Using patient biomarker time series to determine mortality risk in hospitalised COVID-19 patients: A comparative analysis across two New York hospitals.

A large range of prognostic models for determining the risk of COVID-19 patient mortality exist, but these typically restrict the set of biomarkers considered to measurements available at patient admission. Additionally, many of these models are trained and tested on patient cohorts from a single hospital, raising questions about the generalisability of results. We used a Bayesian Markov model to analyse time series data of biomarker measurements taken throughout the duration of a COVID-19 patient's hospitalisation for n = 1540 patients from two hospitals in New York: State University of New York (SUNY) Downstate Health Sciences University and Maimonides Medical Center. Our main focus was to quantify the mortality risk associated with both static (e.g. demographic and patient history variables) and dynamic factors (e.g. changes in biomarkers) throughout hospitalisation, by so doing, to explain the observed patterns of mortality. By using our model to make predictions across the hospitals, we assessed how predictive factors generalised between the two cohorts. The individual dynamics of the measurements and their associated mortality risk were remarkably consistent across the hospitals. The model accuracy in predicting patient outcome (death or discharge) was 72.3% (predicting SUNY; posterior median accuracy) and 71.3% (predicting Maimonides) respectively. Model sensitivity was higher for detecting patients who would go on to be discharged (78.7%) versus those who died (61.8%). Our results indicate the utility of including dynamic clinical measurements when assessing patient mortality risk but also highlight the difficulty of identifying high risk patients.

HLA B53 is associated with a poor outcome in black COVID-19 patients.

A disproportionate incidence of death has occurred in African Americans (Blacks) in the United States due to COVID-19. The reason for this disparity is likely to be multi-factorial and may involve genetic predisposition. The association of human leukocyte antigens (HLA) with severe COVID-19 was examined in a hospitalized population (89% Black, n = 36) and compared to HLA typed non-hospitalized individuals (20% Black, n = 40) who had recovered from mild disease. For additional comparison, HLA typing data was available from kidney transplant recipients and deceased donors. Hospitalized patients were followed for 45 days after admission to our medical center with death as the primary end-point. One HLA allele, B53, appeared to be more prevalent in the hospitalized COVID-19 patients (percent of positive subjects, 30.5) compared to national data in US Black populations (percent of positive subjects, 24.5). The percent B53 positive in non-hospitalized COVID-19 patients was 2.6, significantly less than the percent positive in the hospitalized COVID-19 patients (p = 0.001, Fisher's exact test) and less than the 8 percent positive listed in national data bases for US Caucasian populations. Significantly greater deaths (73 percent) were observed in HLA B53 positive hospitalized COVID-19 patients compared to hospitalized COVID-19 patients who were B53 negative (40 percent). Multi-variate analysis indicated that HLA B53 positive Black hospitalized COVID-19 patients were at a 7.4 fold greater risk of death than Black COVID-19 patients who were B53 negative. Consideration for accelerated vaccination and treatment should be given to HLA B53 positive Black COVID19 patients.

Implementation of a pooled surveillance testing program for asymptomatic SARS-CoV-2 infections in K-12 schools and universities.

BACKGROUND: The negative impact of continued school closures during the height of the COVID-19 pandemic warrants the establishment of cost-effective strategies for surveillance and screening to safely reopen and monitor for potential in-school transmission. Here, we present a novel approach to increase the availability of repetitive and routine COVID-19 testing that may ultimately reduce the overall viral burden in the community. METHODS: We implemented a testing program using the SalivaClear࣪ pooled surveillance method that included students, faculty and staff from K-12 schools (student age range 5-18 years) and universities (student age range >18 years) across the country (Mirimus Clinical Labs, Brooklyn, NY). The data analysis was performed using descriptive statistics, kappa agreement, and outlier detection analysis. FINDINGS: From August 27, 2020 until January 13, 2021, 253,406 saliva specimens were self-collected from students, faculty and staff from 93 K-12 schools and 18 universities. Pool sizes of up to 24 samples were tested over a 20-week period. Pooled testing did not significantly alter the sensitivity of the molecular assay in terms of both qualitative (100% detection rate on both pooled and individual samples) and quantitative (comparable cycle threshold (Ct) values between pooled and individual samples) measures. The detection of SARS-CoV-2 in saliva was comparable to the nasopharyngeal swab. Pooling samples substantially reduced the costs associated with PCR testing and allowed schools to rapidly assess transmission and adjust prevention protocols as necessary. In one instance, in-school transmission of the virus was determined within the main office and led to review and revision of heating, ventilating and air-conditioning systems. INTERPRETATION: By establishing low-cost, weekly testing of students and faculty, pooled saliva analysis for the presence of SARS-CoV-2 enabled schools to determine whether transmission had occurred, make data-driven decisions, and adjust safety protocols. We provide strong evidence that pooled testing may be a fundamental component to the reopening of schools by minimizing the risk of in-school transmission among students and faculty. FUNDING: Skoll Foundation generously provided funding to Mobilizing Foundation and Mirimus for these studies.

Ephrin-A1 and the sheddase ADAM12 are upregulated in COVID-19.

More than 3.5 million people have died globally from COVID-19, yet an effective therapy is not available. It is, therefore, important to understand the signaling pathways that mediate disease progression in order to identify new molecular targets for therapeutic development. Here, we report that the blood serum levels of ephrin-A1 and the sheddase ADAM12 were significantly elevated in COVID-19 patients treated at SUNY Downstate Hospital of Brooklyn, New York. Both ephrin-A1 and ADAM12 are known to be involved in inflammation and regulate endothelial cell permeability, thus providing a gateway to lung injury. The clinical outcome correlated with the ephrin-A1 and ADAM12 serum levels during the first week of hospitalization. In contrast, the serum levels of TNFα were elevated in only a small subset of the patients, and these same patients also had highly elevated levels of the sheddase ADAM17. These data indicate that ephrin-A1-mediated inflammatory signaling may contribute to COVID-19 disease progression more so than TNFα-mediated inflammatory signaling. They also support the notion that, in COVID-19 inflammation, ADAM12 sheds ephrin-A1, while ADAM17 sheds TNFα. Furthermore, the results suggest that elevated serum levels and activity of cytokines, such as TNFα, and other secreted inflammatory molecules, such as ephrin-A1, are not simply due to overexpression, but also to upregulation of sheddases that release them into the blood circulation. Our results identify ephrin-A1, ADAM12, and other molecules in the ephrin-A1 signaling pathway as potential pharmacological targets for treating COVID-19 inflammation.

Correlation of Automated Chemiluminescent Method with Enzyme-Linked Immunosorbent Assay (ELISA) Antibody Titers in Convalescent COVID-19 Plasma Samples: Development of Rapid, Cost-Effective Semi-Quantitative Diagnostic Methods.

BACKGROUND: We investigated the utility of an automated chemiluminescent SARS-CoV-2 IgG antibody assay platform in quantifying the amount of binding antibodies present in donated convalescent plasma. METHODS: A total of 179 convalescent plasma units were analyzed for the presence of SARS-CoV-2 IgG antibodies using the Beckman-Coulter chemiluminescent immunoassay (CLIA) platform. The equipment-derived numerical values (S/Co ratio) were recorded. Aliquots from the same units were subjected to enzyme-linked immunosorbent assay (ELISA) that detects IgG antibodies against the receptor-binding domain (RBD) of the SARS-CoV-2 S1 protein. The relationship between ELISA titers and CLIA S/Co values was analyzed using linear regression and receiver operating characteristics (ROC) curve. RESULTS: Twenty-one samples (11.7%) had S/Co values of less than 1.0 and were deemed negative for antibodies and convalescent plasma had S/Co values between >1.0 and 5.0 (70/179, 39.1%). Fifteen units (8.4%) had negative ELISA titer. The majority of the units (95/179. 53.1%) had titers ≥1:1024. The sensitivities of ELISA to CLIA were comparable (90.5% vs 88.3%, respectively; p=0.18). There was positive linear correlation between CLIA S/Co values and ELISA IgG titer (Rho = 0.75; Spearman's rank = 0.82, p-value = <0.0001). The agreement between the two methods was fair, with a κ index of 0.2741. Using the ROC analysis, we identified a CLIA S/Co cutoff value of 8.2, which gives a sensitivity of 90% and a specificity of 82% in predicting a titer dilution of ≥1:1024. CONCLUSION: The utility of automated antibody detection systems can be extended from simply a screening method to a semi-quantitative and quantitative functional antibody analysis. CLIA S/Co values can be used to reliably estimate the ELISA antibody titer. Incorporation of chemiluminescent-based methods can provide rapid, cost-effective means of identifying anti-SARS-CoV-2 antibody titers in donated plasma for use in the treatment of COVID-19 infection.

A Dynamic Bayesian Model for Identifying High-Mortality Risk in Hospitalized COVID-19 Patients.

As Coronavirus Disease 2019 (COVID-19) hospitalization rates remain high, there is an urgent need to identify prognostic factors to improve patient outcomes. Existing prognostic models mostly consider the impact of biomarkers at presentation on the risk of a single patient outcome at a single follow up time. We collected data for 553 Polymerase Chain Reaction (PCR)-positive COVID-19 patients admitted to hospital whose eventual outcomes were known. The data collected for the patients included demographics, comorbidities and laboratory values taken at admission and throughout the course of hospitalization. We trained multivariate Markov prognostic models to identify high-risk patients at admission along with a dynamic measure of risk incorporating time-dependent changes in patients' laboratory values. From the set of factors available upon admission, the Markov model determined that age >80 years, history of coronary artery disease and chronic obstructive pulmonary disease increased mortality risk. The lab values upon admission most associated with mortality included neutrophil percentage, red blood cells (RBC), red cell distribution width (RDW), protein levels, platelets count, albumin levels and mean corpuscular hemoglobin concentration (MCHC). Incorporating dynamic changes in lab values throughout hospitalization lead to dramatic gains in the predictive accuracy of the model and indicated a catalogue of variables for determining high-risk patients including eosinophil percentage, white blood cells (WBC), platelets, pCO2, RDW, large unstained cells (LUC) count, alkaline phosphatase and albumin. Our prognostic model highlights the nuance of determining risk for COVID-19 patients and indicates that, rather than a single variable, a range of factors (at different points in hospitalization) are needed for effective risk stratification.

Administration of high titer convalescent anti-SARS-CoV-2 plasma: From donor selection to monitoring recipient outcomes.

Early in the SARS-CoV-2 pandemic, convalescent plasma (CP) therapy was proposed as a treatment for severely ill patients. We conducted a CP treatment protocol under the Mayo Clinic Extended Access Program at University Hospital Brooklyn (UHB). Potential donors were screened with a lateral flow assay (LFA) for IgM and IgG antibodies against the SARS-CoV-2 S1 receptor-binding domain (RBD). Volunteers that were LFA positive were tested with an ELISA to measure IgG titers against the RBD. Subjects with titers of at least 1:1024 were selected to donate. Most donors with positive LFA had acceptable titers and were eligible to donate. Out of 171 volunteers, only 65 tested positive in the LFA (38.0%), and 55 (32.2%) had titers of at least 1:1024. Before our donation program started, 31 CP units were procured from the New York Blood Center (NYBC). Among the 31 CP units that were obtained from the NYBC, 25 units (80.6%) were positive in the LFA but only 12 units (38.7%) had titers of at least 1:1024. CP was administered to 28 hospitalized COVID-19 patients. Patients who received low titer CP, high titer CP and patients who did not receive CP were followed for 45 days after presentation. Severe adverse events were not associated with CP transfusion. Death was a less frequent outcome for patients that received high titer CP (>1:1024) 38.6% mortality, than patients that received low titer CP (≤1:1024) 77.8% mortality.