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.

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.

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.

Time-Series Analysis of Laboratory Values in the Context of Long-Term Hospitalized Patient Mortality.

Objectives: Long-term hospitalized patients have a higher risk of adverse outcomes and mortality rate. These patients often rapidly deteriorate, leading to death. We aim to evaluate end-of-life laboratory values time trends among deceased long-term inpatients.