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.

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.