bluuebunny/crossref_metadata_2025_split huggingface dataset:
Genomic Epidemiology of Carbapenemase-Producing Enterobacterales (CPE) in Toronto, Canada
Authors: Jamal Alainna Juliette , Williams Victoria , Leis Jerome , Tijet Nathalie , Zittermann Sandra , Melano Roberto , Mataseje Laura , Mulvey Michael , McGeer Allison 
Published: November 2020

Objective:
We investigated whether whole-genome sequencing (WGS) data altered interpretations of clonal transmission as determined by conventional epidemiology and pulsed-field gel electrophoresis (PFGE) at a tertiary-care hospital (hospital Z, HZ). Methods: We included all carbapenemase-producing Enterobacterales (CPE)–colonized or –infected patients identified via population-based surveillance from 2007 through 2018, who were admitted to HZ during and/or in the year prior to or following CPE detection. HZ reported clonal transmission clusters using epidemiology and PFGE for CPE identified at HZ or reported to HZ by other hospitals as potentially acquired at HZ. We assessed single-nucleotide polymorphism (SNP) phylogenies and case epidemiology. Results: Overall, 85 CPE-colonized or -infected patients were included: 50 were detected at HZ and 35 were detected at another local hospital but were admitted to HZ in the previous or following year. HZ reported 6 transmission clusters (Table 1). SNP analyses confirmed clusters B, C, E, and F. In cluster A, SNP analyses cast doubt on 2 of 9 cases (possibly representing plasmid transmission) but also identified 2 additional cases with isolates highly related (0–3 SNP differences) to other isolates. One case may be the index case: a travel-related case who stayed on the same unit as case 1, 4 months before case 1 detection. The second case stayed in a room previously occupied by 5 cluster A cases. In cluster D, SNP analyses found 1 additional case whose isolate was highly related (ie, 17–19 SNP differences) to other isolates. This case was identified a year before cluster D at another hospital that shares patients with HZ; however, the case’s admission to HZ was after all cluster D cases were detected and no direct epidemiologic link was identified. Conclusions: WGS data can identify cases belonging to transmission clusters that conventional epidemiologic methods missed.Funding: NoneDisclosures: Allison McGeer reports funds to her institution from Pfizer and Merck for projects for which she is the principal investigator. She also reports consulting fees from Sanofi-Pasteur, Sunovion, GSK, Pfizer, and Cidara
Molecular Epidemiology and Outcomes of Patients with Carbapenem-Resistant Enterobacteriaceae Bacteriuria, Atlanta 2012–2015
Authors: Howard-Anderson Jessica , Petit Robert , Bower Chris , Smith Gillian , Ansari Uzma , Halpin Alison , Karlsson Maria , Lawson Adrian , Lutgring Joseph , McAllister Gillian , Farley Monica , Jacob Jesse , Satola Sarah 
Published: November 2020

Background: Carbapenem-resistant Enterobacteriaceae (CRE) represent a significant antibiotic resistance threat, in part because carbapenemase genes can spread on mobile genetic elements. Here, we describe the molecular epidemiology and outcomes of patients with CRE bacteriuria from the same city in a nonoutbreak setting. Methods: The Georgia Emerging Infections Program performs active, population-based CRE surveillance in Atlanta. We studied a cohort of patients with CRE (resistant to all tested third-generation cephalosporins and ≥1 carbapenem, excluding ertapenem) first identified in urine, and not in a prior or simultaneous sterile site, between 2012 and 2015. Whole-genome sequencing (WGS) was performed on a convenience sample. We obtained epidemiologic and outcome data through chart review and Georgia Vital Statistics records (90-day mortality). Using WGS, we created a core-genome alignment-based phylogenetic tree of the Klebsiella pneumoniae isolates and calculated the SNP difference between each sample. Using SAS version 9.4 software, we performed the Fisher exact test and univariable odds ratios (OR) with 95% CI to compare patient isolates with and without a carbapenemase gene. Results: Among 81 patients included, the median age was 68 (IQR, 57–74) years, and most were female (58%), black (60%), and resided in a long-term care facility 4 days prior to culture isolation (53%). Organisms isolated were K. pneumoniae (84%), Escherichia coli (7%), Enterobacter cloacae (7%), and Klebsiella oxytoca (1%). WGS identified at least 1 β-lactamase gene in 91% of the isolates; 85% contained a carbapenemase gene, the most frequent of which was blaKPC-3 (94%). Patients with CRE containing a carbapenemase gene were more likely to be black (OR, 3.7; 95% CI, 1.0–13.8) and to have K. pneumoniae (OR, 8.9; 95% CI, 2.2–35.0). Using a core-genome alignment of 3,708 genes (~63% of the complete genome), we identified a median of 67 (IQR, 23–3,881) SNP differences between each K. pneumoniae isolate. A phylogenetic tree identified clustering around carbapenemase gene and multilocus sequence type (84% were ST 258) but not based on referring laboratory or county of residence (Fig. 1). Although 7% of patients developed an invasive CRE infection within 1 year and 21% died within 90 days, having a carbapenemase gene was not associated with these outcomes. Conclusions: Molecular sequencing of a convenience sample of CRE bacteriuria support K. pneumoniae ST258 harboring blaKPC-3 being distributed throughout the Atlanta area, across the healthcare continuum. Overall mortality was high in this population, but the presence of carbapenemase genes was not associated with worse outcomes.Funding: NoneDisclosures: NoneDisclosures: NoneFunding: None
Point-Prevalence Survey on Antibiotics Use in Six Regional Hospitals in Sierra Leone
Authors: Williams Ralph , Conteh Christiana , Kanu Joseph Sam 
Published: November 2020

Background: Antibiotic resistance (AMR) is a safety concern for patients in Sierra Leone. AMR can occur in communities and as well in the process of receiving treatments in healthcare settings, and it can pose a major threat to patient safety. Healthcare-associated infections and AMR result in longer duration of illness, longer treatment, higher mortality, increased costs, and increased burden to health facilities. Objective: The purpose of this study was to generate more reliable estimates of the risk factors for the prevalence of HAI and to investigate patterns of antibiotic prescriptions done. Methods: The survey was conducted in 6 regional hospitals in Sierra Leone (Kono, Kambia, BO, Makeni, Moyamba, and Kenema) from June 16 to July 10 2019. The survey targeted inpatients in the pediatric, maternity, medical, and surgical wards. A structured questionnaire adopted from the WHO PPS form was used to collect information from patient medical charts and care notes. Results: Data were collected from 156 patients, of whom 140 patients were on antibiotics, 100 were women, and 40 were men. Patients on 1 antibiotic regimen accounted for 8.6% (n = 12) and 91.4% (n = 128) on a regimen of 2 or 3 antibiotics. Only 5 patients (3.6%) were on oral antibiotics and 135 (96.4%) were on IV antibiotics. In the maternity ward, 28 of 40 patients (70%) had had a caesarian section and were on 2 or more antibiotics; 18 patients with caesarian sections (64.3%) developed complications and continued on an antibiotic regimen for >1 week. The remaining 12 patients (30%) in the maternity ward were admitted for anemia and hypertension (ie, preeclampsia), and these patients were on 1 antibiotic regimen for which they had no clinical indication. Conclusions: The survey results show that every patient admitted to the hospital was covered with antibiotics with or without indications; no laboratory investigations were performed before antibiotics were initiated. These findings further reveal a large number of patients who were exposed to intravenous cannulation, which predisposes catheter-associated bloodstream infections. The survey results justify the need for an antibiotic stewardship program to guide use of antibiotics.Funding: NoneDisclosures: NoneIf I am discussing specific healthcare products or services, I will use generic names to extent possible. If I need to use trade names, I will use trade names from several companies when available, and not just trade names from any single company.DisagreeChristiana Kallon
Measuring Empiric Antibiotic Spectrum Patterns Across Space and Time
Authors: Yarrington Michael , Moehring Rebekah , Anderson Deverick John , Wrenn Rebekah , Sarubbi Christina , Spivey Justin 
Published: November 2020

Background: Quantitative evaluation of antibiotic spectrum is an important, underutilized metric in measuring antibiotic use (AU) and may assist antimicrobial stewards in identifying targets and strategy for intervention. We evaluated the spectrum of initial antibiotic choices by hospital location, day of the week, and time of day to determine whether these factors may be associated with broad-spectrum antibiotic choices. Methods: We identified all admissions with antibiotic exposure in medical and surgical wards and critical care units in a tertiary academic medical center between July 1, 2014, and July 1, 2019. The antibiotic spectrum index (ASI), proposed by Gerber et al, is a numeric score based on the number of pathogens covered by a particular agent. We defined ASI for initial antibiotic choice as follows: ASI for each unique antibiotic administered within 24 hours of the first antibiotic administration was summed and assigned to the administration time of the first dose. We categorized time into 4 distinct categories: weekday days (Monday–Friday, 7 a.m.–7 p.m.), weekday nights, weekend days, and weekend nights. Weekend time began 7 p.m. Friday and ended 7 a.m. Monday. We constructed heatmaps stratified by hospital location. Mann-Whitney U tests were applied to evaluate differences in the distributions of ASI using weekday days as a reference. Results: Data included 90,455 unique antibiotic admissions with initial antibiotic starts in medical and surgical wards and critical care units. Patterns of ASI for initial antibiotic choice varied between unit locations and time (Figs. 1 and 2). Mean and median ASIs for initial antibiotic choices were higher for medical ward and medical ICUs than for surgical wards and surgical ICUs. Initial antibiotic choices had higher ASIs during overnight hours for all units except the surgical ICU. Notable differences in ASIs were identified between weekday and weekend prescribing for surgical units, whereas medical units demonstrated less extreme differences. Conclusion: We observed a “weekend effect” across hospital units; the most extreme occurred in surgical wards. This observation may be due to differences in patient volume and rounding patterns. For example, hospitalist and critical care units have 7-day schedules, whereas surgical wards are highly influenced by operating room schedules. Antimicrobial stewardship teams may use these data to identify strategies targeting the most opportune time and place to intervene on the spectrum of initial antibiotic choice.Funding: NoneDisclosures: None
Evaluating Facility Characteristics and Connectivity Metrics as Predictors of Clostridioides difficile Rates in Nursing Homes, Atlanta, GA
Authors: Sefton Samantha , Goodenough Dana , Saiyed Sahebi , Fridkin Scott 
Published: November 2020

Background: Nursing home (NH) residents are at high risk for Clostridioides difficile infection (CDI) due to older age, frequent antibiotic exposure, and previous healthcare exposure. Incidence of CDI attributed to NHs is not well established, but it is hypothesized to be related to the magnitude of transfers. We evaluated the relationship between NH CDI incidence and facility characteristics to explain variability in rates in Atlanta, Georgia. Methods: Incident C. difficile cases from 2016 to 2018 were identified through the Georgia Emerging Infections Program (funded by the Centers for Disease Control and Prevention), which conducts active population-based surveillance in the 8-county metro Atlanta area. An incident case was defined as an NH resident with a toxin-positive stool specimen (without a positive test within 8 weeks). Sampled (1 to 3 on age and gender) incident cases were attributed to a NH if a patient was an NH resident within 4 days of specimen collection. Facility characteristics (beds, resident days, admissions, and average length of stay [ALOS]) were obtained from NH cost reports, and facility-specific connectivity metrics were calculated (indegree and betweenness) from 2016 Medicare claims data. Case counts were aggregated to estimate yearly incidence and correlated with facility characteristics and location within the healthcare network using the Spearman correlation. A negative binomial model was used to assess residual variability in NH CDI incidence. Results: In total, 386 incident CDI cases were attributed to 64 NHs (range, 0–27). Approximately half (54.7%) resided in the NH at the time of specimen collection; however, 33.7% were in inpatient units (≤4 days of admission), and 10.9% were in an emergency room (ER). The frequency of NH CDI cases correlated strongly with admissions (r = .70; P < .01), inversely with ALOS (r = −0.53; P < .01), and moderately with resident days (r = .38; P < .01). After accounting for admissions, incidence (per 1,000 admissions) still varied (Fig. 1) (median 14; range, 0–34). The inverse association with ALOS decreased and incidence no longer correlated with the remaining facility characteristics or location within the healthcare transfer network (P > .05, all comparisons). However, there was residual correlation with connectivity metrics (indegree r = 0.26; P = .04). Conclusions: Our data suggest that attributing CDI to NHs requires the inclusion of hospital and ER-based specimen collection. NH CDI incidence appears highest among facilities with a low ALOS and a high number of admissions; incidence rates calculated per 1,000 admissions may best account for infection risk inherent early in a resident’s stay. Residual variability attributed to connectivity to the healthcare network was of borderline significance and should be further explored in the NH setting.Funding: NoneDisclosures: Scott Fridkin, consulting fee, vaccine industry (spouse)
Predicting Vancomycin-Resistant Enterococci (VRE) and Carbapenem-Resistant Organism (CRO) Colonization in the Intensive Care Unit
Authors: Çaǧlayan Çaǧlar , Levin Scott , Milstone Aaron Michael , Tamma Pranita , Simner Patricia , Goodman Katherine , Amoah Joe , Smith Aria , Toerper Matt , Barnes Sean , Klein Eili 
Published: November 2020

Background:
Rapidly identifying patients colonized with multidrug-resistant organisms (MDROs) upon ICU admission is critical to control and prevent the spread of these pathogens in healthcare facilities. Electronic health records (EHR) provide a rich source of data to predict the likelihood of MDRO colonization at admission, whereas surveillance methods are resource intensive and results are not immediately available. Our objectives were (1) to predict VRE and CRO colonization at ICU admission and (2) to identify patient subpopulations at higher risk for colonization with these MDROs. Methods: We conducted a retrospective analysis of patients aged ≥16 years admitted to any of 6 medical or surgical intensive care units (ICU) in the Johns Hopkins Hospital from July 1, 2016, through June 30, 2018. Perirectal swabs were collected at ICU unit admission and were tested for VRE and CRO. Patient demographic data, prior hospitalizations, and preadmission clinical data, including prior medication administration, prior diagnoses, and prior procedures, were extracted to develop prediction models. We employed the machine-learning algorithms logistic regression (LR), random forest (RF), and XGBoost (XG). The sum of sensitivity and specificity (ie, Youden’s index) was selected as the performance metric. Results: In total, 5,033 separate ICU visits from 3,385 patients were included, where 555 (11%) and 373 (7%) admissions tested positive for VRE and CRO, respectively. The sensitivity and specificity of our models for VRE were 78% and 80% with LR, 80% and 82% with RF, and 77% and 87% with XG. Predictions for CRO were not as precise, with LR at 73% and 53%, RF at 81% and 48%, and XG at 69% and 61%. The XG algorithm was the best-performing algorithm for both VRE and CRO. Prior VRE colonization, recent (<180 days) long-term care facility stay, and prior hospitalization >60 days were the key predictors for VRE, whereas the primary predictor for CRO colonization was prior carbapenem use. Conclusions: We demonstrated that EHR data can be used to predict >75% of VRE positive cases with a <15% false-positive rate and ~70% of CRO cases with a <40% false-positive rate. Future studies using larger sample sizes may improve the prediction accuracy and inform model generalizability across sites and thus reduce the risk of transmission of MDROs by rapidly identifying MDRO-colonized patients.Funding: This work was funded by the Centers for Disease Control and Prevention (CDC) Epicenters Program (Grant Number 1U54CK000447) and the CDC MInD-Healthcare Program (Grant Number 1U01CK000536).Disclosures: Aaron Milstone, BD (consulting)
Transmission of Listeriosis in a Neonatal Intensive Care Unit Supported by Whole-Genome Sequencing
Authors: Kim Janice , Rosen Hilary , Angel Kristen , Maroufi Azarnoush , Tweeten Samantha , Lui Jacqueline , Crandall John , Lanier Tracy , Siegel Jane , Kimura Akiko 
Published: November 2020

Background: Listeriosis is a rare but serious infectious disease caused by Listeria monocytogenes (LM) and predominantly transmitted through contaminated food. Moreover, 15% of listeriosis cases in the United States are pregnancy associated; nosocomial neonatal transmission in hospitals is extremely rare. In July 2018, the California Department of Public Health (CDPH) was notified of 4 patients, a mother–neonate pair and twin neonates, with listeriosis at the same hospital. The CDPH and San Diego County Health and Human Services Agency initiated an investigation to determine transmission and prevent additional infections. Methods: We reviewed medical records of the neonates and their mothers, interviewed the mothers with a detailed food exposure questionnaire, interviewed healthcare personnel (HCP), and performed an infection control assessment of the neonatal intensive care unit (NICU). CDPH performed whole-genome sequencing (WGS) on LM isolates that were then analyzed by whole-genome multilocus sequence typing (wgMLST) by the Centers for Diseases Control and Prevention (CDC) to assess relatedness in PulseNet, a public health laboratory database. The CDC also performed testing for LM on formalin-fixed placentas from the mother of the twins. Results: During a 1-week period, 4 patients with LM were identified at the hospital. A mother was admitted at 31 weeks gestation with acute abdominal and back pain that progressed with precipitous vaginal delivery and postpartum sepsis. Her neonate was resuscitated, transported to the NICU, underwent a sepsis evaluation, received antibiotics, and was transferred to another hospital within 6 hours. Maternal blood, placenta, and neonatal blood cultures grew LM. Twin neonates, born to an asymptomatic mother and present in the NICU during the index neonate’s stay, developed acute infection 4 and 6 days after the index neonate’s transfer; blood cultures confirmed LM. The LM isolates from the 4 patients were indistinguishable by wgMLST and were not related to other PulseNet isolates. LM was not detected in the twin placentas. There were no common food exposures between the mothers. At least 1 common HCP cared for all 3 neonates. Infection control lapses included lack of proper hand hygiene during the index neonate’s resuscitation and potentially after cleaning and disinfection of the neonate’s incubator. Conclusions: This report provides supportive evidence that nosocomial transmission of LM can occur during a brief NICU stay due to lapses in infection control practices. Strict adherence to standard precautions in the delivery room and NICU is imperative to prevent cross transmission.Disclosures: NoneFunding: None
National Reporting Trend for HO-MRSA Bacteremia LabID Events, 2010–2018
Authors: Jones Karen , Mu Yi , Li Qunna , Nkwata Allan , Soe Minn , Edwards Jonathan , Dudeck Margaret NA 
Published: November 2020

Background: The Centers for Disease Control and Prevention’s National Healthcare Safety Network (NHSN) has included surveillance of laboratory-identified (LabID) methicillin-resistant Staphylococcus aureus (MRSA) bacteremia events since 2009. In 2013, the Centers for Medicare & Medicaid Services (CMS) began requiring acute-care hospitals (ACHs) that participate in the CMS Inpatient Quality Reporting program to report MRSA LabID events to the NHSN and, in 2015, ACHs were required to report MRSA LabID events from emergency departments (EDs) and/or 24-hour observation locations. Prior studies observed a decline in hospital-onset MRSA (HO-MRSA) rates in national studies over shorter periods or other surveillance systems. In this analysis, we review the national reporting trend for HO-MRSA bacteremia LabID events, 2010–2018. Method: This analysis was limited to MRSA bacteremia LabID event data reported by ACHs that follow NHSN surveillance protocols. The data were restricted to events reported for overall inpatient facility-wide and, if applicable, EDs and 24-hour observation locations. MRSA events were classified as HO (collected >3 days after admission) or inpatient or outpatient community onset (CO, collected ≤3 days after admission). An interrupted time series random-effects generalized linear model was used to examine the relationship between HO-MRSA incidence rates (per 1,000 patient days) and time (year) while controlling for potential risk factors as fixed effects. The following potential risk factors were evaluated: facility’s annual survey data (facility type, medical affiliation, length of facility stay, number of beds, and number of intensive care unit beds) and quarterly summary data (inpatient and outpatient CO prevalence rates). Result: The number of reporting ACHs increased during this period, from 473 in 2010 to 3,651 in 2018. The crude HO-MRSA incidence rates (per 1,000 patient days) have declined over time, from a high of 0.067 in 2011 to 0.052 in 2018 (Table 1). Compared to 2014, the adjusted annual incidence rate increased in 2015 by 16.38%, (95% confidence interval [CI], 10.26%–22.84%; P < .0001). After controlling for all significant risk factors, the estimated annual HO-MRSA incidence rates declined by 5.98% (95% CI, 5.17%–6.78%; P < .0001) (Table 2). Conclusions: HO-MRSA bacteremia incidence rates have decreased over the past 9 years, despite a slight increase in 2015. This national trend analysis reviewed a longer period while analyzing potential risk factors. The decline in HO-MRSA incidence rates has been gradual; however, given the current trend, it is not likely to meet the Healthy People 2020 objectives. This analysis suggests the need for hospitals to continue and/or enhance HO-MRSA infection prevention efforts to reduce rates further.Funding: NoneDisclosures: None