Predictive Modeling of Poor Outcome in Severe COVID-19: A Single-Center Observational Study Based on Clinical, Cytokine and Laboratory Profiles

Abstract

Pneumonia is the main cause of hospital admission in COVID-19 patients. We aimed to perform an extensive characterization of clinical, laboratory, and cytokine profiles in order to identify poor outcomes in COVID-19 patients. Methods: A prospective and consecutive study involving 108 COVID-19 patients was conducted between March and April 2020 at Hospital Clínico Universitario de Valladolid (Spain). Plasma samples from each patient were collected after emergency room admission. Forty-five serum cytokines were measured in duplicate, and clinical data were analyzed using SPPS version 25.0. Results: A multivariate predictive model showed high hepatocyte growth factor (HGF) plasma levels as the only cytokine related to intubation or death risk at hospital admission (OR = 7.38, 95%CI—(1.28–42.4), p = 0.025). There were no comorbidities included in the model except for the ABO blood group, in which the O blood group was associated with a 14-fold lower risk of a poor outcome. Other clinical variables were also included in the predictive model. The predictive model was internally validated by the receiver operating characteristic (ROC) curve with an area under the curve (AUC) of 0.94, a sensitivity of 91.7% and a specificity of 95%. The use of a bootstrapping method confirmed these results. Conclusions: A simple, robust, and quick predictive model, based on the ABO blood group, four common laboratory values, and one specific cytokine (HGF), could be used in order to predict poor outcomes in COVID-19 patients.

Keywords

• COVID-19
• HGF
• diagnosis
• cytokines
• biomarker
• validation