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

Area of Science:

• Immunology
• Infectious Diseases
• Epidemiology

Background:

• Wuhan, China, was the epicenter of the 2019 coronavirus (COVID-19) outbreak.
• Wuhan Pulmonary Hospital managed over 700 COVID-19 patients.
• Sharing clinical and epidemiological findings is crucial during the global pandemic.

Purpose of the Study:

• To analyze epidemiological and clinical data of COVID-19 patients.
• To identify predictors of patient outcomes (discharge vs. death).
• To develop a web application for evaluating COVID-19 patient severity.

Main Methods:

• Studied 340 confirmed COVID-19 patients with known outcomes.
• Analyzed demographic, epidemiological, clinical, and laboratory data.
• Utilized Mann Whitney U test and multivariate logistic regression.
• Developed an interactive web application for severity evaluation.

Main Results:

• Significant differences in T cell subsets (Total T cells, Helper T cells, Suppressor T cells, TH/TSC ratio) between discharged and deceased patients (p < 0.001).
• Key predictors of outcome included age, underlying disease status, Helper T cells, and TH/TSC ratio.
• The logistic regression model demonstrated high predictive power (AUC = 0.90).

Conclusions:

• Age and underlying diseases are risk factors for poor COVID-19 prognosis.
• Preserved T cell subsets at hospitalization indicate a higher chance of recovery.
• T cell subset monitoring offers valuable insights into patient condition and treatment response.