bluuebunny/crossref_metadata_2025_split huggingface dataset:
417. Comparative Assessment of Multiple SARS-CoV-2 Antibody and Neutralization Assays from Blood Samples in COVID-19 Infected Patients
Authors: Dewar Robin L , Trevino Christina , Lallemand Perrine , Highbarger Helene , Elbeik Tarek A , Rehman Tauseef , Holbrook Michael , Schmaljohn Connie , Lane Cliff , Chary Aarthi , Holodniy Mark 
Published: January 2021

Abstract

Background
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, COVID-19) has caused a world-wide pandemic. Diagnosis is usually made by an RT-PCR test from a respiratory sample. A number of tests are available for antibody detection or assessment, including rapid, enzyme immunoassays (EIA) and neutralization. However, characterization of the antibody immune response is not well documented and the clinical significance of COVID antibodies remains largely unknown. In addition, comparison of results across different assay formats using identical samples has not been rigorously studied, making clinical interpretation of serologic tests difficult.
Assessment of multiple SARS-CoV-2 antibody and neutralization assays from blood samples in COVID-19 infected patients

Methods
1–5 serial (total 33) serum or plasma samples from 14 patients who were positive for SARS-CoV-2 by EUA authorized RT-PCR assays from nasopharyngeal specimens where tested with the following COVID-19 antibody tests: LFA rapid tests (Chembio DPP IgM/IgG, SD Biosensor Standard IgM/IgG, BTNX Rapid Response IgM/IgG), and EIA tests (BioRad Platelia SARS-CoV-2 Total antibody-IgG/IgM/IgA, EuroImmun SARS-CoV-2 IgG, and EuroImmun SARS-CoV-2 IgA). See Table 1 for results and EUA. Results were recorded as positive, negative, or equivocal. Additionally, antibody neutralization was assessed on matched samples.

Results
Mean age of SARS-CoV-2 positive patients was 73 years (range 65–89), 11/14 had symptoms, all were male and hospitalized (6 ICU), and 3 died. Average number of days serum was collected after RT-PCR positivity was 13.5 days (range -3 to 46 d). BTNX assay was only tested on 16 samples. Among all assays, total concordance of results was 91%. When only IgG/IgM or total antibody assays were considered, concordance of results was 96% (Table). IgA specific results were discordant in 9/33 (27%) of samples compared to other assays. Two patients were negative in all assays in serial samples collected within one week of PCR positivity. Antibody neutralization was detected, but not from all samples.

Conclusion
In general, there was good agreement among antibody detection assays. Neutralization may reflect disease outcome. The study was limited by the number of positive samples and patient number, and at the time specificity was not addressed for all the assays.

Disclosures
All Authors: No reported disclosures
418. Comparison of the Abbott SARS-CoV-2 IgG and DiaSorin LIASON SARS-CoV-2 S1/S2 IgG Antibody Assays
Authors: Schaack Grace A , Chen Derrick 
Published: January 2021

Abstract

Background
The Abbott Laboratories SARS-CoV-2 IgG assay and the DiaSorin LIASON SARS-CoV-2 S1/S2 IgG assay are both chemiluminescent immunoassays that qualitatively detect IgG antibodies against SARS-CoV-2 antigens. The Abbott assay detects IgG against the viral nucleocapsid (N) protein, while the DiaSorin assay uses antigen derived from the viral spike (S) protein. Here we evaluate the performance of these two assays at our institution.

Methods
45 patient samples (serum or plasma) were tested for anti-SARS-CoV-2 IgG by both the Abbott and DiaSorin assays. The samples were previously characterized at a national reference laboratory using the Abbott assay or by an in-house PCR-based test for SARS-CoV-2 RNA. Samples yielding discordant results across platforms were further tested using the EUROIMMUN Anti-SARS-CoV-2 ELISA (IgG) assay at the reference laboratory.

Results
22 samples tested negative for SARS-CoV-2 by the reference lab Abbott assay, and 23 tested positive by the same reference lab test (n = 13) or by an in-house PCR-based test (n = 10). The 22 samples characterized as negative again tested negative by both the Abbott (in-house) and DiaSorin assays (100% NPA). Among the 23 samples characterized as positive, all 23 tested positive by the Abbott assay (100% PPA), while only 15 tested positive by the DiaSorin assay (65% PPA). For each of the 8 discordant cases, samples were further tested by EUROIMMUN assay, which targets the S protein; 7 of the 8 samples tested negative by this assay, in agreement with the DiaSorin test results. Thus, for the discordant cases, testing for IgG against N (in-house and reference lab Abbott assays) gave positive results, while testing for IgG against S (DiaSorin and EUROIMMUN assays) mostly gave negative results.

Conclusion
These findings highlight the importance of the differences between various SARS-CoV-2 antibody tests, and providers should be aware of the specific antigenic target(s) in each test. Selection of a specific assay may depend on the need to assess past exposure to SARS-CoV-2 (for which a nucleocapsid target may be more sensitive) or to detect neutralizing antibodies (for which a spike target may be more relevant). This also has implications for disease surveillance as reliance on anti-spike antibodies alone may underestimate infection prevalence.

Disclosures
All Authors: No reported disclosures
424. Sensitivity Results for the Abbott m2000 PCR Assay of SARS-CoV-2 at a Denver, Colorado Medical Center
Authors: Aragon Deborah , Young Heather , Wilson Michael L , Knepper Bryan C , Triplett Laura , Bacon Tim 
Published: January 2021

Abstract

Background
The Abbott RealTime SARS-CoV-2 assay (Abbott Laboratories, Chicago, Illinois) is an RT-PCR test for qualitative detection of SARS-CoV-2 nucleic acid in NP and OP specimens performed on the Abbott m2000 System. Currently, no published data exists on the performance characteristics of the assay.

Methods
Denver Health Medical Center (DHMC) is a 550-bed hospital that is Denver County’s safety net institution. The Department of Pathology and Laboratory Services at DHMC provides testing for both inpatient and outpatient populations. In March 2020, we validated the Abbott RealTime SARS-CoV-2 assay. Beginning March 19, inpatients and outpatients with SARS-CoV-2 symptoms were tested. On April 22, universal testing began on admitted patients, regardless of symptoms, and on May 2, testing began on asymptomatic outpatients prior to time-sensitive procedures. We evaluated the sensitivity and negative predictive value (NPV) for tests done March 19 through June 16 using a surrogate method. False negative (FN) results: patients with an initial negative test then a positive test within 7 or 14 days. True negative (TN) results: patients with two initial consecutive negative tests within 7 or 14 days. True positive (TP) results: patients with an initial positive test.

Results
There were 16,152 tests done for 13,673 patients. Test results are shown in Table 1. Sensitivity for 7 and 14 days was 99.1% and 97.6%, respectively. The NPV for 7 and 14 days was 94.7% and 91.4%, respectively.
Table 1

Conclusion
There are limitations to our analysis. First, our assumption of no false positives may be incorrect. Although PCR assays are known to have a low false positive rate, the rate likely is not zero, but in the absence of a true gold standard comparator, we could not calculate test specificity. Second, testing asymptomatic patients may artificially inflate the TN results and the NPV. Third, results depend on the quality of specimen collection, preservation, transport, and handling. We believe accounting for repeat testing in a short timeframe lends credibility to the sensitivity and NPV results. Without published gold standard data on SARS-CoV-2 testing, infection can be reliably ruled both in and out using this assay. Providers can confidently use the results to make clinical and infection prevention management decisions.

Disclosures
All Authors: No reported disclosures
428. Very High Clinical Likelihood (VHCL) Of COVID-19 Infection: Peering Beyond A Negative Nasopharyngeal Swab
Authors: Khatri Akshay M , Flannery Sarah , Singh Vansha , Khameraj Aradhana , Malhotra Prashant , Farber Bruce , Kast Charles L , Gitman Michael , Foley Mathew , Hirschwerk David 
Published: January 2021

Abstract

Background
Diagnosis of COVID-19 relies upon RT-PCR assays for SARS-CoV-2 from a nasopharyngeal swab (NPS). However, results depend upon duration of illness at the time of testing and operator performance. False negatives occur 10–30% of the time. In our center we formulated & applied a clinical prediction tool for diagnosis of COVID-19 infection. Patients who satisfied criteria were designated as having COVID-19 regardless of NPS results. Herein, we describe the set of patients who fulfilled full and strict clinical criteria (VHCL) (Table 1) and had at least 2 negative NPSs on hospital admission.
Table 1: Clinical criteria for Very High Clinical Likelihood of COVID-19 Infection (VHCL COVID-19)

Methods
A retrospective descriptive study was conducted from March 4th to April 11th, 2020. We evaluated patients with ≥ 2 consecutive negative NPS COVID-19 results admitted to our hospital. Of these, we identified patients fitting all 5 criteria for Very High Clinical Likelihood (VHCL) of COVID-19 infection (Table 1). We analyzed symptoms & lab data (including results of repeat NPS testing if performed) in those patients.

Results
1855 patients were diagnosed with COVID-19 in the study period. Of these, 23 had ≥ 2 negative COVID-19 NPS results but met criteria for VHCL (Table 2). Of these 23, 7 had a subsequent 3rd NPS that was positive-proving infection. Similar to other reports, patients had low lymphocytes and elevated procalcitonin, ferritin, C-reactive protein levels. And consistent with proven cases, our cohort presented after a median of 5 days of symptoms (Table 3).
Table 2: Clinical and laboratory characteristics of VHCL COVID-19 patients [Rows Bolded include those patients who had a 3rd NPS swab that was positive].

Table 3: Demographic and Median Lab Data of VHCL COVID-19 patients

Conclusion
It is critical to be mindful of the imperfection of laboratory testing & to integrate clinical criteria to diagnostic algorithms. This is especially true in the COVID-19 pandemic, which is marked by high morbidity & mortality. In our study, we demonstrated how a set of clinical parameters (which we termed VHCL) can aid in widening the net of patients diagnosed with COVID-19 despite negative laboratory tests. While 16 patients in our cohort did not have a confirmatory result, the strict criteria for VHCL & the close match of other study variables with those of proven cases supports the value of VHCL designation. Applying VHCL can optimize infection control, identify patients for emerging therapeutics & aid in contact tracing to reduce nosocomial & community transmission of COVID-19.

Disclosures
All Authors: No reported disclosures
429. County-level predictors of COVID-19 testing across the 62 counties in New York State: A comparison across machine learning algorithms
Authors: Zeng Chengbo , Xiao Yunyu 
Published: January 2021

Abstract

Background
More than 360,000 people infected with COVID-19 in New York State (NYS) by the end of May 2020. Although expanded testing could effectively control statewide COVID-19 outbreak, the county-level factors predicting the number of testing are unknown. Accurately identifying the county-level predictors of testing may contribute to more effective testing allocation across counties in NYS. This study leveraged multiple public datasets and machine learning algorithms to construct and compare county-level prediction models of COVID-19 testing in NYS.

Methods
Testing data by May 15th was extracted from the Department of Health in NYS. A total of 28 county-level predictors derived from multiple public datasets (e.g., American Community Survey and US Health Data) were used to construct the prediction models. Three machine learning algorithms, including generalized linear regression with the least absolute shrinkage and selection operator(LASSO), ridge regression, and regression tree, were used to identify the most important county-level predictors, adjusting for prevalence and incidence. Model performances were assessed using the mean square error (MSE), with smaller MSE indicating a better model performance.

Results
The testing rate was 70.3 per 1,000 people in NYS. Counties (Rockland and Westchester) closed to the epicenter had high testing rates while counties (Chautauqua and Clinton) located at the boundary of NYS and were far away from the epicenter had low testing rates. The MSEs of linear regression with the LASSO penalty, ridge regression, and regression tree was 123.60, 40.59, and 298.0, respectively. Ridge regression was selected as the final model and revealed that the mental health provider rate was positively associated with testing (β=5.11, p=.04) while the proportion of religious adherents (β=-3.91, p=.05) was inversely related to the variation of testing rate across counties.

Conclusion
This study identified healthcare resources and religious environment as the strongest predictor of spatial variations of COVID-19 testing across NYS. Structural or policy efforts should address the spatial variations and target the relevant county-level predictors to promote statewide testing.

Disclosures
All Authors: No reported disclosures
431. Geospatial prediction model of COVID-19 outbreak in New York State
Authors: Zeng Chengbo , Xiao Yunyu 
Published: January 2021

Abstract

Background
More than 360,000 people infected with COVID-19 in New York State (NYS) by the end of May 2020.The spatial variations of prevalence across the counties in NYS suggested that variations in county-level factors might contribute to the statewide COVID-19 outbreak. However, no study to date investigates such variations and the relevant predictors. We leveraged multiple public datasets and machine learning approaches to construct the county-level spatial-temporal prediction model of COVID-19 in NYS. Findings generating from this study help identify counties with high prevalence, county-level predictors, and promising next steps for policy efforts to control the second wave of statewide COVID-19 transmission.

Methods
Cumulative confirmed case rates (CCCR) of COVID-19 by county in NYS were extracted from the US Health Data system at four critical time points including March 17th (state of emergency, 4.40 per 100,000 people), April 18th (coronavirus peak, 310.10 per 100,000 people), April 25th (expand testing, 393.90 per 100,000 people), and May 11th (daily increased rate back to the level in March, 505.30 per 100,000 people. A total of 28 county-level predictors were used to construct the prediction model, and the generalized linear mixed effect least absolute shrinkage and selection operator (LASSO) regression was employed to select the predictors of COVID-19 outbreak across the counties in NYS with adjusting for time effect.

Results
The CCCR by the final timepoint was 1,850.3 per 100,000 people. Rockland County had the highest CCCR than any other counties, with a rate of 3,856.82 per 100,000 people, while Chautauqua and Franklin counties had the lowest CCCR (0.03 per 100,000 people). LASSO regression revealed counties with a larger proportion of non-citizen (β=9537.97, p=0.02) had a higher CCCR of COVID-19 across the time. In contrast, counties with a lower proportion of people with at least high school education (β=-6157.89, p=0.025) and a larger proportion of houses with less than 3 people (β=-5995.79471, p=0.01) had lower CCCR.

Conclusion
We identified immigrant status, education level and household type influenced the spatial variations of COVID-19 outbreak in NYS. Future interventions shall target on areas with greater density of non-citizens to prevent transmission.

Disclosures
All Authors: No reported disclosures
436. Assessing SARS-CoV-2 Infection among Persons Experiencing Homelessness — Atlanta, GA, 2020
Authors: Yoon Jane C , Morris Sapna , Schmit Kristine , Hernandez Alfonso , Montgomery Martha , Ko Jean , Boyd Andrew T , Buff Ann , Flowers Nicole , Jamison Calla , Mosites Emily , Paulin Heather , Ajoku Sophia , Swancutt Mark , Holland David , Turner Kim , Andrews Tom , Shah Sarita , Prieto Juliana , Smith Sasha , Gaffga Nicholas , Cavanaugh Joseph , Marchman Cathryn 
Published: January 2021

Abstract

Background
Persons experiencing homelessness (PEH) often do not have sufficient resources to practice public health interventions. PEH may reside at overcrowded shelters, have poor access to health care, and often suffer from significant co-morbid conditions. In this study, we describe prevalence of COVID-19 amongst PEH and homeless service staff in Atlanta, Georgia, as well as demographic data, underlying conditions, and symptoms.

Methods
In response to the COVID-19 pandemic in Atlanta, PEH and homeless service providers were offered PCR testing for SARS-CoV-2 via nasopharyngeal, nasal mid-turbinate, and oropharyngeal swabs. Shelters identified as having a higher number of COVID-19 infections underwent re-testing 3–4 weeks after initial testing. Demographic information was collected, and individuals were screened for symptoms and underlying conditions.

Results
A total of 2,294 PEH and 544 staff underwent SARS-CoV-2 PCR testing from April 8 - May 6, 2020. 24 homeless shelters were included in this study. The majority of individuals were male (66.3% sheltered, 85.5% unsheltered, 55.3% staff), and 2,192 of 2,838 (78.3%) persons identified as black. Approximately half of individuals had no known underlying conditions (51.5% sheltered, 47.7% unsheltered, 55.5% staff). Cardiovascular disease was the most frequently reported condition (32.0%), followed by chronic lung disease (14.8%). In total, 45 of 2,823 (1.6%) individuals had detectable PCR results for SARS-CoV-2, including 35 of 1,666 (2.1%) sheltered clients, 3 of 614 (0.5%) unsheltered clients, and 7 of 543 (1.3%) staff. Among individuals with detectable SARS-CoV-2, 5 of 20 (25%) reported at least one symptom in the last week. Most shelters (16 of 24) had at least one individual with detectable SARS-CoV-2. The prevalence of SARS-CoV-2 in the majority of shelters (19 of 24) was &lt; 3%, with a median prevalence of 0.2% across all shelters. Upon retesting 4 shelters with a higher number of COVID-19 infections, only 3 individuals from 2 shelters tested positive out of 357 total retested (0.8%).

Conclusion
We identified a low prevalence of SARS-CoV-2 infection among PEH in Atlanta, which was influenced by a pre-emptive testing strategy. Continued vigilance is necessary to limit spread in this vulnerable population.

Disclosures
All Authors: No reported disclosures
439. Clinical Characteristics and Mortality of an Initial Cohort of COVID-19 Patients in México City
Authors: Castañeda-Mendez Paulo F , Castillo-Álvarez José Luis , Barragán-Reyes Armando , Bay-Sansores Daniela , Pulido-Enríquez Jessica Isabel , González-Ramírez Luis Humberto , Cabrera-Ruiz Maria Lorena , Aguilar-Zapata Daniel , Reyes-Mar Javier , Soto-Ramírez Luis Enrique 
Published: January 2021

Abstract

Background
As of today, more than 8 million people have been infected and around 440,000 of them have lost their lives due to complications of SARS-CoV-2 infection.
The first confirmed case of COVID-19 in Mexico was on February 28, 2020, and currently, there are more than 150,300 confirmed cases and more than 17,500 deaths have been reported, this work presents the characteristics of the first cases on a tertiary care center with special focus on common comorbidities in Mexicans.

Methods
We conducted a case series of patients with the diagnosis of pneumonia due to SARS-CoV-2 virus admitted to a tertiary care center in Mexico City, between March 14th and May 4th, 2020. Data collected included demographic information, comorbidities, clinical presentation, and outcomes. Regarding clinical outcomes, we measured the need of admission to Intensive Care Unit (ICU), mortality during hospitalization, discharge, and patients that remained hospitalized.

Results
85 patients were included, median age 53.5 years; 69.4% were male. Most common clinical manifestations at admission were fever (61, 71.8%), cough (29, 34.1%), headache (25, 29.4%) and dyspnea (22, 25.9%). Most common comorbidities were overweight (44/82, 53.6%), obesity (25/82, 30.5%), hypertension (18, 21.2%), and diabetes (17, 20%).
31 of 85 (36.5%) patients were diagnosed with critical disease, whereas 54 of 85 (63.5%) were classified as non-critical. In the 31 critically ill patients, the length of invasive mechanical ventilation was 13 days [range {2–45}]; 5 patients (16.1%) required tracheostomy. The mean of mechanical ventilation prior to tracheostomy was 19.8 days [range {14–25}].
In all patients, the total length of hospitalization was 12.1 days [range {2–52}], 14.8 days [range {3–52}] in ICU patients, and 6.7 days [range {2–30}] in floor unit patients. No readmissions were documented.
Global mortality was 4.7% (9.6% in ICU, 1.8% in floor unit). Of the 4 deceased patients, 3 presented comorbidities (75%), while 1 was previously healthy, documenting massive pulmonary embolism as the cause of sudden death.

Conclusion
This study shows that the clinical characteristics in this initial cohort are not different that described elsewhere. Mortality is low but it is mainly related to prevalent comorbidities in the Mexican population.

Disclosures
All Authors: No reported disclosures
442. Common symptoms of outpatient COVID-19 compared to non-COVID-19 Cases: A prospective epidemiologic study in a major US metropolitan area
Authors: Fairley Jessica K , Taibl Kaitlin R , Landay Taylor , Sherman Amy C , Wu Henry M , Collins Matthew H 
Published: January 2021

Abstract

Background
The majority of novel coronavirus 2019 (COVID-19) cases is comprised of non-critically ill adults. However, the medical epidemiology and clinical profile for mild COVID-19 is poorly described in the United States.

Methods
We prospectively recruited 151 mildly symptomatic adults from Emory Healthcare COVID-19 screening clinics in Atlanta, Georgia from March 18 to June 16, 2020. Interview-based questionnaires captured participants’ demographics, epidemiological history, and clinical features. Nasopharyngeal swabs were collected to test for SARS-CoV-2 by RT-PCR. Convalescent serum (13–74 days post symptom onset) from 19 participants was tested by an IgG ELISA. Descriptive and χ 2 analyses were performed to determine the characteristics of COVID-19 cases compared to patients who tested negative.

Results
A total of 151 patients were recruited. The majority were non-Hispanic white (51%), female (60%), middle-aged adults (46.3 y +/-15). Twenty-seven (17.9%) tested positive for SARS-CoV-2 and most frequently reported fever (63%), cough (67%), fatigue (56%), and myalgias (56%). See Table 1. Fever was statistically more common in positive cases vs negative (63% vs 34%, p = 0.005). Cases also experienced loss of taste (22%) and loss of smell (19%) more frequently than non-cases (p=0.01 and p=0.03). Diarrhea (22% vs 23%) and shortness of breath (33% vs 36%) did not differ significantly between groups. None of the 14 PCR-negative participants tested positive for SARS-CoV-2-specific IgG and 3 out of 5 COVID-19 cases tested positive for SARs-CoV-2-specific IgG.

Conclusion
Mild COVID-19 cases reported fever, loss of smell and loss of taste significantly more than non-COVID-19 cases. Strong correlations between anosmia and ageusia with COVID-19 have been reported elsewhere, however these symptoms were only present in 19–22% of cases at the time of testing, limiting their utility for clinical diagnosis. Also, none of the PCR-negative participants tested positive for convalescent serology, supporting good sensitivity and negative predictive value of the RT-PCR test used in our clinic. Symptoms alone cannot differentiate COVID-19 from other illnesses, highlighting the critical need for widely available and highly sensitive and specific diagnostic tests.

Disclosures
All Authors: No reported disclosures