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Prediction of the clinical outcome of COVID-19 patients using T lymphocyte subsets with 340 cases from Wuhan, China: a retrospective cohort study and a web visualization tool

By Qibin Liu, Xuemin Fang, Shinichi Tokuno, Ungil Chung, Xianxiang Chen, Xiyong Dai, Xiaoyu Liu, Feng Xu, Bing Wang, Peng Peng

Posted 11 Apr 2020
medRxiv DOI: 10.1101/2020.04.06.20056127

BackgroundWuhan, China was the epicenter of the 2019 coronavirus outbreak. As a designated hospital, 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. MethodsIn this retrospective cohort study, we studied 340 confirmed COVID-19 patients 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. FindingsBaseline T lymphocyte Subsets differed significantly between the discharged cases and the death cases in two-sample t-tests: Total T cells (p < 2{middle dot}2e-16), Helper T cells (p < 2{middle dot}2e-16), Suppressor T cells (p = 1{middle dot}8-14), and TH/TS (Helper/Suppressor ratio, p = 0{middle dot}0066). Multivariate logistic regression model with death or discharge as the outcome resulted in the following significant predictors: age (OR 1{middle dot}05, p 0{middle dot}04), underlying disease status (OR 3{middle dot}42, p 0{middle dot}02), Helper T cells on the log scale (OR 0{middle dot}22, p 0{middle dot}00), and TH/TS on the log scale (OR 4{middle dot}80, p 0{middle dot}00). The McFadden pseudo R-squared for the logistic regression model is 0{middle dot}35, suggesting the model has a fair predictive power. InterpretationWhile 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 lymphocyte subsets might provide valuable information of the patients condition change during the treatment process. Our web visualization application can be used as a supplementary tool for the evaluation. FundingThe authors report no funding.

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