A web visualization tool using T cell subsets as the predictor to evaluate COVID-19 patient's severity

Abstract

Wuhan, China was the epicenter of the 2019 coronavirus outbreak. As a designated hospital for COVID-19, Wuhan Pulmonary Hospital has received over 700 COVID-19 patients. With the COVID-19 becoming a pandemic all over the world, we aim to share our epidemiological and clinical findings with the global community. We studied 340 confirmed COVID-19 patients with clear clinical outcomes from Wuhan Pulmonary Hospital, including 310 discharged cases and 30 death cases. We analyzed their demographic, epidemiological, clinical and laboratory data and implemented our findings into an interactive, free access web application to evaluate COVID-19 patient's severity level. Our results show that baseline T cell subsets results differed significantly between the discharged cases and the death cases in Mann Whitney U test: Total T cells (p < 0.001), Helper T cells (p <0.001), Suppressor T cells (p <0.001), and TH/TSC (Helper/Suppressor ratio, p<0.001). Multivariate logistic regression model with death or discharge as the outcome resulted in the following significant predictors: Age (OR 1.05, 95% CI, 1.00 to 1.10), underlying disease status (OR 3.42, 95% CI, 1.30 to 9.95), Helper T cells on the log scale (OR 0.22, 95% CI, 0.12 to 0.40), and TH/TSC on the log scale (OR 4.80, 95% CI, 2.12 to 11.86). The AUC for the logistic regression model is 0.90 (95% CI, 0.84 to 0.95), suggesting the model has a very good predictive power. Our findings suggest that while age and underlying diseases are known risk factors for poor prognosis, patients with a less damaged immune system at the time of hospitalization had higher chance of recovery. Close monitoring of the T cell subsets might provide valuable information of the patient's condition change during the treatment process. Our web visualization application can be used as a supplementary tool for the evaluation.