorded findings for measurement elements that can be expanded in a scoring tool such as facial hair, hairstyle, and the quality of hand hygiene. Intervention studies should also examine how often the training must be repeated to maintain competency. This intervention may have implications for the training of other critical safety behaviors in infection control and other high-risk procedures.Funding: NoneDisclosures: None
Using Data Collected from a Commercial Sensor System to Inform Mathematical Models of Healthcare-Associated Infections
Authors: Liang Jiazhao , Jang Hankyu , Hasan D M Hasibul , Polgreen Philip , Pemmaraju Sriram , Segre Alberto 
Published: November 2020

Background: Hospital-acquired infections are commonly spread through the movement of healthcare professionals (HCPs). Computational simulations provide a powerful tool for understanding how HCP behavior contributes to these infections, but how well they reflect the real world rests on a number of critical parameters. Our goal is to provide accurate, fine-grained estimates of real HCP movement and interaction parameters suitable for simulating the potential spread of pathogens over different types of inpatient facilities. Methods: We obtained a commercial data set with 44 million deidentified elements compiled from >27,000 HCPs from >30 job types. The data were collected over 27 months from >20 facilities of varying size using a proprietary electronic sensor system. Each observation recorded an HCP visiting 1 of 12,000 rooms (38% being patient rooms) and consisted of the entry and exit time stamps, hand hygiene behavior, and for many rooms, their (x, y) geometric coordinates within the facility. From these data, we can reconstruct the behavior (including location and hand-hygiene adherence) of each instrumented HCP across multiple shifts. Results: Distributions describing various aspects of HCP behavior (eg, arrival rates and dwell times) were derived using HCP job function, department or unit assignment, type of shift (day vs night), time of day, facility size, and staffing of facility. In a similar fashion, we constructed HCP cross-table transition probabilities using job type, room type, department type, unit type, and facility type. These distributions were used to generate reasonable HCP movement and behavior patterns in a simulation environment. Distributions of dwell time were, for the most part, heavy tailed, but they varied by type of job and facility: dwell times over all facilities, job types, and room types averaged ∼339 seconds (SD, 495 seconds), with a mean of maximums by job type of ∼37,168 seconds. However, these distributions differ within job type but across facilities (ie, nurses in 1 facility averaged 397 seconds, but 277 seconds in another) and within facility but across job type. For example, physicians averaged 292 seconds, whereas nurses averaged 397 seconds and physical therapists averaged 861 seconds. Conclusions: Our results provide a unique resource for disease modelers who wish to build meaningful simulations of the transmission of hospital-acquired infections. The scale and diversity of the data gave us the unique capability to provide, with confidence, distinct parameter sets for different types and sizes of healthcare facilities across a wide range of situations.Funding: NoneDisclosures: None
Validation Methodology of Healthcare-Associated Infection Device Day Denominators When Switching Electronic Medical Records
Authors: Luong Lan , Simkins Michelle , Snyders Rachael , Gase Kathleen Anne , Leone Carole , Sykora Carol , Hoehner Christine , Babcock Hilary 
Published: November 2020

Background: From August 2017 to June 2018, 11 hospitals within a large healthcare system switched from multiple different electronic medical records (EMRs) to 1 EMR. At the time of this transition, the NHSN provided guidelines to validate healthcare-associated infection (HAI) denominators when switching from manual denominator collection to electronic denominator collection, but the NHSN did not give guidelines for validation when switching from 1 EMR to another. We aimed to build a validation process to ensure the accuracy of central-line and urinary catheter days reported to the NHSN after switching EMRs. Methods: Our validation process began with a statistical phase followed by a targeted manual validation phase. The statistical phase used 3 prediction methods (linear regression, time series analysis, and statistical process control [SPC] charts) to forecast device days after the EMR switch for units within hospitals. Models were developed using baseline data from the old EMR (January 2015 through the new EMR implementation). Using prespecified criteria for each method to determine discrepancies, we built a decision tree to identify units needing manual validation. Any unit that failed the statistical phase would need to participate in the manual validation phase, using a midnight census and direct visualization of devices. The manual validation process was composed of 14-day blocks. At the end of each block, if manual device days were within ±5% of EMR device days, they were considered validated. Manual validation would be repeated in 14-day blocks until 2 consecutive blocks passed within ±5%. Results: Overall, 157 units were evaluated for urinary catheter days and central-line days. Among them, 143 units passed the statistical validation test for urinary catheter days and 151 passed for central-line days. There was no specific pattern when comparing forecasted versus actual device days. The manual validation process for the 20 failing units (14 urinary catheter and 6 central-line units) is ongoing; preliminary results identified issues with missing nursing documentation in the EMR and with inaccurate manual counting of device days. There were no systematic discrepancies associated with the new EMR. Conclusions: We developed a novel validation process using statistical prediction methods supplemented with a targeted manual process. This process saved resources by identifying the units that need manual validation. Discrepancies were largely related to nursing documentation, which the infection prevention team addressed with additional training.Funding: NoneDisclosures: None
VIM-Positive Pseudomonas aeruginosa Sink Colonization Dynamics in Patient Rooms of a Dutch Tertiary-Care Hospital
Authors: Pirzadian Jannette , Klaassen H.W. , Goeij Inge de , Vos Margreet C. , Severin tte A. 
Published: November 2020

Background: In hospitals, Verona integron-encoded metallo-β-lactamase (VIM)–positive Pseudomonas aeruginosa (VPPA) frequently colonize sink drains. Sink use has been shown to disperse VPPA to other surfaces surrounding sinks, creating a potential transmission source. Objective: Because VPPA have been isolated from sinks and patients within our hospital, we analyzed colonization dynamics in 2 sinks identified as VPPA hot spots between 2012 and 2018. Methods: One sink was in an intensive care unit (ICU) patient room and the other was in a gastrointestinal surgery (GIS) patient room. ICU patients were screened for VPPA on admission, at discharge, and weekly during hospitalization. GIS patients were screened for VPPA only on clinical indication. In this study, patient and sink isolates were typed at 8 loci using multiple-locus variable-number tandem repeat (VNTR) analysis (MLVA). Results: Overall, 19 sink isolates from the ICU room were included. In 2013, VPPA isolates with identical VNTR genotypes (“clone B,” VNTR nos. 2-4-8-4-4-5-10-[-2]) were found in the wash basin, aerator, drain, and siphon. The drain plug was replaced in August 2013, but B was isolated from the drain 1 month later. Every year between 2014 and 2018, clone B and other closely related genotypes were recovered from this drain. In 2018, clone B was also found on the wash basin and counter. No positive patients were identified in this room until 2016, when a patient acquired clone B 6 days after admission. From the GIS room, 6 sink isolates and 4 patients’ isolates were included. In 2012, clone B was found on the wash basin and sink drain plug. Also, 3 VPPA-positive patients stayed in this room in 2012: at discharge, 1 patient was colonized by “clone A,” VNTR numbers 6-7-8-5-9-8-6-1. Furthermore, 2 other patients screened positive for clone B prior to admission in 2012, so they likely acquired VPPA elsewhere. The drain plug was replaced in 2013, and no VPPA was found again until 2017, when 2 VPPA-positive patients stayed in this room: 1 was already a carrier of clone B, and the other was a carrier of clone B 1 day after admission. No positive sink cultures were found until January 2018, when closely related B isolates were recovered from the wash basin, drain, and drain plug. Conclusions: Between 2013 and 2018, clone B persisted in the ICU room sink. In the GIS room sink, clone B may have disappeared after 2012, but it was reintroduced by known carriers. However, few patients became colonized after being admitted to these rooms, even when VPPA were discovered on surfaces outside the sink drain (ie, the wash basin and counter).Funding: NoneDisclosures: None
What’s in a Handshake? Exploring the Best Form of Greeting to Prevent Hand to Hand Spread of Viruses
Authors: Jones Lucas , Ha Wilson , Cadnum Jennifer , Donskey Curtis 
Published: November 2020

Background:
Respiratory and enteric viruses are highly contagious pathogens that can be spread by contaminated hands and surfaces. We hypothesized that alternatives to handshake greetings that reduce the time and surface area of hand contact would be associated with decreased transfer of viral particles. Methods: In a simulation of hand-contact greetings, volunteers (N = 22) used a keyboard contaminated with the benign bacteriophage MS2 and then performed a handshake and fist bump with additional volunteers. To assess viral transfer, hands were cultured for MS2, and plaque-forming units (PFU) were compared for the different types of hand contact. Additional simulations (N = 10) were conducted to compare viral transfer with the fist bump versus a cruise tap greeting (ie, a modified fist bump involving single knuckle contact). Results: The handshake greeting resulted in significantly greater transfer of MS2 than the fist bump (1.31 vs 0.54 log10 PFUs, P < .001) (Fig. 1A), but the frequency of transfer of virus was high for both greetings (91% transfer by handshake vs 59% by fist bump). The cruise-tap greeting did not result in reduced transfer of viral particles in comparison to the fist bump (Fig. 1B), and the frequency of transfer remained high (70%). Conclusions: The fist-bump and cruise-tap greetings could potentially reduce transmission of viruses in comparison to the handshake, but transfer occurred frequently, even with these greeting methods. To eliminate hand-to-hand transmission of respiratory and enteric viruses, alternative greeting methods that do not involve physical contact are needed.Funding: NoneDisclosures: None
Where To From Here? Identifying and Prioritizing Future Directions for Addressing Drug-Resistant Infection in Australia
Authors: Hall Lisa , Merlo Greg , Avent Minyon , Yarwood Trent , Smith Bonnie , Driel Mieke van 
Published: November 2020

Background: The Australian Government released a national strategy for antimicrobial resistance in 2015 that calls for a collaborative effort to change practices that have contributed to the development of drug-resistant infection and for the implementation of new initiatives to reduce antibiotic use. Although many achievements have been made in antimicrobial stewardship (AMS), particularly in the acute-care hospital setting, progress more broadly has been slow, and novel solutions are now required to improve clinical practice and community awareness. A facilitated workshop was undertaken at the 2019 National Australian Antimicrobial Resistance Forum to explore the complexity of AMS implementation in Australia and to prioritize future action. Methods: Participants engaged in rotating rounds of discussion using a world café format. The participants sat face-to-face at tables of 7 or fewer. At each table were 2 facilitators: one was a note taker and the other was the discussion leader. Each of the 6 facilitator pairs had a topic for discussion related to implementing antimicrobial stewardship in different contexts, with a focus on experience with strategies that have worked, major implementation barriers, and prioritizing the next steps. The topics for discussion included (1) engaging with hospital staff; (2) implementation in resource-poor settings; (3) implementation in primary care and aged care; (4) engaging and empowering the public; (5) linking data with implementation strategies; and (6) leadership. The facilitators moved between tables at 15-minute intervals to encourage evolving rounds of conversation. Once all tables had discussed all of the themes, the discussion concluded and notes were summarized. A qualitative analysis using an interpretive description approach was conducted using the discussion summaries. The documents were independently openly coded by 2 researchers to identify elements relating to the implementation of antimicrobial stewardship. An iterative approach was used to identify themes and reach a consensus on overarching emergent themes from the workshop. Results: In total, 39 experts (ie, pharmacists, infectious disease physicians, infection prevention nurses, researchers, journalists and consumers) participated in the facilitated discussions. Strategies were discussed relating to engaging with clinicians, consumers, and politicians; adapting to funding, governance, and accreditation limitations; and models for outreach of antimicrobial services. Other themes included the role of clinical champions and mentors as leaders and improving use of audit and feedback through focusing on monitoring appropriateness rather than usage. Conclusions: Recommendations from the workshop will be used to prioritize novel ideas to improve the implementation of AMS initiatives across Australia.Funding: NoneDisclosures: None
Whole-Genome Sequencing for Bacterial Strain Typing Using the iSeq100 Platform
Authors: Vestal Grant , Bruzek Steven , Lasher Amanda , Lima Amorce , Silbert Suzane 
Published: November 2020

Background: Hospital-acquired infections pose a significant threat to patient health. Laboratories are starting to consider whole-genome sequencing (WGS) as a molecular method for outbreak detection and epidemiological surveillance. The objective of this study was to assess the use of the iSeq100 platform (Illumina, San Diego, CA) for accurate sequencing and WGS-based outbreak detection using the bioMérieux EPISEQ CS, a novel cloud-based software for sequence assembly and data analysis. Methods: In total, 25 isolates, including 19 MRSA isolates and 6 ATCC strains were evaluated in this study: A. baumannii ATCC 19606, B. cepacia ATCC 25416, E. faecalis ATCC 29212, E. coli ATCC 25922, P. aeruginosa ATCC 27853 and S. aureus ATCC 25923. DNA extraction of all isolates was performed on the QIAcube (Qiagen, Hilden, Germany) using the DNEasy Ultra Clean Microbial kit extraction protocol. DNA libraries were prepared for WGS using the Nextera DNA Flex Library Prep Kit (Illumina) and sequenced at 2×150-bp on the iSeq100 according to the manufacturer’s instructions. The 19 MRSA isolates were previously characterized by the DiversiLab system (bioMérieux, France). Upon validation of the iSeq100 platform, a new outbreak analysis was performed using WGS analysis using EPISEQ CS. ATCC sequences were compared to assembled reference genomes from the NCBI GenBank to assess the accuracy of the iSeq100 platform. The FASTQ files were aligned via BowTie2 version 2.2.6 software, using default parameters, and FreeBayes version 1.1.0.46-0 was used to call homozygous single-nucleotide polymorphisms (SNPs) with a minimum coverage of 5 and an allele frequency of 0.87 using default parameters. ATCC sequences were analyzed using ResFinder version 3.2 and were compared in silico to the reference genome. Results: EPISEQ CS classified 8 MRSA isolates as unrelated and grouped 11 isolates into 2 separate clusters: cluster A (5 isolates) and cluster B (6 isolates) with similarity scores of ≥99.63% and ≥99.50%, respectively. This finding contrasted with the previous characterization by DiversiLab, which identified 3 clusters of 2, 8, and 11 isolates, respectively. The EPISEQ CS resistome data detected the mecA gene in 18 of 19 MRSA isolates. Comparative analysis of the ATCCsequences to the reference genomes showed 99.9986% concordance of SNPs and 100.00% concordance between the resistance genes present. Conclusions: The iSeq100 platform accurately sequenced the bacterial isolates and could be an affordable alternative in conjunction with EPISEQ CS for epidemiological surveillance analysis and infection prevention.Funding: NoneDisclosures: None