bluuebunny/crossref_metadata_2025_split huggingface dataset:
Surveillance of Candidemia in Connecticut: An Epidemiological Comparison Between Two Periods
Authors: Gleason-Vergados Johanna , Banach David , Clogher Paula , Meek James 
Published: November 2021

Background: Candidemia is the fourth most common bloodstream infection in hospitalized patients in the United States, with an attributable mortality rate between 30% and 50%. Understanding the epidemiology of candidemia is critical due to its prevalence and association with extended hospital stays, high treatment cost, and significant morbidity and mortality. In 2019 the Connecticut Department of Public Health deemed candidemia a mandatory reportable condition and began state-wide surveillance in conjunction with the CDC’s Emerging Infections Program (EIP). Previously, the EIP had conducted population-based statewide surveillance of candidemia in Connecticut from 1998 to 2000, allowing an opportunity to assess how the epidemiology of candidemia has evolved. The goal of this study is to compare state-wide Connecticut EIP candidemia data from 2 periods (1998–2000 and 2019) to identify trends in infections and incidence, providing insight for potential improvements to current prevention measures and treatments. Methods: The sample population included all Connecticut residents aged ≥20 who tested positive for a candidemia infection during 1998–2000 and 2019. Patients who had positive blood cultures for Candida spp but were < 20 years old or were not Connecticut residents were excluded. Connecticut EIP candidemia case report forms from each time period were compared and matching fields were chosen as variables for univariate analysis to search for statistically significant differences. Selected variables include: Candida species present in blood culture, patient demographics, previous exposures to healthcare settings, length of stay, presence of central venous catheter (CVC), and location of the patient at diagnosis (community vs. hospital onset). De-identified patient-level information was provided by the EIP. Results: In total, 381 candidemia episodes from 1998–2000 were compared to 247 episodes in 2019. The proportion of C. albicans species in 1998–2000 was 49.9% and declined to 40.5% of cases in 2019 (P = .02). Outcomes improved as well, with 65.2% of patients in 2019 having survived compared to 51.4% in 1998–2000 (P = .001). Other findings indicate that patients with candidemia in 2019 were less likely to have a central venous catheter, less likely to have undergone a recent surgery, and were more likely to have community-onset infection (all p < 0.05). Conclusions: The epidemiology of candidemia has changed over the past 20 years, with significant improvements in patient survival and a shift toward community-onset infections and non-Albicans spp. These findings have important implications in designing prevention strategies and optimizing candidemia management, particularly in the community setting where increased intravenous drug use and the availability of home healthcare may be important factors.Funding: NoDisclosures: None
Molecular Epidemiology of Large COVID-19 Clusters at an Academic Medical Center, March–October 2020
Authors: Kobayashi Takaaki , Ortiz Miguel , Holley Stephanie , Etienne William , Jenn Kyle , Abosi Oluchi , Meacham Holly , Sheeler Lorinda , Dains Angie , Kukla Mary , Trannel Alexandra , Marra Alexandre , Alsuhaibani Mohammed , McCray Paul , Perlman Stanley , Ford Bradley , Diekema Daniel , Wellington Melanie , Pezzulo Alejandro , Salinas Jorge 
Published: November 2021

Background: COVID-19 in hospitalized patients may be the result of community acquisition or in-hospital transmission. Molecular epidemiology can help confirm hospital COVID-19 transmission and outbreaks. We describe large COVID-19 clusters identified in our hospital and apply molecular epidemiology to confirm outbreaks. Methods: The University of Iowa Hospitals and Clinics is an 811-bed academic medical center. We identified large clusters involving patients with hospital onset COVID-19 detected during March–October 2020. Large clusters included ≥10 individuals (patients, visitors, or HCWs) with a laboratory confirmed COVID-19 diagnosis (RT-PCR) and an epidemiologic link. Epidemiologic links were defined as hospitalization, work, or visiting in the same unit during the incubation or infectious period for the index case. Hospital onset was defined as a COVID-19 diagnosis ≥14 days from admission date. Admission screening has been conducted since May 2020 and serial testing (every 5 days) since July 2020. Nasopharyngeal swab specimens were retrieved for viral whole-genome sequencing (WGS). Cluster patients with a pairwise difference in ≤5 mutations were considered part of an outbreak. WGS was performed using Oxford Nanopore Technology and protocols from the ARTIC network. Results: We identified 2 large clusters involving patients with hospital-onset COVID-19. Cluster 1: 2 hospital-onset cases were identified in a medical-surgical unit in June 2020. Source and contact tracing revealed 4 additional patients, 1 visitor, and 13 employees with COVID-19. Median age for patients was 62 (range, 38–79), and all were male. In total, 17 samples (6 patients, 1 visitor, and 10 HCWs) were available for WGS. Cluster 2: A hospital-onset case was identified via serial testing in a non–COVID-19 intensive care unit in September 2020. Source investigation, contact tracing, and serial testing revealed 3 additional patients, and 8 HCWs. One HCW also had a community exposure. Patient median age was 60 years (range, 48–68) and all were male. In total, 11 samples (4 patients and 7 HCWs) were sequenced. Using WGS, cluster 1 was confirmed to be an outbreak: WGS showed 0–5 mutations in between samples. Cluster 2 was also an outbreak: WGS showed less diversity (0–3 mutations) and ruled out the HCW with a community exposure (20 mutations of difference). Conclusion: Whole-genome sequencing confirmed the outbreaks identified using classic epidemiologic methods. Serial testing allowed for early outbreak detection. Early outbreak detection and implementation of control measures may decrease outbreak size and genetic diversity.Funding: NoDisclosures: None
Coronavirus in Wisconsin Nursing Homes: A Longitudinal Analysis of the First 10 Months of the Pandemic
Authors: Gmehlin Cameron , Rivera Frida , Ramos-Castaneda Jorge , Pezzin Liliana , Duthie EDmund , Munoz-Price L. Silvia 
Published: November 2021

Background: The COVID-19 pandemic has disproportionately affected nursing home residents, and emerging evidence suggests quality, location, resident demographics, and staffing levels may be related to COVID-19 incidence within facilities. We describe the distribution of COVID-19 cases in Wisconsin nursing homes from January 2020 to October 2020, the effect of rural versus urban locations on COVID-19 incidence, and the temporal changes in COVID-19 incidence. Methods: We constructed a database using the Center for Medicaid and Medicare Services’ (CMS) publicly available data. Variables obtained per facility included location, number of beds, ownership type, average census, 5-star ratings (overall, quality, health, staffing, and nurse staffing categories), number of COVID-19 cases, resident Medicaid/Medicare share, area deprivation index, and social vulnerability index. Nursing homes were divided into tertiles based on total COVID-19 cases for descriptive analysis (zero cases, 1–7 cases, >7 cases). Demographic and clinical variables were reported as frequencies, mean (standard deviation) or median (interquartile range). We compared groups using the Pearson χ2 test and the Kruskal-Wallis test. COVID-19 incidence rates were calculated by dividing the number of COVID-19 cases by monthly occupied bed days and multiplied by 10,000. Results: From January 1, 2020, to November 1, 2020, in total, 3,133 SARS-CoV-2–confirmed cases were reported among 248 (70.5%) nursing homes. Urban location (P = .027), overall 5-star rating (P = .035), number of beds (p < 0.001), and average count of residents per day (p < 0.001) were associated with a greater number of COVID-19 cases. Temporal analysis showed that the highest incidence rates of COVID-19 in NHs were observed from January to May and in October 2020 (11.36 and 30.33 cases per 10,000 occupied-bed days, respectively). Urban NHs experienced higher incidence rates until September, then incidence rates among rural facilities surged (Fig.1A). In the first half of the year, NHs with lower quality scores (1-3 stars) had a higher COVID-19 incidence rate; however, in August this trend reversed, and facilities with higher quality scores (4-5 stars) showed the highest incidence rates (Fig.1B). Fig. 2 shows a temporal depiction of the shift from urban to rural settings. Conclusions: Higher COVID-19 incidence rates during the first 5 months of the pandemic were observed in urban, larger facilities with lower 5-star rating. By the end of the year, nursing homes in rural areas and those with higher quality ratings had the highest incidence rates.Funding: NoDisclosures: None
Evaluating the Relationship between Cycle Threshold Values and Reported COVID-19 Symptoms among Healthcare Workers
Authors: Sampson Mindy , Passaretti Catherine , Priem Jennifer , Kester Shelley , Fischer Kristin , Longshore John 
Published: November 2021

Background: SARS-CoV-2 detected by reverse transcription polymerase chain reaction (RT-PCR) can persist for weeks to months in some individuals. Cycle threshold (Ct) values represent the number of cycles needed to amplify viral ribonucleic acid (RNA) to reach a detectable level. As such, Ct values are inversely related to the amount of virus in a sample. As knowledge of SARS-CoV-2 viral dynamics continues to evolve, understanding the relationship between Ct values, type of symptoms, and timing of symptom onset can help determine when infected individuals are most likely to be infectious. Methods: We conducted a retrospective cohort study of 1,027 healthcare workers (HCWs) who tested positive for SARS-CoV-2 by RT-PCR from nasopharyngeal specimens between June 27, 2020, and September 21, 2020. All HCWs were interviewed within 72 hours of their diagnosis for symptom history. Due to multiple PCR platforms being in use in our facility, only 360 HCWs (35%) had Ct values available for analysis. Multivariate linear regression models examined the effect of COVID-19–related symptoms and timing of symptom onset to test on Ct values. Results: The most frequently reported symptoms were congestion (55.6%), cough (50.3%), and headache (46.7%). Other symptoms less commonly reported were fatigue (36.7%), loss of taste or smell (36.4%), fever (35.4%), muscle aches (33.3%), sore throat (27.4%), and diarrhea (26.7%). Symptomatic HCWs (88.3% of sample) had lower Ct values (ORF-1 M = 22.66, SD = 5.17; E-Gene M = 24.34, SD = 6.60) than asymptomatic individuals (ORF-1 M = 25.46, SD = 6.06; E-Gene M = 29.34, SD = 7.96). Of all symptoms measured, only presence of fever, congestion, and muscle aches predicted significantly lower Ct values. Mean Ct values decreased 2 days prior to symptom onset, were lowest the day of symptom onset, then increased in a curvilinear fashion. There were no significant 2-way interactions between symptoms and time of symptom onset to testing. Conclusions: The curvilinear pattern of Ct values over time from symptom onset are consistent with disease progression patterns and support current understanding of infectivity being highest 2 days prior to symptom onset through day 8. Presence of fever, congestion, and muscle aches are significantly correlated with lower Ct values, suggesting that these symptoms are associated with higher viral load. Although Ct values are not without limitations, our findings support the current understanding that presymptomatic and symptomatic individuals, particularly those with fever, congestion, and muscle aches, may pose higher risk of transmission to others.Funding: NoDisclosures: None
COVID-19 Conversion after Exposure in a Semiprivate Room at a Tertiary Care Center in Iowa, July–December 2020
Authors: Trannel Alexandra , Kobayashi Takaaki , Abosi Oluchi , Jenn Kyle , Meacham Holly , Sheeler Lorinda , Etienne William , Dains Angie , Kukla Mary , Alsuhaibani Mohammed , Holley Stephanie , Marra Alexandre , Ford Bradley , Wellington Melanie , Diekema Daniel , Salinas Jorge 
Published: November 2021

Background: Hospital semiprivate rooms may lead to coronavirus disease 2019 (COVID-19) patient exposures. We investigated the risk of COVID-19 patient-to-patient exposure in semiprivate rooms and the subsequent risk of acquiring COVID-19. Methods: The University of Iowa Hospitals & Clinics is an 811-bed tertiary care center. Overall, 16% of patient days are spent in semiprivate rooms. Most patients do not wear masks while in semiprivate rooms. Active COVID-19 surveillance included admission and every 5 days nasopharyngeal SARS-CoV-2 polymerase chain reaction (PCR) testing. We identified inpatients with COVID-19 who were in semiprivate rooms during their infectious periods during July–December 2020. Testing was repeated 24 hours after the first positive test. Cycle threshold (Ct) values of the two tests (average Ct <30), SARS-CoV-2 serology results, clinical assessment, and COVID-19 history were used to determine patient infectiousness. Roommates were considered exposed if in the same semiprivate room with an infectious patient. Exposed patients were notified, quarantined (private room), and follow-up testing was arranged (median seven days). Conversion was defined as having a negative test followed by a subsequent positive within 14 days after exposure. We calculated the risk of exposure: number of infectious patients in semiprivate rooms/number of semiprivate patient-days (hospitalization days in semiprivate rooms). Results: There were 16,427 semiprivate patient days during July–December 2020. We identified 43 COVID-19 inpatients who roommates during their infectious periods. Most infectious patients (77%) were male; the median age was 67 years; and 22 (51%) were symptomatic. Most were detected during active surveillance: admission testing (51%) and serial testing (28%). There were 57 exposed roommates. The risk of exposure was 3 of 1,000 semiprivate patient days. In total, 16 roommates (28%) did not complete follow-up testing. Of 41 exposed patients with follow-up data, 8 (20%) converted following their exposure. Median time to conversion was 5 days. The risk of exposure and subsequent conversion was 0.7 of 1,000 semiprivate patient days. Median Ct value of the source patient was 20 for those who converted and 23 for those who did not convert. Median exposure time was 45 hours (range, 3–73) for those who converted and 12 hours (range, 1–75) for those who did not convert. Conclusions: The overall risk of exposure in semiprivate rooms was low. The conversion rate was comparable to that reported for household exposures. Lower Ct values and lengthier exposures may be associated with conversion. Active COVID-19 surveillance helps early detection and decreases exposure time.Funding: NoDisclosures: None
Outbreak of Pseudomonas aeruginosa Bacteremia Infections among Stem-Cell Transplant Patients Related to Change in Prophylaxis
Authors: VerLee Kerrie , Nguyen Chau , Lampen Russell , Codman Jim , Peters Tunisia 
Published: November 2021

Background:Pseudomonas aeruginosa outbreaks can originate from various sources and can cause severe complications in posttransplant patients. Antibiotic prophylaxis can decrease posttransplant infections; however, consideration must be given to P. aeruginosa coverage as we outline an outbreak among the stem-cell transplant (SCT) population. Methods: A multidisciplinary outbreak investigation was conducted to evaluate sources of contamination and changes in clinical processes. Positive blood cultures from SCT patients and environmental isolates were analyzed using whole-genome sequencing (WGS). Incidence density rates for P. aeruginosa blood cultures from January 2019 through October 2020 were calculated per 10,000 patient days and stratified by unit, specimen, and transplant type. Statistical analysis was calculated with significance at p < 0.05. Results: A cluster of 8 SCT patients was identified between May and September 2020. Moreover, 10 environmental samples were positive for P. aeruginosa including drains, water sources prior to the point-of-use (POU) filter and blood-bank thaw machines. Phylogenetic analysis revealed 1 cluster of 2 patients who shared the same room, 5 patients with unique P. aeruginosa isolates, and 2 separate clusters of environmental isolates with relatedness only to each other. Review of clinical processes showed a change from fluoroquinolone prophylaxis to cephalosporin in the spring of 2020. Also, 5 P. aeruginosa bacteremia infections occurred prior to June (11.78 cases per 10,000 patient days). During the period of cephalosporin use, 8 infections were identified (58.27 cases per 10,000 patient days) (P = .006). Following the restart of fluoroquinolone, zero infections have occurred to date, as of January 28, 2021. Conclusions: Discontinuation of fluoroquinolone prophylaxis was associated with P. aeruginosa bacteremia infections in SCT patients. Use of fluoroquinolone prophylaxis in SCT patients is protective from P. aeruginosa bacteremia infections. There have been no further infections in the following 3 months after the change back to the use of fluoroquinolone. Additionally, WGS showed that most patient isolates did not have a common source, suggesting that P. aeruginosa gastrointestinal colonization may play a role in seeding these bacteremia infections.Funding: NoDisclosures: None
Results of a Multicenter Diagnostic Stewardship Collaborative to Optimize Blood Culture Use in Critically Ill Children
Authors: Koontz Danielle , Woods-Hill Charlotte , Voskertchian Annie , Xie Anping , Miller Marlene , Fackler James , Colantuoni Elizabeth , Milstone Aaron , Li Ximin 
Published: November 2021

Group Name: Bright STAR Authorship GroupBackground: Blood cultures are fundamental in the diagnosis and treatment of sepsis. Culture practices vary widely, and overuse can lead to false-positive results and unnecessary antibiotics. Our objective was to describe the implementation of a multisite quality improvement collaborative to reduce unnecessary blood cultures in pediatric intensive care unit (PICU) patients, and its 18-month impact on blood culture rates and safety metrics. Methods: In 2018, 14 PICUs joined the Blood Culture Improvement Guidelines and Diagnostic Stewardship for Antibiotic Reduction in Critically Ill Children (Bright STAR) Collaborative, designed to understand and improve blood culture practices in critically ill children. Guided by a centralized multidisciplinary study team, sites first reviewed existing evidence for safe reduction of unnecessary blood cultures and assessed local practices and barriers to change. Subsequently, local champions developed and implemented clinical decision-support tools informed by local patient needs to guide new blood-culture practices. The coordinating study team facilitated regular evaluations and discussions of project progress through monthly phone calls, site visits if requested by sites or the study team, and collaborative-wide teleconferences. The study team collected monthly blood culture rates and monitored for possible delays in obtaining blood cultures using a standardized review process as a safety balancing metric. We compared 24 months of baseline data to 18 months of postimplementation using a Poisson regression model accounting for the site-specific patient days and correlation of culture use within a site over time. Results: Across the 14 sites, before implementation, 41,768 blood cultures were collected over 259,701 PICU patient days. The mean preimplementation site-specific blood culture rate was 15.7 cultures per 100 patient days (rate range, 9.6–48.2 cultures per 100 patient days). After implementation, 22,397 blood cultures were collected over 208,171 PICU patient days. The mean postimplementation rate was 10.4 cultures per 100 patient days (rate range, 4.7–28.3 cultures per 100 patient days), which was 33.6% lower than the preimplementation (relative rate 0.66; 95% CI, 0.65–0.68 p <0.01). In 18 months post-implementation, sites reviewed 793 positive blood cultures, and identified only one suspected delay in culture collection possibly attributable to the site’s blood culture reduction program. Conclusions: Multidisciplinary quality improvement teams safely facilitated a 33.6% average reduction in blood culture use in critically ill children at 14 hospitals. Future collaborative work will determine the impact of blood culture diagnostic stewardship on antibiotic use and other important patient safety outcomes.Funding: NoDisclosures: None
Analysis of Recurrent Urinary Tract Infection Management in Outpatient Settings Reveals Opportunities for Antibiotic Stewards
Authors: Valentine-King Marissa , Trautner Barbara , Zoorob Roger , Germanos George , Salemi Jason , Gupta Kalpana , Grigoryan Larissa 
Published: November 2021

Background: Studies of antibiotic prescribing choice and duration have typically excluded women with recurrent UTI (rUTI), yet the Infectious Disease Society of America (IDSA) UTI treatment guidelines are applicable to recurrent and sporadic cystitis. We sought to better understand prescribing practices among uncomplicated rUTI patients in terms of choice of drug, duration of therapy, and the risk factors for receiving guideline-discordant therapy. Methods: We performed a retrospective database study by extracting electronic health record data from adults seen at academic primary care, internal medicine, or urology practices between November 2016 and December 2018. Inclusion criteria included having ≥2 or ≥3 International Classification of Diseases Tenth Edition (ICD-10) cystitis codes recorded within a 6- or 12-month period, respectively. We excluded patients with ICD-10 codes indicating any structural or functional genitourinary comorbidities, interstitial cystitis, vaginosis, compromised immune systems, or pregnancy in the prior year. Patients were also excluded if they had signs or symptoms of pyelonephritis at presentation. Results: Overall, 232 patients presented for 597 outpatient visits. Most were married (52.2%), non-Hispanic white (62.9%), and female (92.2%), with a median age of 58 years (IQR, 41–68). Only 21% of visits with an antibiotic prescribed for treatment consisted of a first-line therapy agent prescribed for the recommended duration. In terms of antibiotic choice, these agents were prescribed in 58.4% of scenarios, which primarily included nitrofurantoin (37.8%) and trimethoprim-sulfamethoxazole (TMP-SMX) (20.3%). Guideline-discordant choices of fluoroquinolones (28.8%), and β-lactams (11.2%) were the second and third most commonly prescribed drug categories, respectively. Multinomial logistic regression identified age (OR, 1.02; 95% CI, 1.002–1.04) or having a telephone visit (OR, 3.17; 95% CI, 1.54–6.52) as independent risk factors for receiving a β-lactam. The duration exceeded the 3-day guideline recommendation in 87.6% of fluoroquinolones and 73% of TMP-SMX (73%) prescriptions, and 61% of nitrofurantoin prescriptions exceeded the recommended 5-day duration. Multiple logistic regression analysis revealed that seeking care at a urology clinic (OR, 2.81; 95% CI, 1.59–5.17) served as an independent factor for therapy duration exceeding guideline recommendations. Conclusions: This retrospective study revealed shortcomings in prescribing practices in the type and duration of therapy for rUTI. rUTI as well as sporadic UTI are important targets for outpatient antibiotic stewardship interventions.Funding: This investigator-initiated research study was funded by Rebiotix Inc, a Ferring Company.Disclosures: None