bluuebunny/crossref_metadata_2025_split huggingface dataset:
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: NoneDisclosures: 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: NoneDisclosures: 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: NoneDisclosures: 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: NoneDisclosures: 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: NoneDisclosures: 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: NoneDisclosures: 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: NoneDisclosures: 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: NoneDisclosures: None