bluuebunny/crossref_metadata_2025_split huggingface dataset:
419. Diagnostic Utility of a Ferritin to Procalcitonin Ratio to Differentiate Patients with COVID-19 from Those with Bacterial Pneumonia
Authors: Jankousky Katherine C , Hyson Peter , Huang Jin , Chastain Daniel B , Franco-Paredes Carlos , Erlandson Kristine M , Henao-Martinez Andres , Shapiro Leland 
Published: January 2021

Abstract

Background
Accurate, rapid, inexpensive biomarkers are needed to differentiate COVID-19 from bacterial pneumonia, allowing effective treatment and antibiotic stewardship. We hypothesized that the ratio of ferritin to procalcitonin (F/P) reflects greater viral activity and host response with COVID-19 pneumonia, while bacterial pneumonia would be associated with less cytolysis (lower ferritin) and more inflammation (higher procalcitonin), thus a lower F/P ratio.

Methods
We conducted a retrospective study of adult patients admitted to a single University hospital in the US through May 2020, during the COVID-19 pandemic. We compared F/P ratio of patients diagnosed with COVID-19 or bacterial pneumonia, excluding patients with COVID-19 and bacterial co-infections. In a logistic regression, we controlled for age, sex, body mass index (BMI), diabetes (DM), and hypertension (HTN). We used a receiver operating characteristic analysis to calculate the sensitivity and specificity of F/P values for the diagnosis of COVID-19 versus bacterial pneumonia.

Results
Of 218 patients with COVID-19 and 17 with bacterial pneumonia, COVID-19 patients were younger (56 vs 66 years, p=0.04), male (66% vs 24%, p=0.009), had higher BMI (31 vs 27 kg/m2, p=0.03), and similar rates of HTN (59% vs 45%, p=0.3) and DM (32% vs 18%, p=0.2). The median F/P ratio was significantly higher in patients with COVID-19 (3195 vs 860, p=0.0003, Figure 1). An F/P ratio cut-off of ≥ 1250 generated a sensitivity of 78% and a specificity of 59% to correctly classify a COVID-19 case (Figure 2). When adjusted for age, gender, BMI, DM, and HTN, a ratio ≥ of 1250 was associated with significantly greater odds of COVID-19 versus bacterial pneumonia (OR: 4.9, CI: 1.5, 16.1, p=0.009).
Figure 1. Ferritin to Procalcitonin Ratios of patients with COVID-19 and patients with Bacterial Pneumonia (controls).

Figure 2. Receiver Operating Characteristic Analysis of Ferritin to Procalcitonin Ratio Cut-off Values Predicting COVID-19 Diagnosis.

Conclusion
We observed an elevated F/P ratio in patients with COVID-19 compared to those with bacterial pneumonia. A F/P ratio ≥ 1250 provides a clinically relevant increase in pre-test probability of COVID-19. Prospective studies evaluating the discriminatory characteristics of F/P ratio in larger cohorts is warranted.

Disclosures
All Authors: No reported disclosures
420. Diagnostic Utility of Chest CT scan for COVID-19, in the Early Stage of the Pandemic in Brooklyn, New York
Authors: Gibbs Rachel , Fu Lung H , Silver Michael , Lockerman Zachary S , Ghitan Monica , Chapnick Edward , Lin Yu Shia 
Published: January 2021

Abstract

Background
Diagnosis of coronavirus disease 2019 (COVID-19) in the early weeks of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic in New York City posed unique challenges. Due to inadequate testing availability and long turnaround times, decisions on which patients to isolate were problematic. With sensitivity comparable to reverse transcription polymerase chain reaction (RT-PCR), the absence of ground glass opacities (GGOs) on chest CT scan was useful to rule out COVID-19. We evaluated the specificity of chest CT scan findings for COVID-19 along with other clinical and laboratory findings.

Methods
A retrospective chart review was done of 182 adult patients who were tested for SARS-CoV-2 by RT-PCR and underwent a chest CT scan while admitted to Maimonides Medical Center between March 1 to 23, 2020. Cases were defined as those with a positive RT-PCR result or who were treated for COVID-19. Negative cases were defined as those with negative RT-PCR and an alternative diagnosis confirmed by an ID physician. Beyond March 23, almost all newly admitted patients were isolated.

Results
There were 111 COVID-19 positive and 71 COVID-19 negative patients. Of the COVID-19 patients, 61% were male and 39% female, 56% white, 20% Hispanic, 14% black, 9% Asian, 36% Jewish, 35% had diabetes mellitus (DM), 50% had hypertension and 42% had cardiovascular disease. Clinical symptoms, signs, and laboratory values for COVID-19 positive and negative groups were not significantly different. COVID-19 patients had significantly higher BMI (p = 0.001). On chest CT scan, bilateral or unilateral, peripheral distribution and lower lobar GGOs were over 80% specific for COVID-19. The frequency of GGOs was significantly higher when chest CT scans were done during the second week of illness compared to the first week (p = 0.0195). Jewish patients were associated with higher rates of death (p = 0.0475) and underlying DM was associated with higher rates of ARDS, AKI, intubation, ICU admission and death (p &lt; 0.05) compared to other demographic and comorbid groups.

Conclusion
Chest CT scan is an important component in the diagnostic process for patients with suspected COVID-19 infection, especially during the second week of symptoms. The findings may aid clinical decisions in the setting of a second surge of SARS-CoV-2.

Disclosures
All Authors: No reported disclosures
425. The Utility of Paired Upper and Lower COVID-19 Sampling in Patients with Artificial Airways
Authors: Kitt Eimear , Sammons Julia S , Chiotos Kathleen , Coffin Susan E , Coffin Susan E , Ballantine Allison , Gallagher Elizabeth , Mojica Coralee DelValle , O’Callaghan Kevin P , Kerman Caryn , Harris Rebecca , Bell Louis , Davis Daniela , Nord Ellen 
Published: January 2021

Abstract

Background
The Centers for Disease Control and Prevention (CDC) recommends upper respiratory tract (URT) polymerase chain reaction (PCR) testing as the initial diagnostic test for Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). Lower respiratory tract (LRT) testing for patients requiring mechanical ventilation is also recommended. The goal of this study was to evaluate concordance between paired URT and LRT specimens in children undergoing pre-admission/procedure screening or diagnostic testing. We hypothesized that &lt; 10% of paired tests would have discordant results.

Methods
Single center cross-sectional study including children with artificial airways who had paired URT and LRT SARS-CoV-2 PCR testing between 4/1/2020 and 6/8/2020. URT specimens included nasopharyngeal (NP) swabs and aspirates. LRT specimens included tracheal aspirates and bronchoalveolar lavages. URT and LRT specimens were classified as paired if the two specimens were collected within 24 hours. Artificial airways included tracheostomies and endotracheal tubes. Tests were classified as diagnostic versus screening based on the indication selected in the order.

Results
102 paired specimens were obtained during the study period. Fifty-nine were performed for screening and 43 were performed for diagnosis of suspected SARS-CoV-2. Overall, 94 specimens (92%) were concordant, including 89 negative from both sources and 5 positive from both sources. Eight specimens (8%) were discordant, all of which were positive from the URT and negative from the LRT (Figure 1). Among patients undergoing screening, 3 of 4 positive tests were discordant and among symptomatic patients, 5 of 9 positive tests were discordant. There were no instances of a positive LRT specimen with a negative URT specimen.
Figure 1. Performance of upper and lower respiratory tract SARS-CoV-2 PCR testing in children with artificial airways

Conclusion
Overall, most paired samples from the URT and LRT yielded concordant results with no pairs positive from the LRT and negative from the URT. These data support the CDC recommendation that URT specimens are the preferred initial SARS-CoV-2 test, while LRT specimens should be collected only from mechanically ventilated with suspected SARS-CoV-2.

Disclosures
All Authors: No reported disclosures
426. Use of Real Time IP-10 Measurements to Identify and Monitor the Dysregulated Immune Response in COVID-19 Patients
Authors: Lev Shaul , Gottesman Tamar , Levin Gal Shachaf , Ber Tahel Ilan , Angel Alon , Kellerman Lior , Barash Eran , Navon Roy , Simon Einav , Avni Noa , Hainrichson Mary , Gottlieb Tanya , Oved Kfir , Eden Eran , Tadmor Boaz 
Published: January 2021

Abstract

Background
It is estimated that up to 10% of SARS-CoV-2 patients progress from early and pulmonary stages to the most severe stage of illness, which manifests as an extra-pulmonary systemic hyperinflammatory syndrome. Interferon gamma-induced protein 10 (IP-10) is an inflammatory marker that plays a role in the dysregulated host response of COVID-19 infected patients. Clinical monitoring of IP-10 has been restricted in the absence of a rapid diagnostic test. MeMed KeyTM is a novel platform recently cleared to provide IP-10 measurements in 15 minutes. We hypothesized that providing physicians with real time IP-10 measurements would support detection and continuous monitoring of patients with a dysregulated immune response and potentially allow personalized immunomodulation to improve patient outcome.
IP-10 levels reflect corticosteroid treatment

Methods
From 7th April 2020 to 10th May 2020 blood was routinely collected serially from 52 SARS-CoV-2 positive patients hospitalized at a COVID-19 dedicated medical center. A clinical decision support protocol was in place focused on managing viral response, oxygenation and inflammatory state (NCT04389645).

Results
The median age of the 52 patients was 69, 69% were male, 21% were ventilated, 4 died, 2 due to non-COVID-19 related complications. The most common comorbidities were Diabetes 40% and Hypertension 46%. IP-10 &gt;1000 pg/ml correlated with ICU admission (p&lt; 0.05) and increased COVID-19 severity score (p&lt; 0.01). 19 of the 52 patients had IP-10 &gt;1000 pg/ml, of these 12 were treated with corticosteroids. Monitoring IP-10 within the clinical decision support protocol assisted with personalized corticosteroid regimens with the aim of reducing IP-10 &lt; 1000 pg/ml. The 10 patients that survived exhibited IP-10 levels &gt;1000 pg/ml for 2.6 days on average. In contrast, the 2 patients that died of COVID-19 related complications displayed an average of 7.5 days with IP-10 &gt;1000 pg/ml (p&lt; 0.05).

Conclusion
Providing physicians with real time measurements of IP-10 in COVID-19 patients proved a useful tool as part of the clinical decision support protocol. Timely identification, monitoring and personalized treatment of COVID-19 patients exhibiting a dysregulated immune response may aid in improving patient outcome. Further studies are warranted.

Disclosures
Tahel Ilan Ber, MD, MeMed (Employee) Alon Angel, n/a, MeMed (Employee) Lior Kellerman, M.D, MeMed (Employee) Eran Barash, MA, MeMed (Employee) Roy Navon, MSc, MeMed (Employee) Einav Simon, PhD, MeMed (Employee) Noa Avni, PhD, MeMed (Employee) Mary Hainrichson, PhD, MeMed (Employee) Tanya Gottlieb, PhD, MeMed (Employee) Kfir Oved, MD, PhD, MeMed (Employee) Eran Eden, PhD, MeMed (Employee)
427. Variation in SARS-CoV-2 molecular diagnostic test performance in symptomatic versus asymptomatic populations
Authors: Kanjilal Sanjat , Baker Meghan , Woolley Ann E , Rhee Chanu , Petrides Athena K , Kim Junghyun , Patel Rajesh , Zaffini Rebecca , Klompas Michael , Brigl Manfred 
Published: January 2021

Abstract

Background
Growing recognition of the importance of asymptomatic and pre-symptomatic transmission for severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has led to a substantial expansion of testing from symptomatic to asymptomatic patients, and particularly those with risk factors for infection. Viral burden in asymptomatic individuals can differ from symptomatic patients, which can impact test performance. We therefore evaluated the impact of expanded testing indications upon the sensitivity and specificity of molecular diagnostic assays for SARS-CoV-2.

Methods
We performed a retrospective review of laboratory results from 5,122 emergency room patients and inpatients tested for SARS-CoV-2 between 05/03/2020 and 06/13/2020 using the Hologic Panther Fusion and the Cepheid Xpert assays at the Brigham & Women’s Hospital in Boston, MA. Descriptive analyses were performed for trends in testing volume, rates of positivity and cycle thresholds (Cts) over time based on symptom status. We calculated the proportion of new diagnoses made on a patient’s first test as an indirect measure of sensitivity. We calculated the proportion of first tests that are positive with a Ct value &lt; 35 as an indirect measure of specificity.

Results
The overall rate of positivity over the study period was 8.7% (599/7,510 tests; 440/4,795 people) and declined by 1.8% (95% CI -2.2% - -1.4%, P&lt; 0.0001) each week. Relative to tests in symptomatic people, the asymptomatic population had a higher mean Ct value (35.1 vs 32.3; P &lt; 0.0001). Ct values increased by 0.7 (95% CI -0.1 - +1.4, P=0.07) and 0.8 (95% CI +0.3 - +1.4; p=0.01), sensitivity declined by 4% (95% CI -9% - +1%, P=0.08) and 12% (95% CI -20% - -5%, P-0.01) and specificity declined by 8% (95% CI -3% to 20%; P=0.13) and 9% (95% CI 7% - 11%; P=0.0002), over the time period of the study for asymptomatic and symptomatic patients, respectively.
Figure 1: Trends in Ct values by symptoms

Figure 2: Trends in diagnosis by first versus second test by symptoms

Figure 3: Trends in proportion of people with their first test having Ct &lt; 35 by symptoms

Conclusion
We show that the proportion of patients with low SARS-CoV-2 viral loads has increased as testing has expanded to the asymptomatic population and as transmission wanes in the community. This negatively impacts the performance of molecular assays by increasing the risk of false negatives and the detection of non-viable virus. Decision algorithms based on molecular assay results may need re-evaluation in light of these dynamics.

Disclosures
All Authors: No reported disclosures
430. Frontline doctors infected with Covid-19 during a hospital outbreak in Veracruz, Mexico
Authors: Del Carpio-Orantes Luis , Munguia-Sereno Alvaro Efren , Guerrero-Mancinas Zeltzin Olivia , Aparicio-Sánchez Elisa Estefanía , Segura-Rodríguez Orlando Israel , García-Hernández Omar , Escobar-Huerta Alejandro , Dorantes-Nava Claudia Leticia , Garcés-García Eustaquio , Ortiz-Espinoza Cirilo , Torres-Sánchez José Luis , López-Varela Luis Daniel , Pascual-Epigmenio Samuel , Márquez-Rodríguez Luis Alberto 
Published: January 2021

Abstract

Background
The current Covid-19 pandemic has affected health workers, some estimates mention more than 90,000 affected, even with deaths throughout the world.
Population characteristics

General symptoms

Methods
Descriptive, analytical and cross-sectional study. The cases of front-line doctors infected with Covid-19 during a hospitalary outbreak, in the General Hospital 71 “Lic. Benito Coquet Lagunes” of Veracruz, dependent on the Mexican Institute of Social Security, from April 1 to May 31, 2020.
Treatments

Results
Seven doctors were entered into the study, with an average age of 42.4 years, all of them male. The affectation by service was: Internal medicine 5 of 17 doctors (29.4%), Emergencies 1 of 15 doctors (6.6%) and Intensive care 1 of 6 doctors (16.6%) infected by Covid-19. Laboratory studies were only performed in 5 cases, the presence of leukopenia in 1 case (20%), leukocytosis in 2 cases (40%), lymphopenia in 4 cases (80%) stand out. Impaired fasting glucose was reported in all cases. There was no alteration in kidney function; in liver function, transaminemia was reported in 80%. Regarding the acute phase reactants, the intake of these was very inconsistent since it was not uniform in all cases, but the most representative was elevated ESR in 4 of 4 cases (100%), positive PCR in 3 of 4 cases (75%), procalcitonin negative in 3 of 3 cases (100%), elevated DHL in 2 of 5 cases (40%), elevated D-dimer in 1 of 3 cases (33.3%), elevated ferritinemia in 1 of 2 cases (50 %). The rest of the characteristics in the table and graphs.

Conclusion
The present cohort of doctors affected by Covid during a hospital outbreak shows that there are several factors to take into account, on the one hand, factors specific to the population (obesity, diabetes, hypertension), as well as the institutions that are in charge of medical personnel. they must identify the risk factors mentioned, influence them and protect said population that is vulnerable per se to a pandemic; Another constantly identified factor is occupational exposure to the pathogen without sufficient and adequate personal protective equipment.

Disclosures
All Authors: No reported disclosures
433. Mathematical Modeling of COVID-19 Transmission by a k Phases SEIR Model
Authors: Couto Braulio Roberto Gonçalves Marinho , Starling Carlos Ernesto Ferreira 
Published: January 2021

Abstract

Background
Mathematical models can provide insights on the spread of infectious diseases, such as the novel SARS-CoV-2 (COVID-19). This work applied a SEIR epidemiological compartmental model (susceptible-exposed-infected-recovered) with k phases to predict the actual spread of the COVID-19 virus.
Fig. 1 – SEIR model for COVID-19.

Methods
Four parameters of the SEIR model were obtained by international experiences: the incubation period = 3.7 in days, the proportion of critical cases = 0.05, the overall case-fatality rate = 0.023, and the asymptomatic proportion of COVID-19 = 0.18. The critical step in the prediction of COVID-19 by the model is the value of R0 (the basic reproduction number) and T_infectious (the infectious period, in days). R0 and T_infectious for each phase of the curve are calculated by mathematical constrained optimization, a numerical method. Differently from a statistical modelling, a numerical method is a type of mathematical modelling that is not dependent on a probability distribution. The objective function that measures the model error is minimized with respect to R0 and T_infectious in the presence of constraints on those variables. For R0, constraints are valid range of values (0.5 ≤ R0 ≤ 20). For T_infectious, constraints also are related to its range of values (2 ≤ T_infectious ≤ 14). A Solver from Excel or NEOS Server, for example, can be used for finding numerically minimum of a function Z, that represents the sum of absolute value of errors between COVID-19 new cases observed in one day, and COVID-19 cases predicted by the SEIR model (Fig. 2 and 3).
Fig. 2 - Mathematical Modeling of COVID-19 transmission by a SEIR model wiht three phases.

Fig.3 - Algorithm for the SEIR model applied to COVID-19 (calculation of new COVID-19 cases day-by-day).

Results
The ECDC has registered 8,142,129 COVID-19 in the world on Jun/17/2020. R0 and T_infectious calculated for a three phases curve in USA, with a stabilized scenario (Fig. 4: R0_1=1.0; T_infectious_1=2; R0_2=17.4; T_infectious_2=2; R0_3=1.0; T_infectious_3=14), a two phases curve in Brazil (Fig. 5: R0_1=8.0; T_infectious_1=9; R0_2=1.3; T_infectious_2=6), and a three phases model for France (Fig. 6: R0_1=4.3; T_infectious_1=11; R0_2=9.3; T_infectious_2=11; R0_3=0.5; T_infectious_3=12).
Fig. 4 - Three phases SEIR models for R0 and T_infectious that minimize the model error in predicting new COVID-19 cases day-by-day in USA.

Fig. 5 - Two phases SEIR models for R0 and T_infectious that minimize the model error in predicting new COVID-19 cases day-by-day in USA.

Fig. 6 - Two phases SEIR models for R0 and T_infectious that minimize the model error in predicting new COVID-19 cases day-by-day in USA.

Conclusion
The k phases SEIR model proved to be a useful to measure the COVID-19 transmission in a City, State or Country. More phases can be applied to fit a scenario with a new second COVID wave.

Disclosures
All Authors: No reported disclosures
434. Tracking COVID-19 in Real Time: Leveraging Public Data Sources to Inform Infection Prevention Practices
Authors: O’Callaghan Kevin P , Winser Kyle , Mehta Vaidehi , Kitt Eimear , Mojica Coralee DelValle , Kerman Caryn , Coffin Susan E , Coffin Susan E , Sammons Julia S 
Published: January 2021

Abstract

Background
Global spread of SARS-CoV-2 led to an urgent need for data on national and regional prevalence to inform public health policy. Healthcare systems were also in need of data to develop best practices around defining patient risk. We describe a data analytics tool developed at our institution which uses public data sources to track county-level prevalence of COVID-19 so as to delineate risk for individual patients.

Methods
We investigated a number of data sources tracking COVID-19 case counts, assessing for (1) frequency of updates, (2) granularity of geographic detail (optimally to zip-code or county) and (3) completeness of the data. We chose the Johns Hopkins University CSSE COVID-19 data set. This contains counts of new diagnoses per day by county using Federal Information Processing System (FIPS) codes. The dataset is updated daily with adjustments made for backdated corrections.
We developed a data analytics tool which allowed for direct comparison of county period prevalence. We developed a metric of 10-day rolling period prevalence calculated as a total case count from the preceding 10 days, divided by county population from 2018 American Community Survey (ACS) estimates.

Results
Benchmarking against local (peak of 3.12 cases per 1,000 persons) and regional prevalence, we set 6 cases/1,000 persons as the threshold for a Geographic Region with Widespread Community Transmission (GReWCoT). Counties have to reach this threshold for at least 4 out of 7 days within the period 3 to 10 days prior to the evaluation, to adjust for bulking of test results and delayed reporting.
We used the analytics tool to support a semimonthly review of geographic regions, and made specific recommendations for patients from qualifying regions including use of modified enhanced precautions (including surgical mask and eye protection), as well as restricted visitation of caregivers.
Figure 1. Epidemic curves for 10-day rolling period prevalence of COVID-19 in the Mid-Atlantic Region: Philadelphia County, PA

Figure 2. Epidemic curves for 10-day rolling period prevalence of COVID-19 in the Mid-Atlantic Region: Westchester County, NY

Figure 3. Epidemic curves for 10-day rolling period prevalence of COVID-19 in the Mid-Atlantic Region: Bergen County, NJ

Conclusion
This approach allowed for a nuanced investigation of COVID-19 prevalence in real-time, and provided support for risk stratification of patients throughout our large catchment area. The dashboard was shared on an inward-facing site to support staff messaging about regions of increased risk. Next steps include leveraging international data to inform a similar approach to international travel for our patients and staff.

Disclosures
All Authors: No reported disclosures