bluuebunny/crossref_metadata_2025_split huggingface dataset:
59. Persistence of Respiratory and Non-respiratory Symptoms Among COVID-19 Patients Seeking Care at an Ambulatory COVID-19 Center
Authors: Ramakrishnan Aditi , Zreloff Jennifer , Moore Miranda , Bergquist Sharon H , Cellai Michele , Higdon Jason , O’Keefe James B , Roberts David L , Wu Henry M 
Published: January 2021

Abstract

Background
While hospitalized COVID-19 patients are well described in the literature, studies of the natural history and ambulatory cases are limited. We aim to describe the symptoms and clinical course of COVID-19 among ambulatory patients seen at the Emory University multidisciplinary Acute Respiratory Clinic (ARC) developed to care for patients with confirmed or suspected COVID-19.

Methods
PCR-confirmed COVID-19 cases seen at ARC from 4/3–5/16/2020 were included in a retrospective chart review. Encounters were classified as acute, subacute, or convalescent depending on the duration since illness onset (&lt; 1, 1–4, or &gt;4 weeks, respectively). Demographic, clinical, physical exam, diagnostic test, and disposition data were abstracted and analyzed with standard descriptive statistics.

Results
Among 404 visits at ARC, 127 (31.4%) were for confirmed COVID-19 illness (107 unique patients with 1–4 visits). The majority (75.7%) of patients were female, and the median age was 55 years (range 24–89). Patients presented during acute, subacute, and convalescent phases of illness (15.7%, 58.3%, and 26.0%, respectively; Table). Prevalent co-morbidities included hypertension (39.3%), obesity (27.1%), diabetes (20.6%), and asthma (21.5%). While measured or subjective fever was reported in the majority of acute visits (60.0%), it was less common in subacute and convalescent encounters (27.0% and 30.3%). Cough was commonly reported in acute, subacute, and convalescent visits (70.0%, 79.7%, 66.7%), as were dyspnea on exertion (45.0%, 70.3%, 66.7%) and chest tightness (40.0%, 40.5%, 60.6%). Although smell or taste alteration was present in almost half of acute and subacute patients, it was only reported in a quarter of convalescent patients. Among the three stages of illness, transfers from ARC to the ED or direct hospitalizations occurred in 15.0%, 23.0%, and 12.1% of acute, subacute and convalescent visits, respectively.
Table Timecourse of Symptoms among COVID-19 Patients in the Ambulatory Context

Conclusion
Following acute illness, COVID-19 patients can experience persistent symptoms, primarily respiratory symptoms, which can be severe enough to warrant hospitalization. Clinics evaluating recovering patients should prepare to manage these symptoms. Further study of the pathophysiology and treatment of persistent pulmonary symptoms in COVID-19 is needed.

Disclosures
All Authors: No reported disclosures
60. New Prognostic Markers for COVID-19 Disease
Authors: Ramahi Amr , Chan Kok Hoe , Prabhakar Laxminarayan , Farouji Iyad , Thimmareddygari Divya , DaCosta Theodore R , Chittamuri Sahithi , Patel Kinjal D , Poudel Bishnu , Omour Bader I Al , Paige Amy , Joseph Ormena , Patel Khamoshi , Bhavsar Nilam , Shaaban Hamid S , Slim Jihad 
Published: January 2021

Abstract

Background
A few COVID-19 related retrospective studies have established that older age, elevated neutrophil-lymphocyte ratio (NLR), and decreased lymphocyte-CRP ratio (LCR) were associated with worse outcome. Herein, we aim to identify new prognostic markers associated with mortality.

Methods
We conducted a retrospective hospital cohort study on patients ≥ 18 years old with confirmed COVID-19, who were admitted to our hospital between 03/15/2020 and 05/25/2020. Study individuals were recruited if they had a complete CBC profile and inflammatory markers such as CRP, ferritin, D-dimer and LDH, as well as a well-defined clinical outcomes (discharged alive or expired). Demographic, clinical and laboratory data were reviewed and retrieved. Univariate and multivariate logistic regression methods were employed to identify prognostic markers associated with mortality.

Results
Out of the 344 confirmed COVID-19 hospitalized patients during the study period, 31 who did not have a complete blood profile were excluded; 303 patients were included in the study, 89 (29%) expired, and 214 (71%) were discharged alive. Demographic analysis was tabulated in Table 1.
The univariate analysis showed a significant association of death with absolute neutrophil count (ANC, p=0.022), NLR (p=002), neutrophil-monocyte ratio (NMR, p=&lt; 0.0001), LCR (p=0.007), lymphocyte-LDH ratio (LLR, p=&lt; 0.0001), lymphocyte-D-dimer ratio (LDR, p=&lt; 0.0001), lymphocyte-ferritin ratio (LFR, p=&lt; 0.0001), and platelets (p=0.037) with mortality. With multivariable logistic regression analysis, the only values that had an odds of survival were high LDR (odds ratio [OR] 1.763; 95% confidence interval [CI], 1.20–2.69), and a high LFR (OR 1.136, CI 1.01–1.34).
We further build up a model which can predict &gt;85% mortality in our cohorts with the utilization of D-dimer (&gt;500 ng/ml), Ferritin (&gt;200 ng/ml), LDR (&lt; 1.6), LFR (&lt; 4) and ANC (&gt;2.5). This new model has a ROC of 0.68 (p&lt; 0.0001).

Conclusion
This retrospective cohort study of hospitalized patients with COVID-19 suggests LDR and LFR as potential independent prognostic indicators. A new model with combination of D-dimer, Ferritin, LDR, LFR and ANC, was able to predict &gt;85% mortality in our cohort with ROC of 0.68, it will need to be validated in a prospective cohort study.

Disclosures
Jihad Slim, MD, Abbvie (Speaker’s Bureau)Gilead (Speaker’s Bureau)Jansen (Speaker’s Bureau)Merck (Speaker’s Bureau)ViiV (Speaker’s Bureau)
61. Using Machine Learning for Prediction of Poor Clinical Outcomes in Adult Patients Hospitalized with COVID-19
Authors: Rodriguez-Nava Guillermo , Trelles-Garcia Daniela Patricia , Yanez-Bello Maria Adriana , Chung Chul Won , Chaudry Sana , Khan Aimen , Friedman Harvey , Hines David 
Published: January 2021

Abstract

Background
As the ongoing COVID-19 pandemic develops, there is a need for prediction rules to guide clinical decisions. Previous reports have identified risk factors using statistical inference model. The primary goal of these models is to characterize the relationship between variables and outcomes, not to make predictions. In contrast, the primary purpose of machine learning is obtaining a model that can make repeatable predictions. The objective of this study is to develop decision rules tailored to our patient population to predict ICU admissions and death in patients with COVID-19.

Methods
We used a de-identified dataset of hospitalized adults with COVID-19 admitted to our community hospital between March 2020 and June 2020. We used a Random Forest algorithm to build the prediction models for ICU admissions and death. Random Forest is one of the most powerful machine learning algorithms; it leverages the power of multiple decision trees, randomly created, for making decisions.

Results
313 patients were included; 237 patients were used to train each model, 26 were used for testing, and 50 for validation. A total of 16 variables, selected according to their availability in the Emergency Department, were fit into the models. For the survival model, the combination of age &gt;57 years, the presence of altered mental status, procalcitonin ≥3.0 ng/mL, a respiratory rate &gt;22, and a blood urea nitrogen &gt;32 mg/dL resulted in a decision rule with an accuracy of 98.7% in the training model, 73.1% in the testing model, and 70% in the validation model (Table 1, Figure 1). For the ICU admission model, the combination of age &lt; 82 years, a systolic blood pressure of ≤94 mm Hg, oxygen saturation of ≤93%, a lactate dehydrogenase &gt;591 IU/L, and a lactic acid &gt;1.5 mmol/L resulted in a decision rule with an accuracy of 99.6% in the training model, 80.8% in the testing model, and 82% in the validation model (Table 2, Figure 2).
Table 1. Measures of Performance in Predicting Inpatient Mortality

Conclusion
We created decision rules using machine learning to predict ICU admission or death in patients with COVID-19. Although there are variables previously described with statistical inference, these decision rules are customized to our patient population; furthermore, we can continue to train the models fitting more data with new patients to create even more accurate prediction rules.
Figure 1. Receiver Operating Characteristic (ROC) Curve for Inpatient Mortality

Table 2. Measures of Performance in Predicting Intensive Care Unit Admission

Figure 2. Receiver Operating Characteristic (ROC) Curve for Intensive Care Unit Admission

Disclosures
All Authors: No reported disclosures
62. Association Between Influenza Co-infection and Poor Outcomes in Patients Hospitalized with COVID-19
Authors: Bobadilla Renato G , Flores Carlos , Francis Garry , Bengualid Victoria 
Published: January 2021

Abstract

Background
In December 2019, a novel coronavirus (COVID-19) infection emerged in Wuhan, China, establishing itself as a deadly pathogen leading to an ongoing pandemic. The incidence of co-infection of COVID-19 and Influenza has not been widely reported. Both infections have been known to share similar mechanisms of transmission, however currently, there is no evidence regarding the relationship between co-infection between this viruses and worsening outcomes. Once social distancing measures are eased, and daily activities resumed, there is a possibility for a second wave of cases. Given the incidence of influenza is higher during winter, a higher co-infection rate is expected in these months.

Methods
In this study, the aim was to assess the association of influenza co-infection with outcomes in patients diagnosed with COVID-19 in a hospital-based case-control study in Bronx, New York. 19 patients with Influenza co-infection were found in total. 1 patient did not meet inclusion/exclusion criteria. Charts were reviewed from 18 confirmed cases of influenza and COVID-19 patients. Controls were selected from the remaining pool of patients with COVID-19 in the same period. Cases were matched for age, sex and underlying comorbidities (Hypertension, Diabetes Mellitus, liver disease, cardiovascular disease, HIV status, immunocompromised state other than HIV). The measured outcomes were: in-hospital mortality, need for mechanical ventilation, need for vasopressors and need for renal replacement therapy. For each outcome, Chi Square test and Odds ratio were obtained.

Results
After statistical analysis, no significative difference was found in the following variables: in-hospital mortality [Odds ratio (OR) 0.769; 95% confidence interval (CI): 0.185–3.191; p value= 0.717], need for mechanical ventilation (OR 1.3; 95% CI: 0.313–5.393; p value= 0.717), need for vasopressors (OR 1.923; 95% CI: 0.383–9.646; p value= 0.423), need for renal replacement therapy (OR 1.0; 95% CI: 0.208–4.814; p value= 1.0).

Conclusion
There was no difference in the outcome in COVID-19 patients co-infected with influenza compared to non co-infected patients, however, a larger sample of cases will be needed for further assessment of these outcomes.

Disclosures
All Authors: No reported disclosures
64. Metagenomic Sequencing to Identify Alternative Infections and Co-infections in Persons Under Investigation for covid-19
Authors: Babiker Ahmed , Bradley Heath L , Stittleburg Victoria D , Key Autum , Kraft Colleen , Waggoner Jesse , Piantadosi Anne 
Published: January 2021

Abstract

Background
Broad testing for respiratory viruses among persons under investigation (PUI) for SARS-CoV-2 is performed inconsistently, limiting our understanding of alternative infections and co-infections in these patients. Here, we used unbiased metagenomic next-generation sequencing (mNGS) to assess the frequencies of 1) alternative viral infections in SARS-CoV-2 RT-PCR negative PUIs and 2) viral co-infections in SARS-CoV-2 RT-PCR positive PUIs.

Methods
A convenience sample set was selected from PUIs who were tested for SARS-CoV-2 in the Emory Healthcare system during the first 2 months of the pandemic from 02/26-04/23/20. Laboratory results were extracted by chart review; Flu/RSV and multiplex respiratory pathogen PCRs had been performed at the discretion of treating physicians. Excess nasopharyngeal swab samples were retrieved within 72 hours of collection and underwent RNA extraction and SARS-CoV-2 testing by triplex RT-PCR. mNGS was performed by DNAse treatment, random primer cDNA synthesis, Nextera XT tagmentation, and high-depth Illumina sequencing. Reads underwent taxonomic classification by KrakenUniq, as implemented in viral-ngs.

Results
53 PUIs were included, 30 negative and 23 positive for SARS-CoV-2 by RT-PCR. Among SARS-CoV-2 negative PUIs, 28 (93%) underwent clinical testing for alternative infections, and 8 (29%) tested positive for another respiratory virus. In all cases, mNGS identified the same virus (Table 1). In another 3 PUIs, mNGS identified two viruses that were not tested for and one that was missed by routine testing. No SARS-CoV-2 was detected by mNGS among RT-PCR negative PUIs. Among SARS-CoV-2 RT-PCR positive PUIs, 18 (69%) underwent clinical testing for co-infections, and none were detected. mNGS did not identify any viral co-infections but did detect SARS-CoV-2 in all 23 PUIs.
Table 1: Molecular and Metagenomic Testing of Persons Under Investigation

Conclusion
Unbiased mNGS offers the powerful opportunity to streamline testing for PUIs by assessing for SARS-CoV-2 and alternative infections simultaneously; this technique can also be used to identify co-infections, but none were observed in our study population. Interestingly, many PUIs had no infection identified on routine testing or mNGS, which may reflect inadequate sampling, rapid virus clearance, or a non-viral process.

Disclosures
All Authors: No reported disclosures
67. SARS-CoV-2 Transmission: Preliminary Findings from a Household-based Study
Authors: Grijalva Carlos G , Zhu Yuwei , Halasa Natasha B , Kim Ahra , Rolfes Melissa A , Steffens Andrea , Reed Carrie , Talbot Helen 
Published: January 2021

Abstract

Background
Social distancing measures, such as shelter-in-place or stay-at-home orders, are recommended for control of community transmission of SARS-CoV-2. Few studies, however, have characterized the transmission of SARS-CoV-2 infections in households.

Methods
We conducted a case-ascertained study of household transmission in Nashville, TN starting in April 2020, after recommendations to stay at home were enacted. Index cases were ambulatory patients identified through clinical RT-PCR testing at Vanderbilt walk-in-clinics dispersed across the Nashville metropolitan area. For this study, the index case was the first person presenting with respiratory or compatible symptoms in a household and who lived with at least one other household member. After informed consent was obtained, household members were remotely trained in the self-collection of nasal swabs and use of REDCap electronic questionnaires. Household members completed daily symptom diaries and collected daily nasal swabs for 14 days. Contact patterns within households before and after disease onset were ascertained. Nasal swab samples were tested using RT-PCR at an academic research laboratory.

Results
At the time of writing, 18 families were enrolled (including 18 index cases and 34 household members) with at least 1 follow-up nasal swab tested. The median age of index cases and household members was 37 years (IQR: 26–46) and 27 years (15–39), respectively. The median number of days from index patient onset of symptoms to first sample collected in the household was 4 (2–5). Before onset of symptoms, 83% of index cases spent &gt;4 hours in the same room with at least one other household member, whereas after disease onset and diagnosis, 44% did. Among 34 non-index household members, 18 (53%) had a positive test during follow-up; the median number of days from index case’s symptoms onset to first positive detection in a household member was 4.5 (3–5) days. Interestingly, 13 (72%) of 18 secondary infections were detected within the first 3 days of follow-up, whereas 5 (28%) were detected during subsequent days.

Conclusion
These observations suggest that transmission of SARS-CoV-2 within households is high, with many infections detected during the initial days of study follow-up.

Disclosures
Carlos G. Grijalva, MD, MPH, AHRQ (Grant/Research Support)Campbell Alliance (Grant/Research Support)CDC (Grant/Research Support)FDA (Grant/Research Support)Merck (Consultant)NIH (Scientific Research Study Investigator)Pfizer (Consultant)Sanofi (Consultant)Sanofi (Grant/Research Support) Natasha B. Halasa, MD, MPH, Genentech (Other Financial or Material Support, I receive an honorarium for lectures - it’s a education grant, supported by genetech)Karius (Consultant)Moderna (Consultant)Quidel (Grant/Research Support, Research Grant or Support)Sanofi (Grant/Research Support, Research Grant or Support)
68. Active Monitoring of a Healthcare Worker Cohort During the COVID-19 Epidemic
Authors: Datta Rupak , Campbell Melissa , Wyllie Anne , Casanovas-Massana Arnau , Handoko Ryan , Sewanan Lorenzo , Naushad Nida , Simonov Michael , White Elizabeth , Valdez Jordan , Liu Feimei , Omer Saad , Cruz Charles Dela , Farhadian Shelli F , Ring Aaron , Iwasaki Akiko , Grubaugh Nathan , Martinello Richard A , Ko Albert I 
Published: January 2021

Abstract

Background
Initial CDC recommendations for passive monitoring of COVID-19 related symptoms among staff may not be sufficient in preventing the introduction and transmission of SARS-CoV-2 in healthcare settings. We therefore implemented active monitoring for SARS-CoV-2 infection in healthcare workers (HCWs) at an academic medical center during the COVID-19 epidemic in northeast US.

Methods
We recruited a cohort of HCWs at Yale New Haven Hospital who worked in COVID-19 units and did not have COVID-19 related symptoms between March 28 and June 1, 2020. During follow-up, participants provided daily information on symptoms by responding to a web-based questionnaire, self-administered nasopharyngeal (NP) and saliva specimens every 3 days, and blood specimens every 14 days. We performed SARS-CoV-2 RT-PCR and an anti-spike protein IgM and IgG ELISA to identify virological and serological-confirmed infection, respectively.

Results
We enrolled 525 (13%) amongst 4,136 HCW of whom daily information on symptoms and NP, saliva, and blood specimens were obtained for 66% (of 13208), 42% (or 1977), 44% (of 2071) and 65% (of 1099), respectively, of the follow-up measurement points. We identified 16 (3.0% of 525) HCWs with PCR-confirmed SARS-CoV-2 infection and an additional 12 (2.3% of 525) who were not tested by PCR or had negative PCR results but had serological evidence of infection. The overall cumulative incidence of SARS-CoV-2 infection was 5.3% (28 of 525) amongst HCWs. Cases were not identified by hospital protocols for passive staff self-monitoring for symptoms. Amongst 16 PCR-confirmed cases, 9 (56%) of the 16 PCR-confirmed HCW had symptoms during or after the date of initial detection. We did not identify an epidemiological link between the 28 confirmed cases.

Conclusion
We found that a significant proportion (5.3%) of HCWs were infected with SARS-CoV-2 during the COVID-19 epidemic. In the setting of universal PPE use, infections were possibly acquired in the community rather than stemming from patient-HCW or HCW-HCW transmission. Passive monitoring of symptoms is inadequate in preventing introductions of SARS-CoV-2 into the healthcare setting due to asymptomatic and oligosymptomatic presentations.

Disclosures
All Authors: No reported disclosures
70. Lack of SARS-CoV-2 Antibody Seroconversion After Prompt Identification and Cohorting of Sentinel sars-cov-2-positive Residents in a Skilled Nursing Facility
Authors: Bhattacharya Debika , Winnett Alexander , Fulcher Jennifer A , Sohn Linda , Calub Feliza , Lee-Chang Ian , Schwartzman William A , Beenhouwer David O , Vallone John , Graber Christopher J , Goetz Matthew B 
Published: January 2021

Abstract

Background
Despite numerous outbreaks, antibody responses to SARS-CoV-2 in residents of skilled nursing facilities (SNF) are not well described. We reviewed serological test results in a cohort of SNF residents who had been repetitively screened for SARS-CoV-2 infection by nasopharyngeal swab PCR.

Methods
In late March 2019, we identified symptomatic SARS-CoV-2 PCR positive residents at a SNF. In response, all remaining SNF patients were serially screened, and all SARS-CoV-2 PCR positive patients were transferred to the acute care hospital or cohorted in a separate COVID Recovery Unit (CRU) in the SNF. In early June, all SNF residents (SARS-CoV-2 PCR positive and negative) underwent serologic testing for SARS-CoV-2 Spike (S1/S2) IgG (DiaSorin). DiaSorin IgG-positive results for patients that were SARS-CoV-2 PCR-negative were reflexed to nucleocapsid IgG (Abbott). Antibody testing occurred a median of 69 days (63–70 IQR) after PCR positivity.

Results
Nineteen SARS-CoV-2 PCR positive residents were identified from the outbreak and an additional 9 were transferred from the acute care hospital to the CRU; 1 died and 1 received convalescent plasma leaving 26 SARS-CoV-2 PCR positive residents, including 6 who were asymptomatic, that were eligible for serologic testing. Twenty-four of the 26 were positive for IgG by the DiaSorin assay; one seronegative resident was one of the asymptomatic residents. There were an additional 121 residents in the SNF whose SARS-CoV-2 PCR was negative at least once. Among these 121 SNF residents with negative SARS-CoV-2 RT-PCR, all but two were seronegative by the Diasorin assay. The two seropositive residents had no nucleocapsid antibodies when reflex tested by the Abbott assay.

Conclusion
In a limited sample of SNF residents with SARS-CoV-2 PCR positivity, the sensitivity of the Diasorin assay was 92% (24/26) and the specificity was 98% (119/121). None of the residents with negative SARS-CoV-2 PCR had confirmed positive antibody results using reflex testing (DiaSorin/Abbott). Despite high risk exposure in congregate living facilities, we found no evidence of additional SARS-CoV-2 exposure, reinforcing the importance of serial surveillance SARS-CoV-2 testing and early cohorting in SNF settings.

Disclosures
All Authors: No reported disclosures