bluuebunny/crossref_metadata_2025_split huggingface dataset:
424. The Impact of COVID-19 Response on Infection Prevention Programs and Practices in Southeastern United States
Authors: Advani Sonali D , Advani Sonali D , Cromer Andrea , Wood Brittain A , Baker Esther , Crawford Kathryn L , Crane Linda , Roach Linda , Padgette Polly W , Ashley Elizabeth Dodds , Akinboyo Ibukunoluwa , Weber David J , Weber David J , Sickbert-Bennett Emily , Anderson Deverick J 
Published: December 2021

Abstract

Background
Early assessments of COVID19 preparedness reported resource shortages, use of crisis capacity strategies, variations in testing, personal protective equipment (PPE), and policies in US hospitals. One year later, we performed a follow-up survey to assess changes in infection prevention practice and policies in our diverse network of community and academic hospitals.

Methods
This was a cross-sectional electronic survey of infection preventionists in 58 hospitals within the Duke Infection Control Outreach Network (community) and Duke/UNC Health systems (academic) in April-May 2021 to follow-up our initial survey from April 2020. The follow-up survey included 26 questions related to resource availability, crisis capacity strategies, procedures, changes to PPE and testing, and staffing challenges.

Results
We received 54 responses (response rate, 93%). Facilities reported significantly fewer PPE and resource shortages in the follow-up survey compared to our initial survey (Figure 1, P&lt; 0.05). Only 32% of respondents were still reprocessing N95 respirators (compared to 73% in initial survey, P&lt; 0.05). All hospitals performed universal masking, universal symptom screening on entry, and 30% required eye protection. In 2020, most hospitals suspended elective surgical procedures in March-April, and restarted in May-June. Approximately 92% reported in-house testing for SARS-COV-2 by April 2020, at least a third of which had a weekly capacity of &gt;100 tests. Almost 80% performed universal pre-operative testing, while 61% performed universal preadmission testing for SARS-COV-2. Almost all hospitals switched from test-based to time-based strategy for discontinuing isolation precautions, majority in August-September 2020. Twenty-five percent hospitals reported infection prevention furloughs, staffing cuts, and or reassignments, while 81% reported increased use of agency nursing during the pandemic.

Conclusion
Our follow-up survey reveals improvement in resource availability, evolution of PPE guidance, increase in testing capacity, and burdensome staffing changes. Our serial surveys suggest increasing uniformity in infection prevention policies, but also highlight the increase in staff turnover and infection prevention staffing shortages.

Disclosures
Sonali D. Advani, MBBS, MPH, Nothing to disclose David J. Weber, MD, MPH, PDI (Consultant)
427. Healthcare Personnel Perceived Benefit of Infection Prevention Strategies during COVID
Authors: Sickbert-Bennett Emily , Schnell Natalie , Summerlin-Long Shelley , Brewer Brooke , DiBiase Lauren , Stancill Lisa , Teal Lisa , Weber David J , Weber David J 
Published: December 2021

Abstract

Background
During the COVID-19 pandemic, many infection prevention policy and practice changes were introduced to mitigate hospital transmission. Although each change had evidence-based infection prevention rationale, healthcare personnel (HCP) may have variable perceptions of their relative values.

Methods
Between October-December 2020, we conducted a voluntary, anonymous, IRB-approved survey of UNC Medical Center HCP regarding their views on personal protective equipment (PPE) and hospital policies designed to prevent COVID acquisition. The survey collected occupational and primary work location data (COVID unit or not) as well as their views on specific infection prevention practices during COVID. Chi squared tests (two tailed) were used to compare differences in the proportions.

Results
The overall results are displayed (Figure). Among the 694 HCP who responded to the survey, we found HCP were largely (68%) satisfied that the organization was taking all the necessary measures to protect them from COVID-19. A significantly greater proportion (14% more) of HCP (81.7% compared to 67.6%; 95% CI of difference 9.4-18.5%, P&lt; 0.0001) agreed that all PPE was available to them compared to those who were confident that the organization was taking necessary steps for protection, highlighting that safety is more than simply availability of supplies. More than 90% felt that daily screening of patients/visitors and patient/visitor mask requirements were important for protecting them from acquiring COVID in the workplace and that wearing a mask themselves was a key intervention for protecting others. Fewer HCP (72-80%), although still a majority, perceived that eye protection and daily symptom screening for HCP were beneficial. Symptom screening for patients/visitors was perceived by 19% more HCP (90.9% compared to 72.2%; 95% CI of difference 15-23%) to be beneficial than symptom screening of HCP (P&lt; 0.0001).
Figure. HCP Perceived Benefit of Infection Prevention Strategies during COVID

Conclusion
Although infection prevention strategies were implemented based on evidence and in alignment with CDC recommendations, it is important to acknowledge that the perception and acceptance of these recommendations varied among our HCP. Compliance can only be optimized with key interventions when we seek to understand the perceptions of our staff.

Disclosures
David J. Weber, MD, MPH, PDI (Consultant)
428. Assessing the Confidence, Knowledge and Preferences of Hospital Staff with Regards to Personal Protective Equipment (PPE) Practices During the COVID-19 Pandemic
Authors: Brown Rachel , Markman Sharon , Brown Amanda , Mian Rukhshan , Arora Vineet , Umscheid Craig 
Published: December 2021

Abstract

Background
Effective use of personal protective equipment (PPE) by hospital staff is critical to prevent transmission of COVID-19. This study examines hospital staff confidence in and knowledge of effective PPE use, and their preferences for learning about PPE practices.

Methods
Three isolation precautions signs were created for use in the care of those with or under investigation for COVID-19 infection: first, a special respiratory precautions sign designed by infection control; and next, two signs outlining proper donning and doffing practices – one created internally with the support of health literacy, and another developed with a design firm (IDEO) using principles of human-centered design (Figure 1). All signs were used for ≥ 10 weeks prior to distribution of a questionnaire (REDCap) to clinical and non-clinical hospital staff. Those who had not worked on hospital units during the pandemic (after March 15, 2020) were excluded. The 38-item survey was sent by supervisors over email between July 14-31, 2020, and examined demographics, confidence in and knowledge of PPE best practices, and preferences for each precaution sign with regards to trustworthiness, ease of following, informative content, and clarity of image/layout. Responses were reported using descriptive statistics. A non-parametric test of trends compared staff preferences across signs. Logistic regression examined the association between answering all knowledge-based questions correctly and staff role and confidence in PPE practices (Stata).

Results
Of the 531 respondents, 461 were eligible for inclusion. The majority were female, white, and not high risk for COVID-19 (Table 1). Most were confident about PPE use, correctly answered questions examining knowledge of PPE best practices, and found PPE signage helpful (Table 2). Staff preferred the professionally designed sign for informative content (p&lt; 0.01) and clear imagery/layout (p=0.01) (Table 3). Confidence in PPE practices and physician or nurse roles were associated with answering all knowledge-based questions correctly (p&lt; 0.001 and p=0.04, respectively).

Conclusion
In a convenience sample of hospital staff, most were confident and knowledgeable about PPE use, found PPE signage helpful, and preferred professionally designed signs.

Disclosures
All Authors: No reported disclosures
429. Infection Prevention During Use of a Warm Zone Model in Cohort Patient Care Locations during the COVID-19 Pandemic
Authors: Sacht Joseph , Holton Samantha , Yun Mylinh , Varkey Jay 
Published: December 2021

Abstract

Background
The COVID-19 pandemic required hospitals to care for influxes of patients in cohort locations during critical shortages of personal protective equipment (PPE). Safety zones can be used to protect healthcare workers caring for patients with infectious pathogens. During the COVID-19 pandemic, our hospital developed a Warm Zone model (WZM) to streamline the care of patients with COVID. We established specific areas in our COVID cohort units where staff were permitted to bridge between rooms without doffing gowns, but still doffing gloves and performing hand hygiene between patients. We recognized that a WZM could inadvertently increase risk of nosocomial transmission of pathogens if gowns acted as fomites. For this reason, patients with known infectious pathogens were excluded from the WZM. To measure for unintended harmful consequences of the WZM, our Infection Prevention (IP) department performed surveillance for hospital onset (HO) Clostridioides difficile (CDI), Carbapenem-resistant enterobacteriaceae (CRE) and Methicillin-resistant Staphyloccocus aureus (MRSA) bloodstream infections on units that implemented the WZM.

Methods
Two intensive care units and 3 wards where COVID positive patients were cohorted were included in surveillance. The timeframe for this analysis was 7/1/2020 - 3/31/2021. An electronic surveillance system was used to measure HO infections. The National Healthcare Surveillance Network (NHSN) LabID definitions were used when determining HO CDI and MRSA bloodstream infections (BSI).

Results
During the study period, there were no HO CRE, 1 HO CDI, and 2 HO MRSA BSI in cohort units. There was no evidence to suggest that the HO CDI or HO MRSA BSI were associated with use of a WZM. During this time period, there were 14 cases of community onset (CO) CDI, 2 cases of CO MRSA BSI, and one CO CRE.

Conclusion
During use of a WZM in COVID cohort units, IP did not identify significant increase in HO CDI, CRE, or MRSA BSI compared to non-cohort units. We were limited in our ability to measure acquisition of pathogens because active surveillance screening for colonization was not performed. However, we were able to safely employ a WZM to streamline patient care in COVID cohort areas without evidence of causing nosocomial infections via patient-to-patient transmission.

Disclosures
All Authors: No reported disclosures
431. SARS-CoV-2 Environmental Contamination in Hospitalized COVID-19 Patients’ Rooms
Authors: Warren Bobby G , Nelson Alicia , Barrett Aaron , Addison Bechtler S , Graves Amanda M , Lewis Sarah S , Smith Becky A , Weber David J , Weber David J , Sickbert-Bennett Emily , Anderson Deverick J 
Published: December 2021

Abstract

Background
The correlation between SARS-CoV-2 RNA and infectious viral contamination of the hospital environment is poorly understood.

Methods
housed in a dedicated COVID-19 unit at an academic medical center. Environmental samples were taken within 24 hours of the first positive SARS-CoV-2 test (day 1) and again on days 3, 6, 10 and 14. Patients were excluded if samples were not obtained on days 1 and 3. Surface samples were obtained with flocked swabs pre-moistened with viral transport media from seven locations inside (bedrail, sink, medical prep area, room computer, exit door handle) and outside the room (nursing station computer). RNA extractions and RT-PCR were completed on all samples. RT-PCR positive samples were used to inoculate Vero E6 cells for 7 days and monitored for cytopathic effect (CPE). If CPE was observed, RT-PCR was used to confirm the presence of SARS-CoV-2.

Results
We enrolled 14 patients (Table 1, Patient Characteristics) between October 2020 and May 2021. A total of 243 individual samples were obtained – 97 on day 1, 98 on day 3, 34 on day 6, and 14 on day 10. Overall, 18 (7.4%) samples were positive via RT-PCR – 9 from bedrails (12.9%), 4 from sinks (11.4%), 4 from room computers (11.4%) and 1 from the exit door handle (2.9%). Notably, all medical prep and nursing station computer samples were negative (Figure 1). Of the 18 positive samples, 5 were from day 1, 10 on day 3, 1 on day 6 and 2 on day 10. Only one sample, obtained from the bedrails of a symptomatic patient with diarrhea and a fever on day 3, was culture-positive (Figure 2).
Table 1. Patient Characteristics

Figure 1. Proportion of RT-PCR Positive Samples by Sample Day and Location

Figure 2. Cell cultures of negative control (left) and CPE positive sample (right)

Conclusion
Overall, the amount of environmental contamination of viable SARS-CoV-2 virus in rooms housing COVID-19 infected patients was low. As expected, more samples were considered contaminated via RT-PCR compared to cell culture, supporting the conclusion that the discovery of genetic material in the environment is not an indicator of contamination with live infectious virus. More studies including RT-PCR and viral cell culture assays are needed to determine the significance of discovering SARS-CoV-2 RNA versus infectious virus in the clinical environment.

Disclosures
David J. Weber, MD, MPH, PDI (Consultant)
435. Outcomes for E484K Mutation Negative COVID-19 Patients Cohorted with E484K Mutation Positive COVID-19 Patients: A Retrospective Cohort Study
Authors: Buskandar Fahad , Linkneheld-Struk Amber L , Williams Victoria R , Chan Adrienne , Mieusement Lorraine Maze Dit , Salt Natasha , Leis Jerome A 
Published: December 2021

Abstract

Background
The emergence of the E484K mutation of SARS-CoV-2 poses a risk of immune evasion but the risk of re-infection during acute infection is not well defined. Our aim was to assess the risk of re-infection among patients with existing acute E484K mutation negative COVID-19 infection who were exposed to an E484K mutation positive SARS-CoV-2 infected patient.

Methods
We performed a retrospective cohort study of patients admitted with acute E484K negative COVID-19 infection and shared a hospital room with a patient who was E484K mutation positive during their period of communicability. The primary outcome was laboratory confirmed and/or clinical evidence of re-infection within the E484K negative population within 30 days of exposure and the secondary outcome was the 30-day risk of death or re-admission to hospital due to COVID-19.

Results
We identified 41 patients who were E484K mutation negative who shared a hospital room with some of the identified 34 E484K positive patients. Six (14%) underwent repeat COVID-19 testing and remained E484K negative and none developed signs or symptoms of COVID-19 re-infection during the 30 days following exposure. The mortality rate was 7% (3/41) and re-admission rate was zero at 30 days from exposure.

Conclusion
Despite the small sample size, we did not observe any evidence of re-infection among patients with COVID-19 who shared a hospital room with E484K positive patients during their acute infection. If necessary due to high hospital occupancy, patients with discordant E484K results can be safely cohorted in a shared room.

Disclosures
All Authors: No reported disclosures
436. U-shaped-aggressiveness of SARS-CoV-2: Period Between Initial Symptoms and Clinical Progression to COVID-19 Suspicion. A Population-Based Cohort Study
Authors: Buitano Bruno G , Morgenstern Dan , Talavera Juan , Zaldívar Andrea , Martínez Mercedes 
Published: December 2021

Abstract

Background
Until now, studies have been focused on patient-centered risk factors, while SARS-CoV-2 aggressiveness has been established as causing 20% of severe and critical patients. However, there are still many unanswered questions concerning the clinical aggressiveness behavior of SARS-CoV-2. This study focuses on progression of symptoms as a marker of such aggressiveness, using the Period between initial symptoms and clinical progression to COVID-19 suspicion (PISYCS) to determine the risk of severe disease and mortality.

Methods
Historic cohort study of Mexican patients. Data from January-April 2020 were provided by the Health Ministry. Setting: Population-based. Patients registered in the Epidemiologic Surveillance System in Mexico. Participants were subjects who sought medical attention for clinical suspicion of COVID-19. All patients were subjected to RT-PCR testing for SARS-CoV-2. We measured the Period between initial symptoms and clinical progression to COVID-19 suspicion (PISYCS) and compared it to the primary outcomes (mortality and pneumonia)

Results
65,500 patients were included. Reported fatalities and pneumonia were 2176 (3.32%), and 11568 (17.66%), respectively. According to the PISYCS, patients were distributed as follows: 14.89% in &lt; 24 hours, 43.25% between 1–3 days, 31.87% between 4–7 days and 9.97% &gt; 7 days. The distribution for mortality and pneumonia was 5.2% and 22.5% in &lt; 24 hours, 2.5% and 14% between 1–3 days, 3.6% and 19.5% between 4–7 days, 4.1% and 20.6% &gt; 7 days, respectively (p&lt; 0.001). Adjusted-risk of mortality was (OR [95% CI], p-value): &lt; 24 hours = 1.75 [1.55–1.98], p&lt; 0.001; 1–3 days = 1 (reference value); 4–7 days = 1.53 [1.37–1.70], p&lt; 0.001; &gt; 7 days = 1.67 [1.44–1.94], p&lt; 0.001. For pneumonia: &lt; 24 hours = 1.49 [1.39–1.58], p&lt; 0.001; 1–3 days = 1; 4–7 days = 1.48 [1.41–1.56], p&lt; 0.001; &gt; 7 days = 1.57 [1.46–1.69], p&lt; 0.001.
Risk of Mortality vs. PISYCS

Logistic regression anlaysis of mortality based on PISYCS. Note that risk of mortality is significantly higher when PISYCS is &gt; 24 hours and &lt; 7 days
Risk of Pneumonia vs. PISYCS

Logistic regression anlaysis of developing pneumonia based on PISYCS. Note that risk of pneumonia is significantly higher when PISYCS is &gt; 24 hours and &lt; 7 days.

Conclusion
The PISYCS shows a U-shaped SARS-CoV-2 aggressiveness pattern. Further studies are needed to corroborate the time-related pathophysiology behind these findings and possibly justify use of PISYCS as an initial evaluation tool and therapies/monitoring in high-risk patients.

Disclosures
All Authors: No reported disclosures
437. Longitudinal Plasma Cytokine Profiles Differentiating COVID-19 Severity Groups
Authors: Green Amanda M , Souquette Aisha , Agrawal Mona , Wolf Joshua , Wolf Joshua , Gaur Aditya , Allison Kim J , Estepp Jeremie , Allen Emma , Thomas Paul , Smallwood Heather 
Published: December 2021

Abstract

Background
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19), an infection with widely varying clinical severity. Severe COVID-19 was initially proposed to be secondary to cytokine storm syndrome (CSS). However, studies since showed that patients with severe COVID-19 rarely display CSS cytokine phenotypes, and may have more limited inflammatory responses instead.

Methods
Prospective cohorts, aged 0-90 years of age who tested positive by polymerase chain reaction (PCR) for SARS-CoV-2 were enrolled from inpatient hospitals and outpatient testing centers in Memphis, TN from May 2020-January 2021. Longitudinal blood samples were obtained including acute, sub-acute and convalescent timepoints. Severity scores of asymptomatic, mild, moderate, and severe COVID-19 were assigned at time of convalescent assessment. Plasma was analyzed with a quantitative human magnetic 38-plex cytokine assay.

Results
: 169 participants were enrolled, including 8 asymptomatic, 117 mild, 22 moderate and 17 severe cases, and 5 children with post-COVID-19 multisystem inflammatory syndrome in children (MIS-C). All moderate and severe patients were hospitalized and received treatment (39%). Clear distinctions were seen between asymptomatic-mild cases and moderate-severe cases at acute timepoints and during disease progression for GCSF, IL-8, IL-10, IL-15, IL-1Ra, IP-10, MIP-1a, MIP-1β, and TGFα. There was a significant difference between participants who did and did not require hospitalization for acute timepoint levels of IL-10, IL-15, MIP-1 β and TGFα (p&lt; 0.01). Only 4 participants with active COVID-19 were found to meet criteria for CSS (2%), only 3 of which were severe. MIS-C participants showed nearly universally elevated cytokine levels compared to those with active COVID-19.
Temporal and severity associations of IL-10 and IP-10

Figure 1. Temporal and severity associations of IL-10 and IP-10 Examples of differentiating cytokine profiles by severity and time. Among SARS-CoV-2 PCR positive participants, IL-10 and IP-10 displayed increased levels in their acute plasma samples as clinical severity increased [A,C]. IL-10 and IP-10 also showed distinct time-dependent responses (ln(Cytokine level (pg/mL)) that differentiated the more severe from the less severe groups [B,D].

Conclusion
Moderate and severe acute COVID-19 has a distinct cytokine profile from asymptomatic and mild cases, as detected from acute, subacute and convalescent plasma.

Disclosures
Joshua Wolf, MBBS, PhD, FRACP, Karius Inc. (Research Grant or Support) Joshua Wolf, MBBS, PhD, FRACP, Nothing to disclose Paul Thomas, PhD, Cytoagents (Consultant)Immunoscape (Consultant)
440. Detection of COVID-19 Patients Requiring Escalation to ICU Status Using a Naïve Bayes Classifier
Authors: Barnett William R , Jaenke Chad , Holtzapple Zachary , Williams James , Kesireddy Nithin , Khokher Waleed , Assaly Ragheb 
Published: December 2021

Abstract

Background
A naïve Bayes classifier is a popular tool used in assigning variables an equal and independent contribution to a binary decision. With respect to COVID-19 severity, the naïve Bayes classifier can consider different variables, such as age, gender, race/ethnicity, comorbidities, and initial laboratory values to determine the probability a patient may need to be admitted or transferred to an intensive care unit (ICU). The aim of this study was to develop a screening tool to detect COVID-19 patients that may require escalation to ICU status.

Methods
Patients hospitalized with COVID-19 were gathered from the end of March 2020 to the end of May 2020 from four hospitals in our metropolitan area. We began searching for potential variables to include in the classification model using chi-square analysis or calculating the optimal cutpoint to separate ICU and non-ICU status. After identifying significant variables, we began using standard procedures to construct a classifier. The dataset was split 7:3 to create samples for training and testing. To appraise the model’s performance, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), area under the curve (AUC), and the Matthew’s correlation coefficient (MCC) were calculated.
Table 1. Univariate analysis of variables in the COVID-19 dataset dichotomized by ICU status

Results
A total of 574 COVID-19 patients were included in the study. There were 402 patients in the training sample and 172 patients in the testing sample. The naïve Bayes classifier demonstrated an overall accuracy result of 75.6% (95% CI; 68.5% – 81.8%) using the 14 variables listed in Table 1. The model was able to correctly classify 84.9% of ICU status patients (sensitivity), but only 54.7% of non-ICU status patients (specificity). The PPV and the NPV were 80.1% and 61.7%, respectively. The AUC was 0.717 (95% CI; 0.629 – 0.805) and the MCC was 0.410.

Conclusion
Our naïve Bayes classifier operates by recognizing certain aspects of severe COVID-19 cases and looking for the probability of the variables in said patients. We present a classification model that potentially could be used alongside other tools to screen patients with COVID-19 early in their hospital course to identify those needing escalation to ICU level care.

Disclosures
All Authors: No reported disclosures