bluuebunny/crossref_metadata_2025 huggingface dataset:
Decision Support Tool for Screening of Tuberculosis Exposed Individuals Seeking Care at a Public Academic Health System
Authors: Cobb Stephanie , Nguyen Stephanie , Raj Deepa , Taherzadeh Dena , Sreeramoju Pranavi
Published: November 2020

Background:Mycobacterium tuberculosis (TB) is one of the leading causes of morbidity and mortality worldwide. At our health system, 50–100 patients are diagnosed with tuberculosis every year. One risk factor for TB is residence within a homeless shelter. In response to an increased number of cases in local homeless shelters, the health department sought assistance with contact tracing of individuals potentially exposed to tuberculosis. We report the results of contact tracing performed at our health system. Methods: The setting is a 770-bed, safety-net, academic hospital with community clinics and a correctional health center. Name, date of birth, and social security number of contacts potentially exposed during February 2009 to July 2013 were programmed into the electronic medical records to create a decision support tool upon entering the health system. The best practice alert (BPA) informed physicians of the exposure and offered a link to a screening test, T-spot.TB, and a link to an information sheet. This intervention was implemented from July 2013 to July 2015. After excluding patients with active TB, data on the magnitude of exposure in each homeless shelter and screening test results were analyzed with ANOVA using SPSS v 26 software. Results: Of the 8,649 identified exposed contacts, 2,118 entered our health system. Of those for whom the BPA was triggered, 1,117 had a T-spot.TB done, with 313 positive results and 57 borderline results. Table 1 shows that shelter 3 was correlated with a positive T-spot.TB. Conclusions: The BPA, which prompted physicians to evaluate an individual for TB, was effective at capturing high-risk, exposed individuals. Clinical decision support tools enabled our safety-net health system to respond effectively to a local public health need.

Funding: None

Disclosures: None
Developing a Public Health Model for Regional Infection Prevention Collaboration Among Healthcare Facility Networks
Authors: Garcia Erin P. , Epson Erin
Published: November 2020

Background:
Antimicrobial resistance (AR), including Clostridioides difficile infection (CDI), can spread across the healthcare continuum when patients move between facilities. In 2015, the CDC recommended that healthcare facilities (HCFs) and local public health departments (LPH) coordinate their efforts to prevent the spread of AR and CDI. Accordingly, the California Department of Public Health (CDPH) Healthcare-Associated Infections (HAI) Program developed a model for implementing regionally based AR/CDI prevention collaboratives within HCF networks. Methods: The CDPH HAI Program began identifying regions in California with high AR/CDI incidence or risk for AR/CDI emergence using NHSN data. During 2015–2019, we organized AR/CDI prevention collaboratives in these regions. We recruited HCFs for participation by presenting at local professional organization meetings and engaging skilled nursing facility corporate leadership. HAI Program infection preventionists conducted onsite infection prevention assessments at each participating HCF. HAI Program and LPH staff convened quarterly in-person learning and discussion sessions focused on infection prevention and antimicrobial stewardship best practices. Participating HCFs committed to facility-tailored process improvement plans and conducted self-assessments to evaluate infection prevention practice changes at the conclusion of the collaborative. For CDI-focused collaboratives, we used data reported to CDPH via NHSN to assess changes in hospital- and community-onset CDI incidence among participating hospitals before and after the collaboratives. Results: Since 2015, 205 HCFs in 15 LPH jurisdictions have participated in 6 regional AR/CDI prevention collaboratives. Participating HCFs reported improved implementation of AR/CDI prevention strategies. For CDI-focused collaboratives, hospital-onset CDI incidence decreased by ~3% per month among participating hospitals. The collaboratives created forums for communication and relationship building, which previously did not exist among LPH and the HCF networks. We used our experience and feedback from partners to develop an HAI Program AR Prevention Collaborative Tool Kit to help LPHs and healthcare organizations develop and implement regional AR/CDI prevention collaboratives in other at-risk or high AR/CDI prevalence areas in California. The tool kit includes materials developed for each of our collaboratives, which may be adapted to meet local needs. Conclusions: Regionally coordinated AR/CDI prevention initiatives among LPHs and HCFs can contribute to increased AR awareness, improved AR prevention practices, and decreased AR/CDI incidence. The effectiveness of regional AR/CDI prevention collaboratives may be the result of concurrent efforts to improve AR prevention practices both within individual HCFs and across patient sharing networks.

Funding: None

Disclosures: None
Development of National Infection Control and Prevention Guidelines in Georgia, 2017–2019
Authors: Madzgarashvili Lali , Weiss Jamine , Baidauri Marina , Geleishvili Marika , Lyman Meghan , Kolwaite Amy
Published: November 2020

Background: In 2015, the Ministry of Internally Displaced Persons from the Occupied Territories, Labor, Health and Social Affairs (MoLHSA) of Georgia identified infection prevention and control (IPC) as a top priority. Infection control legislation was adopted and compliance was made mandatory for licensure. Participation in the universal healthcare system requires facilities to have an IPC program and infrastructure. To support facilities to improve IPC, MoLHSA and the National Center for Disease Control and Public Health (NCDC) requested assistance from the US CDC to revise the 2009 National IPC guidelines, which were translated versions of international guidelines and not adapted to the Georgian context. Methods: An IPC guideline technical working group (TWG), comprising clinical epidemiologists, IPC nurses, head nurses, and infectious diseases doctors from the NCDC, academic and healthcare organizations and the CDC was formed to lead the development of the national IPC guidelines. Additionally, an IPC steering committee was established to review and verify the guidelines’ compliance with applicable decrees and regulations. The TWG began work in April 2017 and was divided into 4 subgroups, each responsible for developing specific guideline topics. A general IPC guideline template for low- and middle-income countries was used to develop 7 of the guidelines. Additional reference materials and international guidelines were used to develop all the guidelines. Drafts were shared with the subgroups and the steering committee during 2 workshops to discuss unresolved technical issues and to validate the guidelines. Results: The revised guidelines consist of 18 topics. In addition to standard precautions (eg, hand hygiene, personal protective equipment, injection safety, etc) and transmission-based precautions, the guideline topics include laundry, environmental cleaning and disinfection, decontamination and sterilization, occupational health and safety, biosafety in clinical laboratory, blood bank and transfusion services, intensive care unit, emergency room, and mortuary. They do not include healthcare-associated infection surveillance or organism-specific guidance. To supplement the guidelines, a separate implementation manual was developed. The guidelines were approved by MoLHSA in October 2019. The TWG continues to be engaged in IPC activities, assisting with guideline rollout, training, and monitoring, and drafting the National IPC strategy and action plans. Conclusions: The Georgian Ministry of Health developed national IPC guidelines using local experts. This model can be replicated in other low- and middle-income countries that lack country-specific IPC guidelines. It can also be adapted to develop facility-level guidelines and standard operating procedures.

Funding: None

Disclosures: None
Educational Intervention Improves Clinician Infection Prevention Knowledge and Behavior in a Pediatric Emergency Department
Authors: Linam Matthew , Rangwalla Nikita , Shroff Purvi , Armstead Christopher , Beiter James
Published: November 2020

Background: Children with contagious illness are frequently cared for in the pediatric emergency department. Incomplete infection prevention knowledge and incorrect practices create opportunities for transmission of pathogens. We implemented an educational intervention to improve clinician infection prevention knowledge and behavior. Methods: Clinicians providing care in an academic tertiary-care affiliated, pediatric ED were invited to participate in a 3-month educational intervention focusing on basic infection prevention principles such as hand hygiene and symptom-based isolation. Qstream, an educational platform that employs spaced-learning and game theory, was used to develop and electronically distribute 7 scenario-based questions. Questions were distributed 1 question per day 3 times per week. Questions were repeated until they were answered correctly twice. Round 1 of questions began in March 2019. After a ~4-week hiatus, the process was repeated in May 2019 (round 2). The Kirkpatrick model was used to measure the effectiveness of the educational intervention. Outcomes included clinician assessment of acceptability of the intervention, change in correct responses to knowledge-based questions over time, hand hygiene performance of clinicians and the number of infectious exposures in the emergency department. Results: Overall, 61 (73%) clinicians participated, of whom >90% liked the format, found it easy to use, and thought it required an appropriate amount of time. During round 1, average proficiency increased from 36% to 64%. During round 2, average proficiency increased from 62% to 78%. Starting in May 2019, physician hand hygiene gradually increased from a baseline of 78% to 100%. In the 10 months before and after March 2019, there were 2 exposure events involving 10 healthcare personnel versus 3 exposure events involving 4 healthcare personnel. Conclusions: Novel education strategies that utilize adult learning principles are user-friendly effective strategies that improve infection prevention knowledge and practice.

Funding: None

Disclosures: None
Emergence of Vancomycin Resistance after Treatment of Enterococcus: Risk Factors for Subsequent Pathogen Resistance
Authors: Shepard Zachary , Woods Robert , Patel Twisha
Published: November 2020

Background: Vancomycin-resistant Enterococcus (VRE) is a leading cause of nosocomial infections that carries an increased risk of mortality when compared to vancomycin-sensitive Enterococcus (VSE). Data on the frequency of conversion between VSE and VRE in patients are scarce. Among patients presenting with VSE infections, little is known about the subsequent risk of conversion to VRE in the initial treatment period. Methods: A descriptive analysis of VSE to VRE conversion and a retrospective case-control study were performed examining cases of VSE that had subsequent cultures positive for VRE within 90 days within a quaternary healthcare system. Cases were obtained from June 2013 through December 2018. Controls were patients who had VSE culture followed by another VSE culture and were matched by organism (E. faecalis or E. faecium), time between cultures, and initial culture site. Age, gender, healthcare, antibiotic, Clostridiodes difficile, proton pump inhibitor (PPI) exposure, and H2 blocker exposures, and prior VRE infection or colonization were abstracted from the electronic medical record. A univariate analysis with the Fisher exact test was performed with significance considered for P < .05. Results: In total, 8,913 cases of E. faecalis and 2,322 cases of E. faecium were included in the study. Of 8,913 cases of E. faecalis, 51 of 8,503 (0.6%) cultured VRE after VSE, and 47 of 403 (11.7%) cultured VSE after initial VRE. Of E. faecium, 51 of 783 (6.5%) cultured VRE after VSE, and 76 of 1,532 (5.0%) cultured VSE after initial VRE. In total, 76 cases were matched with 99 controls. Patients converting from VSE to VRE were more likely to have prior admission to an intensive care unit (P = .0207), prior positive swab or culture for VRE (P = .0114), previous C. difficile infection (P = .0155), prior vancomycin (P = .0022) and cefepime (P = .0089) exposure. Patients receiving vancomycin after initial VSE culture were more likely to have subsequent cultures positive for VRE (P = .0053). There was no difference in age (P = .966) or male sex (P = .7588). Conclusions: Conversion from VSE to VRE is common, and E. faecium is more likely to become resistant than E. faecalis. Reversion to a vancomycin-sensitive phenotype is also common, and E. faecalis is more likely to show subsequent sensitivity than E. faecium. Previous admission to an intensive care unit, prior colonization or infection with VRE, prior C. difficile infection, and exposure to vancomycin and cefepime are risk factors for emergence of VRE after treatment for vancomycin-sensitive Enterococcus.

Funding: None

Disclosures: None
Epidemiology and Microbiology of Outpatient Cases of Carbapenem-Resistant Enterobacteriaceae in Connecticut During 2018
Authors: Gupta Simran , Leung Vivian , Maloney Meghan , Macierowski Bobbie , Banach David
Published: November 2020

Background: Little is known about the epidemiology, microbiology, and clinical management of carbapenem-resistant Enterobacteriaceae (CRE) in outpatient settings. In Connecticut, all clinical CRE isolates are submitted to the state public health laboratory (SPHL) for a customized panel of antimicrobial susceptibility and carbapenemase gene testing. We describe all outpatient cases of CRE in Connecticut in 2018, including location of presentation, risk factors, microbiology and aspects of treatment. Methods: Outpatient CRE cases were defined as CRE infection in a patient not hospitalized at the time of positive CRE culture or within 30 days after culture collection. Outpatient cases were identified from routine statewide CRE reporting by reviewing clinical and laboratory data. A questionnaire was sent to outpatient providers who ordered the cultures that yielded CRE to collect additional clinical information. Antimicrobial susceptibility and carbapenemase gene detection results from the SPHL were also summarized. Results: Among 53 outpatient CRE cases (1 blood, 52 urine), the most common organisms were Enterobacter (25, 47%), Klebsiella (12, 23%), and E. coli (11, 21%). Overall, 21 (39.6%) patients presented in primary care settings, 8 (15%) in urology offices, 6 (11%) in women’s health/OBGYN clinics; the remainder presented across various clinical settings (Fig. 1). Of 42 patients for whom clinical data were available, 45% had been hospitalized within the prior year and 19% had a chronic indwelling device. Among outpatient CRE cases, infectious diseases consultation was reported in 9.5% and lab consultation in none. Median patient age was 83 years. Of 36 CRE samples for which lab data were available, 9 (25%) were carbapenemase-producers (CP-CRE), of which 8 were blaKPC positive. Sensitivity rates to oral antimicrobials ranged from 43% to 75% (Table 1). Conclusions: CRE infections occur in several different outpatient settings, and formal ID consultation in the management of these patients is infrequent. These findings highlight the critical need for providers across different outpatient specialties to be familiar with the clinical management and infection control practices needed in caring for patients with CRE. Hospitalization within the year prior to presentation was frequent among patients who developed subsequent CRE-positive cultures. Most outpatient CP-CRE cases in CT are due to KPC production. Overall, sensitivity to oral antimicrobials frequently prescribed in outpatient settings is low, providing additional challenges to the outpatient management of patients with CRE infection. Fosfomycin, though only approved for E. coli infections, may be an acceptable antibiotic choice for the treatment of these patients.

Funding: None

Disclosures: None
Estimating the Impact of County Boundaries on State-wide Patient-Sharing Network Models
Authors: Sewell Daniel , Justice Samuel , Hahn Amy , Pemmaraju Sriram , Segre Alberto , Polgreen Philip
Published: November 2020

Background:
In the field of public health, network models are useful for understanding the spread of both information and infectious diseases. Collecting network data requires determining network boundaries (ie, the entities selected for data collection). These decisions, if not made carefully, have potential outsized downstream effects on study findings. In practice, collaboration and coordination between healthcare organizations are often dictated by historical or geopolitical boundaries (eg, state or county boundaries), which may distort the underlying network under study, and thereby affect the reliability and/or accuracy of the network model. Objective: We compared natural communities in a patient-sharing network with those induced by geopolitical boundaries. Methods: Using data from the Healthcare Cost and Utilization Project (HCUP), we constructed a patient-sharing network among hospitals in California, splitting the data into a training set and a holdout set. We performed edge-betweenness clustering on the training set, and with the holdout set, we compared the resulting partition with the partition by counties using modularity. We also clustered contiguous counties that might function more cohesively together than individually. We performed spatially constrained hierarchical clustering on the network constructed from total patient flow between pairs of counties. The results were again compared via modularity on the holdout set to the county partition. Lastly, we built an individual-based model (IBM) using HCUP and American Hospital Association data to perform epidemic simulations. For each of several counties, we implemented this model to estimate the proportion of patients infected over time. We then reran the individual-based model using the entire state while dividing the results into corresponding counties. Results: In total, 680,485 patients transferred between 374 hospitals in 55 counties from 2003 to 2011. The out-of-sample modularity for the edge-betweenness clustering partition was 464% higher than that of the county partition. Aggregating the counties into half as many contiguous clusters was 319% higher, and aggregating them into 6 clusters was 489% higher (Fig. 1). The epicurves from the individual-based model ranged from small to significant deviations between state versus county boundaries (Fig. 2) . Conclusions: Collecting network data using externally imposed boundaries may lead to inaccurate network models. For example, counties serve as a poor proxy for their underlying communities, resulting in poor overall disease spread simulation results when county boundaries are allowed to drive network construction. These issues should be considered when building coordination partnerships such as the Accountable Communities for Health.

Funding: None

Disclosures: None
Etiology, Incidence, and Risk Factors for Meningitis after Ventriculoperitoneal Shunt Procedures: A Multicenter Study
Authors: Silva Danilo , Couto Henrique , Pereira Hoberdan , Souza Gregory Lauar , Silveira Andressa , Carvalho Handerson Dias Duarte de , Bracarense Fernando , Carvalho Fernanda de , Couto Braulio , Starling Carlos
Published: November 2020

Background: The ventriculoperitoneal shunt is the main procedure used for to treat communicating hydrocephalus. Surgical site infection associated with the shunt device is the most common complication and a cause of morbidity and mortality of related to the treatment. We sought to answer 3 questions: (1) What is the risk of meningitis after ventricular shunt operations? (2) What are the risk factors for meningitis? (3) What are the main microorganisms causing meningitis? Methods: We conducted a retrospective cohort study of patients undergoing ventricular shunt operations between July 2015 and June 2018 from 12 hospitals at Belo Horizonte, Brazil. Data were gathered by standardized methods defined by the CDC NHSN. Our sample size was 926, and we evaluated 26 preoperative and operative variables by univariate and multivariate analysis. Our outcome variables of interest were meningitis and hospital death. Results: In total, 71 cases of meningitis were diagnosed (risk, 7.7%; 95% CI, 6.1%–9.6%). The mortality rate among patients without infection was 10%, whereas hospital mortality of infected patients was 13% (P = .544). The 3 main risk factors for meningitis after ventricular shunt were identified by logistic regression model: age <2 years (OR, 3.20; P < .001), preoperative hospital stay >4 days (OR, 2.02; P = .007) and >1 surgical procedure, in addition to ventricular shunt (OR, 3.23; P = .043). Almost 1 of 3 of all patients was <2 years old (290, 31%). Also, 430 patients had >4 preoperative days (46%). Patients aged ≥2 years who underwent surgery 4 days after hospital admission had an increased risk of meningitis, from 4% to 6% (P = .140). If a patient <2 years old underwent surgery 4 or more days after hospital admission, the risk of meningitis increased from 9% to 18% (P = .026; Fig. 1). We built a risk index using the number of main risk factors based on a logistic regression model (0, 1, 2 or 3; Fig. 2). Conclusions:We identified 2 intrinsic risk factors for meningitis after ventricular shunt, age <2 years and multiple surgical procedures, and 1 extrinsic risk factor, the preoperative length of hospital stay.

Funding: None

Disclosures: None
Evaluation of a Continuously Active Disinfectant for Disinfection of Mobile Equipment
Authors: Redmond Sarah , Silva Sandra Y , Cadnum Jennifer , Carlisle Matthew , Donskey Curtis
Published: November 2020

Background: One of the limitations of current cleaning and disinfection strategies is that cleaned surfaces rapidly become recontaminated. In laboratory testing, a novel quaternary ammonium disinfectant provided sustained antimicrobial activity against multiple pathogens on surfaces after 24 hours. We hypothesized that this continuously active disinfectant would be effective in reducing contamination of portable medical equipment in a real-world healthcare setting.

Methods: In a hospital and affiliated long-term care facility, 114 portable devices were randomized to receive no treatment (N = 38) or a single spray application of a quaternary ammonium-alcohol disinfectant (N = 38) or of the continuously active disinfectant (N = 38). The devices were cultured at baseline and on days 1, 4, and 7 after treatment for total aerobic colony counts, methicillin-resistant Staphylococcus aureus (MRSA), and enterococci.

Results: As shown in Fig. 1, both spray disinfectants significantly reduced total aerobic colony counts in comparison to the untreated controls. The continuously active disinfectant resulted in sustained significant reductions in aerobic colony counts in comparison to baseline levels (P < .05), whereas counts returned to baseline levels by day 4 in the quaternary ammonium-alcohol disinfectant group. Recovery of MRSA and enterococci was significantly reduced on days 1–7 in the continuously active disinfectant group versus untreated controls (3 of 93, 3% vs 20 of 97, 21% respectively; P = .002), but not in the quaternary ammonium-alcohol disinfectant (11 of 97, 11%; P = .12). Conclusions: A single spray application of a continuously active disinfectant resulted in sustained reductions in total aerobic colony counts over 7 days and reduced recovery of MRSA and enterococci. The continuously active disinfectant could potentially reduce the risk for transmission of pathogens by portable devices.

Funding: None

Disclosures: None
Evaluation of a Sick Employee Online Log System for Tracking Sick Hospital Employees During Two Influenza Seasons
Authors: Cleve William , Cochran Kathy , Ramsey Keith M.
Published: November 2020

Background: Since 2009, Vidant Health has used a Sick Employee Online Log (SEOL) system to track illnesses among employees and to capture this information in real time. The CDC assessed the 2017–2018 influenza season as a high-severity influenza season, whereas the 2018–2019 influenza season was of moderate severity. Objective: In this research project, we sought to determine whether the influenza season severity would affect either the hospital-based employee illness surveillance system results or would correlate with state influenza-like illness (ILI) visits. Methods: The SEOL system is an internet-based system initiated in December 2008. When a hospital employee calls in sick, the department manager records whether the employee reports the following symptoms: nausea, vomiting, diarrhea, upper respiratory infection, fever, sore throat, headache, conjunctivitis, rash, and/or cough. The information is confidential, with raw data access restricted to review by occupational health and infection control leadership. The correlation value was determined for each symptom using the North Carolina Division of Human Services (NC DHHS) percentage of ILI visits in statewide emergency departments.1 The data collection dates covered January 1–May 31 for each year. In this study, only symptoms related to influenza were included: upper respiratory infection, fever, influenza-like illness, cough and self-reported influenza. Correlation values were calculated using MS Excel software. Results: There were no breaks in confidentiality. All of the correlation values had a correlation value of 0.5 or better (Fig. 1), showing good correlation with the NC DHHS ILI data for both years; however, the more severe 2017–2018 influenza season had correlation values of 0.7 for all symptoms, versus 0.52–0.59 for URI and ILI, respectively, only during 2018–2019. Conclusions: The higher-severity influenza season did correlate with a higher r values when compared to North Carolina’s DHHS ILI emergency department data than did the influenza season of moderate severity. A possible explanation is that a higher-severity influenza season would correlate better than a moderate influenza season because it shows fewer ILI peaks and troughs. In conclusion, the SEOL system served as an early warning that influenza is present among our staff, and it correlates well with the state system for ILI surveillance. Potential limitations of SEOL are that respiratory symptoms are not specific to influenza; thus, they are subject to variation due to other respiratory viruses circulating among our employees.

1. The North Carolina Disease Event Tracking and Epidemiologic Collection Tool. NC DETECT website. http://www.ncdetect.org. Accessed Nov 8, 2019.

Funding: None

Disclosures: None
Evaluation of Surgical Site Infections After Change in Surgical Prophylaxis in VAD Patients
Authors: Min Emily , Adediran Timileyin , Thom Kerri , Heil Emily
Published: November 2020

Background: In October 2013, the University of Maryland Medical Center established a formal antibiotic prophylaxis protocol for patients undergoing ventricular assist device (VAD) placement, replacing a previous system of various broad-spectrum antibiotic combinations typically for prolonged durations based on surgeon preference. This new protocol consisted of a standardized regimen of 72 hours of vancomycin and ceftriaxone after the procedure. The objective of this project was to evaluate the rate of surgical site infection (SSI) related to VAD placement to ensure that implementing the new protocol did not cause an increase in SSI rates. Methods: The study was a retrospective cohort study of patients who had undergone VAD placement before the protocol change (January 1, 2011, to October 1, 2013) and after the change (October 1, 2013, to November 15, 2015). The primary outcomes was the difference in SSI rate before and after the protocol change using CDC NHSN definitions. Pertinent data points of interest included reason for VAD placement, duration/type of antibiotics used, delayed sternal closure, SSI, characterization of infection (bloodstream, driveline, or pocket), organism identified on culture and mortality at 30 days and 1 year. SSI rates were assessed using the Fischer exact test, and descriptive statistics were used for other outcome variables. Results: In total, 75 patients were included before the protocol and 46 after the protocol change. Overall, 27% and 17% of patients were on therapeutic antibiotics prior to the VAD placement, respectively (P = 0.23). Also, 8 (6.6%) patients in the preintervention group had an SSI compared to 1 patient (0.8%) in the postintervention group (P = .15). Adherence to the protocol was suboptimal, with 27% of patients in the postintervention group receiving non–protocol-adherent antibiotics and 65% of patients receiving antibiotics >96 hours postoperatively. When evaluating the patients collectively, SSI rates were the same when antibiotics were discontinued <72 hours postoperatively versus when antibiotics were continued beyond 72 hours postoperatively or were not given at all postoperatively (3.1% vs 10.7% vs 0%; P = .24). SSI rates were also no different among patients who received cefazolin monotherapy (0%), vancomycin and ceftriaxone (2.7%), vancomycin and piperacillin tazobactam (2%), and other antibiotic combinations (7.7%) for surgical prophylaxis (P = 0.1). Conclusions: No change in SSI rates was noted after a protocol change narrowing the spectrum and duration of antibiotic prophylaxis was implemented. Evaluation of optimal surgical prophylaxis in this patient population is difficult due to low event rates and frequent therapeutic indications for antibiotics outside the standard prophylaxis. Despite these challenges, this study supports the safety of studying SSI prophylaxis reduction in the VAD population. Further studies are reasonable and warranted.

Funding: None

Disclosures: None
Evaluation of the bioMérieux EPISEQ-CS Software for wgMLST-Based Bacterial Strain Typing
Authors: Lima Amorce , Bruzek Steven , Lasher Amanda , Vestal Grant , Silbert Suzane
Published: November 2020

Background: Whole-genome sequencing (WGS) is becoming the method of choice for outbreak analysis of microbial pathogens. However, the main challenge with WGS for microbial strain typing is the conversion of raw sequencing data to actionable results for epidemiology and surveillance analysis. We evaluated the bioMrieux EPISEQ-CS, a cloud-based WGS data analysis software for outbreak detection to compare the results for 4 groups of different species previously characterized by strain typing and commonly isolated in hospital-acquired infections. Methods: In total, 30 methicillin-resistant Staphylococcus aureus (MRSA), 15 Clostridioides difficile (CDIFF), 17 Pseudomonas aeruginosa (PSA), and 10 Acinetobacter baumannii (ACB) isolates were included in this study. All isolates had been previously characterized by rep-PCR using the DiversiLab system (bioMrieux, France) and saved at 70C. Before testing, samples were thawed and plated, and DNA extraction was performed on the QIAcube (Qiagen, Hilden, Germany) using the DNEasy Ultra Clean Microbial kit extraction protocol. DNA libraries were prepared using the Nextera DNA Flex Kit and sequenced on the Illumina iSeq100 platform according to manufacturer’s recommendations (Illumina, San Diego, CA). Generated sequences were uploaded into EPISEQ-CS, and wgMLST-based analysis was performed. We compared clusters generated by the DiversiLab system and EPISEQ-CS. Results: DiversiLab identified 9 MRSA clusters among 30 isolates. EPISEQ-CS reclassified 14 of 30 isolates into 5 MRSA clusters and the remaining 16 isolates were unrelated. DiversiLab identified 2 CDIFF clusters among 15 isolates. EPISEQ-CS reclassified 3 isolates into 1 CDIFF cluster and determined the remaining 12 to be unrelated. DiversiLab identified 5 PSA clusters among 17 isolates, whereas EPISEQ-CS reclassified all 17 isolates as unrelated. DiversiLab identified 2 ACB clusters among 10 isolates, whereas EPISEQ-CS reclassified 2 ACB isolates into 1 cluster and determined 8 to be unrelated. Analysis using Simpson’s diversity index (D) suggested that the EPISEQ-CS showed increased diversity when compared to DiversiLab clustering across all bacterial species analyzed in this study. Conclusions: EPISEQ-CS enabled a comprehensive wgMLST analysis, including quality control and comparative epidemiological analysis, thereby providing a more reliable method for bacterial strain typing. As WGS becomes more affordable and applicable to routine epidemiological surveillance, EPISEQ-CS provides an informative tool in the monitoring of hospital-acquired infections.

Funding: None

Disclosures: None
Exploring the Potential Limitations of Using Medicare Data to Study the Spread of Infections from Hospital Transfers
Authors: Sewell Daniel , Justice Samuel , Pemmaraju Sriram , Segre Alberto , Polgreen Philip
Published: November 2020

Background: Patient sharing between hospitals has long been known to be a contributor to the regional transmission of hospital-acquired infections. This inter–healthcare-facility connectedness suggests that regional, as opposed to local, control and surveillance strategies should be favored. However, the absence of easily and universally accessible patient transfer data hampers researchers and public health agencies who wish to build accurate network models of interfacility transmissions. Medicare data offer only a biased subsample of the full patient transfer network, but because it is widely available, this data source has historically been used for inference and simulation studies. Objective: The purpose of this study was to determine whether Medicare data could successfully be recalibrated to more closely resemble 100% inpatient capture data. Methods: We used data from the Healthcare Cost and Utilization Project (HCUP) to construct 100% capture and Medicare-only patient sharing networks among hospitals in Iowa, Wisconsin, and Nebraska. We used matrix decomposition techniques on the Medicare-only networks along with hospital characteristics from the American Hospital Association (AHA) data for feature construction in a truncated Poisson regression model, and we used Monte Carlo integration to obtain predicted values. These predicted values served as calibrated Medicare-only networks. We split the patient transfer data into training and testing sets and computed the mean squared prediction error (MSPE) for the testing data. We also built an individual based model (IBM) using HCUP and AHA data to perform epidemic simulations that depended on a matrix of patient transfer rates between hospitals. We then compared epicurves from these IBMs resulting from 100% capture networks, Medicare-only networks, and our calibrated networks. Results: Our calibrated networks reduced the MSPE with respect to Medicare-only networks by 84%, 47%, and 88% for Iowa, Wisconsin, and Nebraska, respectively. Although the epicurves from Medicare-only networks differed considerably from that from 100% capture networks, our calibrated networks retained high fidelity to the curves obtained from 100% capture networks. Conclusions: Medicare-only networks greatly underestimate the number of patients transferred between hospitals. Our approach allows us to use Medicare data to estimate networks when 100% inpatient capture is unavailable.

Funding: None

Disclosures: None
Feasibility of Developing Traditional Facility-Specific Nursing Home Antibiograms
Authors: Taylor Lindsay , Howe Michael , Osman Fauzia , Crnich Christopher
Published: November 2020

Background: An antibiogram is a tool for tracking and reporting antimicrobial resistance; the CDC has endorsed as part of a comprehensive antimicrobial stewardship program in nursing homes. We have previously shown that antibiogram utilization has increased in nursing homes, but most facilities employ tools that are not based on facility-specific data. In this study, we investigate the feasibility to develop antibiograms using facility-specific data and compare these results with antibiograms developed using data from multiple facilities that share the same lab and geographic region. Methods: Raw, de-identified culture results from January 1 through December 31st, 2018 were collected from participating nursing homes and their consulting microbiology laboratories under an IRB-exempt protocol. Culture results were entered and stored in REDCap. Number of isolates per species was examined based on nursing home, nursing home laboratory network, and region. Percentage sensitivities of the most frequently isolated species to commonly used antibiotics were calculated at the nursing home and regional level and compared. T tests of the absolute difference between nursing home- and regional level percentage sensitivities were performed. All data analyses were performed in R software. Results: The mean annual cultures per nursing home was 23.5 (SE, ±3.29). Grouping cultures by lab and region increased the mean culture count 6-fold and 12-fold, respectively. The most commonly isolated species were Escherichia coli (29.7%), Enterococcus spp (11.6%), Proteus spp (10%), Klebsiella spp (8.5%). None of the nursing homes had >30 isolates of a single species (Fig. 1). Escherichia coli was the only species that exceeded the 30-isolate threshold when aggregated at the laboratory network level (Fig. 2). Grouping nursing home cultures by region provided the greatest average isolate count across the most common species. The greatest differences in percentage sensitivity between nursing homes and their region were noted for Escherichia coli and Proteus spp to fluoroquinolones (>20% difference; P < .01). The difference in sensitivity was <5% for Escherichia coli to nitrofurantoin. Conclusions: Nursing homes do not generate enough annual isolates to create antibiograms compliant with Clinical Laboratory Standard Institute guidelines. Grouping isolates from multiple nursing homes at the regional level does reliably exceed the 30-isolate threshold for multiple bacterial species but leads to susceptibility estimates that may vary substantially from those observed at the facility level. Alternative tools for tracking antibiotic resistance and guiding antibiotic prescribing decisions at the local level are needed.

Funding: None

Disclosures: None
First-Time Use of Clinical Pharmacists to Improve Appropriate Antibiotic Prescribing in a Medical ICU in Viet Nam
Authors: Van Tuan Mai , Pham Nhu Hiep , Hoang Thi Lan Huong , Dang Nhat Tan , Pham Thi Ngoc Anh , Nguyen Thanh Huy , Cao Quoc Tuan , Pham Vu Thuy Hang , Nguyen Nga , Vasquez Amber , Phong Lan Nguyen Thi , Bui Hien , Bui Manh Hung
Published: November 2020

Background: Antibiotic overuse has led to increasing rates of antibiotic resistant infections and unnecessary antibiotic costs. Clinical pharmacists can play a key role in optimizing appropriate use of antimicrobials and reducing antimicrobial resistance. However, the role of clinical pharmacists in antimicrobial stewardship is new and not well established in Viet Nam. Objective: We evaluated the use of clinical pharmacists for improved antimicrobial prescribing. Methods: We assembled an antibiotic stewardship program (ASP) team consisting of a clinical pharmacist and a specialist in infection prevention and control in a 60-bed medical intensive care unit (MICU) at Hue Central Hospital in central Viet Nam. During January–September 2018, the ASP team collected baseline antibiotic prescribing days of therapy (DOT) for all antibiotics administered in the MICU. Then, from October 2018 through June 2019, the ASP team reviewed daily positive clinical bacterial cultures and susceptibility results for all patients present in the MICU. They reviewed medical charts, including antimicrobial prescriptions, during week days and only if patient was still in the ICU at the time of ASP rounds. The team recommended changes to antibiotic therapy verbally to physicians and left the decision to change antibiotic therapy to their discretion. The ASP team documented whether their recommendations were accepted or rejected. Statistical significance was determined using the Student t test. Results: The ASP team reviewed 160 medical charts and made 169 ASP recommendations: 122 (72%) to continue current treatment; 24 (14%) to monitor drug levels or obtain diagnostic tests; 10 (6%) to discontinue therapy; 6 (4%) to de-escalate therapy; 5 (3%) to adjust doses; and 2 (1%) to broaden therapy. Only 8 of the recommended changes (5%) were declined by the clinicians. The average monthly DOT for all types of antibiotics declined significantly from 2,213 to 1,681 (24% decrease; P = .04). Reductions in DOT for the most common broad-spectrum antibiotics included colistin from 303 to 276 (P = .75); imipenem-cilastatin 434 to 248 (P = .06); doripenem 150 to 144 (P = .85). Piperacillin-tazobactam increased from 122 to 142 (P = 0.75). Conclusions: We demonstrated that daily review of cultures and antibiotic use decreased overall antibiotic prescribing. Given that few recommendations included discontinuation of therapy, ASP rounds likely raised awareness for clinicians to optimize antibiotic use.

Funding: None

Disclosures: None
Genomic Epidemiology of Clostridioides difficile Sequence Types 1 and 2 Across Three US Medical Centers
Authors: Miles-Jay Arianna , Young Vincent , Pamer Eric , Savidge Tor , Kamboj Mini , Garey Kevin , Snitkin Evan
Published: November 2020

Background:Clostridioides difficile is a toxin-producing bacterium that is the foremost cause of healthcare-associated diarrhea in the United States. Recent epidemiologic and genomic evidence indicates that divergent C. difficile strains have varying propensities for transmission within healthcare settings. We investigated whether and how these differences are reflected in the genomic epidemiology of 2 common C. difficile strains—sequence type (ST) 1 (analogous to Ribotype 027) and ST2 (associated with Ribotypes 014/020)—across 3 geographically distinct US medical centers. Methods: Between 2011 and 2017, a convenience sample of ST1 and ST2 C. difficile clinical isolates were collected from 3 US sites: The University of Michigan Medical Center, Texas Medical Center Hospitals, and Memorial Sloan Kettering Cancer Center. Isolates underwent whole-genome sequencing and in silico multilocus sequence typing to verify strain types. Sequences were mapped to ST1 and ST2 reference genomes and single nucleotide variants (SNVs) were identified, filtered, and used to construct pairwise SNV distance matrices. A range of pairwise SNV distance thresholds were applied to assess genetic linkages consistent with recent transmission within ST1 compared to within ST2. Proportions of genetically linked isolates were compared using 2 tests. Results: We identified 200 ST1 and 188 ST2 isolates across the 3 collection sites. Overall, ST2 was more genetically diverse than ST1 (pairwise SNV distance range, 0–156 SNVs and 0–78 SNVs, respectively). ST2 isolates displayed significantly less evidence of recent transmission: 10 ST2 isolates (5.3%) were within 2 SNVs of another isolate compared to 88 (44%) ST1 isolates (P .001) (Fig. 1). As the SNV threshold increased to 5 and 10 SNVs, this trend was maintained (all P < .001). ST2 isolates were also more likely to be genetically linked to an isolate from a different collection site than ST1 isolates. Among isolates with genetic links to at least 1 other isolate at the 5 SNV and 10 SNV thresholds, 21 of 37 and 74 of 89 ST2 isolates (57%, 83%) were linked to an isolate from a different collection site, compared to 2 of 88 and 48 of 157 ST1 isolates (2% and 31%, respectively; both P < .001). Conclusions: Compared to C. difficile ST1 isolates, ST2 isolates displayed less evidence of recent healthcare transmission and were more likely to be genetically linked to isolates from divergent collection sites. Interpreting genetic linkages among C. difficile isolates requires an understanding of regional and strain-specific genetic diversity to avoid misattribution of genetic linkages to recent transmission.

Funding: None

Disclosures: None
Healthcare-Associated Infections: Enterobacteriaceae Bloodstream Infections in the ICU Settings
Authors: KŁOS Marta , Pomorska-Wesołowska Monika , Romaniszyn Dorota , Chmielarczyk Agnieszka , Wojkowska-Mach Jadwiga
Published: November 2020

Background: Bloodstream infections (BSIs) are one of the most frequently observed hospital-acquired infections (HAIs). Objectives: We aimed to describe the epidemiology and drug resistance of hospital-acquired Enterobacteriaceae BSIs and to check for any correlation with the type of hospital care. Methods: In 2015–2018, 333 Enterobacteriaceae isolates were collected from hospitalized internal medicine and surgical patients. The drug-resistance testing was conducted according to the EUCAST recommendations, using the disc-diffusion method to determine resistance to penicillin, cephalosporins, carbapenems, aminoglycosides, fluoroquinolones, and sulfamethoxazole with trimethoprim. Tests confirming the presence of extended-spectrum β-lactamases (ESBLs) and KPC, NDM, and OXA-48 carbapenemases were performed. We determined the minimum inhibitory concentration (MIC) values (mg/L) for selected antibiotics. To detect the resistance genes, a single PCR reaction, a multiplex PCR, and a real-time PCR were conducted. Results: The prevalence rate of Enterobacteriaceae bacilli in BSIs was 23.5%. Penicillin resistance remained at a very high level of almost 100%, with only the piperacillin-tazobactam resistance remaining at 19%–22%. The same was true for cephalosporins: the bacilli have only shown a high susceptibility to cefoperazone with sulbactam (4%–14% of them were resistant). Ciprofloxacin (53%–62%) and sulfamethoxazole with trimethoprim (48–55%) have proven highly resistant. Carbapenems were the only antibiotics with susceptibility at 98%–99%. No difference was found between the types of hospital care (surgical vs nonsurgical) and the levels of antimicrobial resistance in the studied Enterobacteriaceae isolates (Table 1). Conclusions: The high prevalence of Enterobacteriaceae bacilli in BSI is particularly worrying, as is the high rate of resistance to cephalosporins and aminoglycosides, which are often used in the empirical therapy. Unfortunately, our results indicate the need to base the empirical therapy on carbapenems.

Funding: This work was supported by a grant from Jagiellonian Univerity Medical School (No. N41/DBS/000053)

Disclosures: None