bluuebunny/crossref_metadata_2025_split huggingface dataset:
395. Early Predictors of Intensive Care Unit Admission among COVID-19 Patients in Qatar
Authors: Abuyousef Safae , Alnaimi Shaikha , Omar Nabil E , Elajez Reem , Elmekaty Eman Zeyad Ibrahim , Abdelfattah-Arafa Eiman , Barazi Raja , Ghasoub Rola , Rahhal Ala , Hamou Fatima , Al-Amri Maha K , Karawia Ahmed , Ajaj Fatima , Alkhawaja Raja , Kardousha Ahmed , Awaisu Ahmed , Abou-Ali Adel , Khatib Mohamad , AbouKamar Mohamed , Al-Hail Moza 
Published: December 2021

Abstract

Background
Coronavirus disease (COVID-19) is associated with significant morbidity and mortality. This study aimed to explore the early predictors of intensive care unit (ICU) admission and in-hospital mortality among patients diagnosed with COVID-19.

Methods
This was a case-control study of adult patients with confirmed COVID-19. Cases were defined as patients admitted to ICU during the period February 29 - May 29, 2020. For each case enrolled, one control was matched by age and gender.

Results
A total of 1560 patients with confirmed COVID-19 were included. Each group included 780 patients with a predominant male gender (89.7%) and a median age of 49 years (interquartile range = 18). Predictors independently associated with ICU admission were cardiovascular disease (CVD) (adjusted odds ratio (aOR)=1.64, 95% confidence interval (CI): 1.16 - 2.32, p=0.005), diabetes (aOR=1.52, 95% CI: 1.08 - 2.13, p= 0.016), obesity (aOR=1.46, 95% CI: 1.03-2.08, p= 0.034), lymphopenia (aOR=2.69, 95% CI: 1.80-4.02, p&lt; 0.001), high aspartate aminotransferase (AST) (aOR= 2.59, 95% CI: 1.53-4.36, p&lt; 0.001), high ferritin (aOR=1.96, 95% CI: 1.40-2.74, p&lt; 0.001), high C-reactive protein (CRP) (aOR=4.09, 95% CI: 2.81-5.96, p&lt; 0.001), and dyspnea (aOR=2.50, 95% CI: 1.77-3.54, p&lt; 0.001). Similarly, significant predictors of mortality included CVD (aOR=2.16, 95% CI: 1.32- 3.53, p=0.002), diabetes (aOR=1.77, 95% CI: 1.07-2.90, p=0.025), cancer (aOR=4.65, 95% CI: 1.50-14.42, p= 0.008), lymphopenia (aOR=2.34, 95% CI: 1.45-3.78, p= 0.001), and high AST (aOR= 1.89, 95% CI: 1.04-3.43, p=0.036).
Risk Factors for ICU admission among patients with COVID-19 (N=1560)

Conclusion
Having CVD, diabetes, lymphopenia, and increased AST were independent predictors for both ICU admission and in-hospital mortality in patients with COVID-19. In addition, obesity, high ferritin, and CRP levels were associated with increased risk of ICU admission, while cancer was strongly associated with in-hospital mortality. Early identification and monitoring of patients at risk is essential in planning the level of care needed to prevent delay in medical intervention.

Disclosures
Adel Abou-Ali, PharmD, PhD, Astellas Pharma Global Development, Inc. (Employee)
397. Impact of School Opening Model on Cases of SARS-CoV-2 in Surrounding Communities: A Nationwide, Retrospective Cohort Study
Authors: Branch-Elliman Westyn , Ertem Zeynep , Perkins Elissa , van den Berg Polly , Epshtein Isabella , Chaiyakunapruk Nathorn , Wilson Fernando , Oster Emily , Nelson Richard 
Published: December 2021

Abstract

Background
Early in the COVID-19 pandemic, elementary and secondary schools were closed. There was variation in school opening mode (traditional, hybrid, remote) in fall 2020.The aim of this national, retrospective cohort study is to evaluate the impact of in-person learning on community incidence of SARS-CoV-2 and COVID-19-related deaths.

Methods
Data were extracted from several data sources. School opening mode was collected from the Burbio school tracker, which tracks school openings in a sample of school districts across the US. Incidence of SARS-CoV-2 and COVID-19 related deaths were obtained from the CDC. Data on community-level SARS-CoV-2 mitigation measures were obtained from the Oxford University COVID-19 Government Response Tracker. The effect of school mode on SARS-CoV-2 cases and deaths/100,000 during the 12-weeks following the start of school was estimated using a log-linear model with state, week, and state-week fixed effects. Models were stratified by 9 US Census divisions and adjusted for variables determined a priori to be potentially associated with the outcome.

Results
519 US counties were included (Figure 1); mean cases of COVID-19 were increasing across all regions during the weeks following the start of school, regardless of school mode. Adjusted absolute differences in COVID-19 cases in counties with hybrid and traditional school opening modes relative to fully remote learning models are presented in Figure 2. In the Northeast and Midwest regions of the country, COVID-19 case rates were not statistically different between different school modes. However, in the South and West regions, there was a statistically significant increase in cases per week among counties that opened in an in-person relative to remote learning model, ranging from 17.1 (95% CI: 0.3-33.8) to 24.4 (95% CI: 7.3-41.5) in the South and from 19.0 (95% CI: 8.8-29.3) to 109.2 (95% CI: 50.4-168.0) in the West. There was no impact of school opening mode on COVID-19-related deaths.
Figure 1. Map with distribution of counties and school opening mode across the United States

Figure 2. Impact of school opening mode on subsequent cases of SARS-CoV-2, stratified by region.

Conclusion
Impact of school mode on community case-rates of SARS-CoV-2 varied across the US. In some areas of the country, traditional school mode was associated with increases in case rates relative to virtual while there were no differences in other regions.

Disclosures
All Authors: No reported disclosures
399. Epidemiology of Laboratory-identified Late-onset SARS-CoV-2 Positivity in Two Large, Urban, Acute-Care Hospitals: Implications for Surveillance of Hospital-Acquired COVID-19
Authors: Trick William , Lin Michael Y , Welbel Sharon F , Donceras Onfofre T , Zhang Huiyuan , Tseng Marion , Santos Carlos 
Published: December 2021

Abstract

Background
Laboratory identification (Lab-ID) of late-onset SARS-CoV-2 positive tests during a hospital stay is a potential public health surveillance approach for hospital-acquired COVID-19. However, prolonged RNA fragment shedding and intermittent detection of SARS-CoV-2 virus via PCR testing among infected patients may hamper interpretation of laboratory-identified events. We aimed to describe the epidemiology of late-onset SARS-CoV-2 laboratory events using clinical criteria, to evaluate the feasibility of a Lab-ID approach to detection of nosocomial SARS-COV-2 infection.

Methods
We evaluated all SARS-CoV-2 RT-PCR positive results recovered from patients at two acute-care hospitals in Chicago, IL, during March 1 — November 30, 2020. Each hospital maintained stringent infection control policies through-out the study period. Through chart review (WT & CS), we categorized all initial SARS-CoV-2 positive tests collected &gt; Hospital Day 5 (defined as ‘late-onset’ based on the 5-day mean incubation period for COVID-19) into the following clinical categories: Community Acquired; Unlikely Hospital Acquired; Possible Hospital Acquired; and Probable Hospital Acquired. Categorizations were made using hospital day, symptoms, alternative diagnoses, and clinical notes (Figure 1).

Results
Of 2,671 SARS-CoV-2-positive patients, most positive tests (n=2,551; 96%) were recovered pre-admit or by Hospital Day 2; first positive tests were uncommon during Hospital Days 6 to 14 (n=40; 1.5%); and rare after Hospital Day 14 (n=15; 0.6%). By chart review, of the 55 late-onset records reviewed, categorizations in descending order were: Prior positive at outside facility (n=23); Possible Hospital Acquired (n=16); Community Acquired (n=12); Probable Hospital Acquired (n=4). Less than half of the late-onset cases were categorized as a possible or probable hospital acquisition (Figure 2).

Conclusion
Hospital-acquired SARS-CoV-2 infection was uncommon. Most late-onset episodes of SARS-CoV-2 were explained by detection at an outside healthcare facility or by delayed diagnosis of patients with symptoms at time of presentation. A Lab-ID approach to nosocomial COVID-19 surveillance would potentially misclassify a substantial number of patients.

Disclosures
All Authors: No reported disclosures
400. Impact of SARS-CoV-2 Test-based Strategy to Reduce Quarantine Days Among Asymptomatic Healthcare Workers After Household Exposure
Authors: Navalkele Bhagyashri D , Lucar Jose , Brock James B , Parham Jason 
Published: December 2021

Abstract

Background
Appropriate staffing is essential to provide safe patient care. During the COVID-19 pandemic, healthcare workers (HCWs) are missing work days due to illness or high-risk exposure (HRE) to an infected person. To avoid staffing shortages, we implemented a SARS-CoV-2 test-based strategy among asymptomatic HCWs after HRE to facilitate early return to work.

Methods
In July 2020, our institution implemented a SARS-CoV-2 RT-PCR test-based strategy among HCWs within 7 days of HRE. HCWs were defined as any paid or unpaid persons directly or indirectly involved in patient care. HRE was defined as close contact &lt; 6 feet with an infected household member without use of mask and lasting for ≥ 15 minutes. Contact with a patient or coworker was not considered high-risk due to universal masking and eye protection use. HCWs underwent SARS-CoV-2 RT PCR testing of a nasopharyngeal swab at least once (1-2 days post-exposure) or twice (5-7 days post-exposure). HCWs with symptoms at baseline were excluded. HCWs who were asymptomatic during evaluation were considered as truly asymptomatic (TA). Saved work-days (SWD) were calculated based on number of days saved due to testing strategy compared to the Centers for Disease Control and Prevention’s recommended 14-day quarantine. HCWs were allowed to return to work within 7 days of HRE if they tested negative, or after completing 10-day isolation period ± improvement in symptoms from symptom onset if they tested positive.

Results
Between 07/01/2020 to 12/31/2020, 450 unique asymptomatic HCWs underwent SARS-CoV-2 testing. Of those, 84% were women and median age was 36 years, 347 tested negative and 103 tested positive. Of those positives, 33% of HCWs tested positive on day 2 after HRE with 141 SWDs (average 2 days/person). Only 37% were TA positives. Of those negatives, 94% were TA SARS-CoV-2 negative with 2620 SWDs (average 7.5 days/person). There were no healthcare outbreaks related to HCWs allowed to return to work following this strategy.

Asymptomatic healthcare workers commonly tested positive for SARS-CoV-2 on day 2 from household exposure compared to other days

Conclusion
Test-based strategy among asymptomatic HCWs with HRE reduced loss of workdays and helped limit staffing shortages. Majority of positive HCWs developed symptoms after positive SARS-CoV-2 testing, which may support allowing most fully vaccinated HCWs with no COVID-like symptoms to continue to work unless symptomatic.

Disclosures
All Authors: No reported disclosures
401. Natural History of Shedding and Household Transmission of Severe Acute Respiratory Syndrome Coronavirus 2 Using Intensive High-Resolution Sampling
Authors: Altamirano Jonathan , Govindarajan Prasanthi , Blomkalns Andra , Leary Sean , Robinson India , Chun Leanne , Shaikh Nuzhat , Tam Grace , Lopez Marcela , Robinson Makeda , Carrington Yuan J , De Araujo Monique , Walter Katharine , Andrews Jason , Hogan Catherine , Pinksy Benjamin A , Maldonado Yvonne A 
Published: December 2021

Abstract

Background
In order to mitigate the spread of SARS-CoV-2 and the COVID-19 pandemic, public health officials have recommended self-isolation, self-quarantine of exposed household contacts (HHC), and mask use to limit viral spread within households and communities. While household transmission of SARS-CoV-2 is common, risk factors for HHC transmission are poorly understood.

Methods
In this prospective cohort study, we enrolled 37 households with at least one reverse transcription polymerase chain reaction-confirmed (RT-PCR) COVID-19 index case from March 2020 - March 2021, in order to calculate secondary attack rates (SAR) and define risk factors for secondary infections. Participants were tested daily for SARS-CoV-2 via RT-PCR, using self-collected lower nasal samples. Households were followed until all members tested negative for seven consecutive days. We collected demographics, medical conditions, relationship to index case, and socioeconomic indicators. Subgroup data analysis was conducted and stratified by positivity status.

Results
Of 99 enrolled participants, 37 were index cases and 62 were household contacts (HHC), of whom 25 HHC were infected (40.3%). Secondary attack rate (SAR) was highest among adults caring for a parent (n=4/4, 100%) and parents of index cases (5/10, 50%). Households whose income came from service work had greater risk of transmission compared to households whose primary income was technology (n=5/7; 71.4% vs 3/8; 37.5% respectively). Pediatric contacts were at lower risk of infection when compared to adult contacts (n=5/18, 27.8% vs n=20/44, 45.5% respectively).

Conclusion
This study suggests that household transmission represents a key source of community-based infection of SARS-CoV-2. Allocating resources for education/training regarding prevention among infected individuals and their close contacts will be critical for control of future outbreaks of SARS-CoV-2.

Disclosures
All Authors: No reported disclosures
402. COVID-19 Infection in Nepal: Epidemiological Analysis from April 2020 to March 2021
Authors: Le Hai V 
Published: December 2021

Abstract

Background
In December 2019, SARS-CoV-2 or coronavirus disease 2019 (COVID-19) emerged from Wuhan, China. A global pandemic quickly unfolded, infecting &gt;137 million people and causing &gt;2.9 million deaths globally as of April 13, 2021. Before April 1, 2020, there were only five confirmed COVID-19 cases in Nepal. Like many countries around the world, the COVID-19 situation quickly escalated in Nepal. The purpose of this study was to determine the trends in COVID-19 cases and deaths in Nepal from April 2020 to March 2021.

Methods
We utilized epidemiological data from daily Situation Reports published by the Ministry of Health and Population (MOHP) of Nepal. Data were extracted or calculated from April 1, 2020 to March 31, 2021. Primary variables of interest were national and provincial daily cases, total cases, daily deaths, and total deaths.

Results
Between April 1, 2020 to March 31, 2021, there were 277,304 cases. October 2020 had the highest monthly cases with 92,926 cases. During the one-year study period, the infection rate was 915 cases per 100,000 people. The largest single-day new cases was October 21, 2020 with 5,743 cases, which is calculated to 19 cases per 100,000 people. There were a total of 3,030 deaths. The largest daily new deaths was November 4, 2020 with 43 cases. June 10, 2020 had the highest number of people in quarantine with 172,266 people. October 23, 2020 had the highest number of active cases with 46,329 cases. By March 31, 2021, the percent of mortality was 1.1%, active infection was 0.5%, and recovery was 98.4%.

Conclusion
Nepal had lower COVID-19 infection and case-fatality rates compared to other countries most affected by the pandemic. This was due to several factors, most notably early implementation of strict lockdown measures and closing of international borders on March 24, 2020 after the second confirmed COVID-19 case. As lockdown restrictions were lifted on July 7, 2020, COVID-19 cases and deaths in Nepal rose rapidly. As vaccination begun on January 27, 2021, cases started to slow down until the most recent outbreak coinciding with the second wave in its neighboring country, India. Now, infection and case-fatality rates in Nepal are at an all-time high, prompting further lockdowns on April 29, 2021.

Disclosures
All Authors: No reported disclosures
403. Clinical Characteristics and Outcomes of Patients with COVID-19 Admitted to the Intensive Care Unit in the Dominican Republic
Authors: Rojas-Fermin Rita Alexandra , Sanchez Ann , Guzman Anel E , Germosen Edwin , Matos Cesar , Mena Lora Alfredo J 
Published: December 2021

Abstract

Background
The disease caused by SARS-CoV-2, COVID-19, has caused a global public health crisis. Reported mortality rates across the world vary by region, local population characteristics and healthcare systems. There is a paucity of data on COVID-19 in low and middle income countries (LMICs). Our objective is to describe the clinical characteristics of critically ill patients with COVID-19 in the Dominican Republic (DR)

Methods
We performed a retrospective review of patients admitted to the intensive care unit (ICU) with severe COVID-19 from March to December 31, 2020, at a 295-bed tertiary teaching hospital in the DR. Clinical characteristics, demographics, comorbidities, management and outcomes were tabulated. Survival was categorized by age and comorbidities.

Results
A total of 382 patients were admitted to the ICU. The median age was 64 (range 14-97) and 64.3% (246) were male. Hypertension, diabetes, and obesity were the most common risk factors (Table 1). Corticosteroids were used in 91.6% (350), tocilizumab in 63% (82), and remdesivir in 31.6% (31). Antibacterials were used in 99.2% (379) of patients in the ICU. All-cause mortality in the ICU was 35.3% (135). Mortality was higher in older age groups (Figure 1) and in patients with multiple coexisting comorbidities (Figure 2).
Table 1. Comorbidities of patients with COVID-19 admitted to the ICU

Conclusion
Hypertension, obesity and diabetes were common in critically ill patients with COVID-19 in the DR. Corticosteroids and tocilizumab were commonly used. Antibacterials were used in &gt;99% of patients admitted to the ICU and may signal a target for future antimicrobial stewardship. Higher mortality rates were present in older age groups and those with multiple comorbidities. Risk of death increased drastically after age 40 and was comparative to those in advanced age groups. In patients with 4 comorbidities and above, mortality was more than three times higher.

Disclosures
All Authors: No reported disclosures
404. COVID-19 Normality Rate: Criteria for Optimal Time to Return to In-person Learning
Authors: Starling Carlos , Machado-Pinto Jackson , Tupinambás Unaí , Silva Estevão Urbano , Couto Bráulio R G M 
Published: December 2021

Abstract

Background
The COVID-19 pandemic created the most severe global education disruption in history. According to UNESCO, at the peak of the crisis over 1.6 billion learners in more than 190 countries were out of school. After one year, half of the world’s student population is still affected by full or partial school closures. Here we investigated whether or not it is possible to build a multivariate score for dynamic school decision-making specially in scenarios without population-scale RT-PCR tests.

Methods
Normality rate is based on a COVID-19 risk matrix (Table 1). Total score (TS) is obtained by summing the risk scores for COVID-19, considering the six parameters of the pandemic in a city. The COVID-19 Normality Rate (CNR) is obtained by linear interpolation in such a way that a total score of 30 points is equivalent to a 100% possibility of normality and, in a city with only six total points would have zero percent chance of returning to normality: CNR = (TS – 6)/24 (%). The criteria for opening and closing schools can be defined based on the percentages of return to normality (Table 2).
Table 1. Limits for each parameter of the risk matrix and "normality" scores in relation to COVID-19: the lower the risk, higher is the “normality” score.

Table 2. Criteria for opening and closing schools in a city according to the COVID-19 Normality Rate.

Results
at June 3rd, 2021, we evaluated all 5,570 Brazilian cities (Figure 1): 2,708 cities (49%) with COVID-19 normality rate less than 50% (full schools closure), 2,223 cities (40%) with normality rate between 50% and 70% (in-person learning only for 5 years and 8 months-old children), 583 with normality rate between 71% and 80% (in-person learning extended to children age 12 years and less), 583 cities (1%) with normality rate between 81% to 90% (in-person learning extended to the student population age 18 years), and just one city with 92% COVID-19 normality rate (in-person learning extended to all the student population). We calculated the COVID-19 normality rate between January and May, 2021, in four countries: Brazil, USA, UK, and Italy (Figure 2). At Jun, 3rd, 2021, percentage of people fully vaccinated in Brazil varied from 0% to 69%, an average of 11%.
Figure 1. COVID-19 Normality Rate in 5,570 cities in Brazil, Jun/03/2021.

Figure 2. COVID-19 Normality Rate between January and May, 2021: comparison among Brazil, USA, UK, and Italy.

Conclusion
COVID-19 vaccination programs take several months to implement. Besides fully vaccination of the population, it is important to check if people became really safe from the virus. The COVID-19 Normality Rate is a double check multivariate score that can be used as a criteria for optimal time to return to in-person learning safely.

Disclosures
All Authors: No reported disclosures
405. A Qualitative Study Based on a Case Series of Obstetric COVID-19 Patients to Determine Risks in Management Associated with Severity in a Government Hospital in the Philippines
Authors: Funelas-Santos Sheila Patrice D , Peralta Judith P 
Published: December 2021

Abstract

Background
Enterprise Risk Management (ERM) in healthcare is a method used to identify, assess and reduce risk to patients and the hospital organization. The objective of this study is to identify clinical and organizational challenges and risks in healthcare management caused by COVID-19, and its impact on patients and healthcare workers, in a low-resource obstetric setting.

Methods
From a census of patients from 1 April 2020 to 30 July 2020, four cases of COVID-19 in pregnancy representing different severity levels were selected. A patient tracer activity was done for each patient, documenting events that the patient and healthcare team experienced from admission to discharge. A case series on these patients was written. A focus group consisting of an OB-GYN resident, OB-GYN consultant, OB-GYN nurse, OB-GYN infectious disease consultant, and internal medicine resident and consultant, was formed. Each case was presented to the focus group to establish the context of risk assessment. Risks were identified using the framework of Enterprise Risk Management. Each risk was classified according to their risk domain and severity. Root cause analysis via the fishbone method was used to identify the causes of the risks.

Results
Operational risks identified were delayed swab results, false negative swab results, and delayed patient transport. Clinical/Patient risks identified were COVID-19 exposure of healthcare workers and other non-COVID patients, inadvertent community exposure, risk for severe clinical manifestations of COVID-19, and lack of specific treatment for COVID-19. Risk to human capital identified were COVID-19 infection of hospital staff and decreased quantity of workforce due to quarantine. Most risks were assessed to be moderate risk or high risk in terms of severity. Root cause analysis showed that common causes of risks were due to exposure to asymptomatic patients and delayed and false-negative swab results.

Conclusion
The results of this study may be used towards the final steps of ERM: risk evaluation, treatment and management, in a low resource setting.

Disclosures
Judith P. Peralta, Doctor of Medicine, Fellow of Philippine Obstetrical and Gynecological Society, Fellow of Philippine Infectious Disease Society for Obstetrics and Gynecology, Research Fellow - Harvard Medical School Department of Obstetrics and Gynecology and Reproductive Medicine (2014-2015), Member - International Society of Infectious Diseases (Brookline, MA), Pfizer (Employee, November 2020 to present)