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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

41 Pages Posted: 1 Apr 2020

See all articles by Qibin Liu

Qibin Liu

Wuhan Institute for Tuberculosis Control - Wuhan Pulmonary Hospital

Xuemin Fang

Kanagawa University of Human Services - Graduate School of Health Innovation

Shinichi Tokuno

Kanagawa University of Human Services - Graduate School of Health Innovation

Ungil Chung

Kanagawa University of Human Services - Graduate School of Health Innovation

Xianxiang Chen

Wuhan Institute for Tuberculosis Control - Wuhan Pulmonary Hospital

Xiyong Dai

Wuhan Institute for Tuberculosis Control - Wuhan Pulmonary Hospital

Xiaoyu Liu

Wuhan Institute for Tuberculosis Control - Wuhan Pulmonary Hospital

Feng Xu

Wuhan Institute for Tuberculosis Control - Wuhan Pulmonary Hospital

Bing Wang

Wuhan Institute for Tuberculosis Control - Wuhan Pulmonary Hospital

Peng Peng

Wuhan Institute for Tuberculosis Control - Department of Hospital Affairs

More...

Abstract

Background: Wuhan, 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.

Methods: In 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.

Findings: Baseline T lymphocyte Subsets differed significantly between the discharged cases and the death cases in two-sample t-tests: Total T cells (p < 2·2e-16), Helper T cells (p < 2·2e-16), Suppressor T cells (p = 1·8-14), and TH/TS (Helper/Suppressor ratio, p = 0·0066). Multivariate logistic regression model with death or discharge as the outcome resulted in the following significant predictors: age (OR 1·05, p 0·04), underlying disease status (OR 3·42, p 0·02), Helper T cells on the log scale (OR 0·22, p 0·00), and TH/TS on the log scale (OR 4·80, p 0·00). The McFadden pseudo R-squared for the logistic regression model is 0·35, suggesting the model has a fair predictive power.

Interpretation: 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 lymphocyte 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.

Funding Statement: The authors report no funding.

Declaration of Interests: The named authors have no conflict of interest, financial or otherwise.

Ethics Approval Statement: The study protocol was reviewed and approved by the Ethics committee of Wuhan Pulmonary Hospital (WPE 2020-8). Informed consents were obtained from all participants before enrollment. Patient records and information were de-identified prior to the analysis.

Keywords: COVID-19; coronavirus; clinical characteristics; outcome; prognosis; T lymphocyte; logistic regession; interactive web app

Suggested Citation

Liu, Qibin and Fang, Xuemin and Tokuno, Shinichi and Chung, Ungil and Chen, Xianxiang and Dai, Xiyong and Liu, Xiaoyu and Xu, Feng and Wang, Bing and Peng, Peng, 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 (3/18/2020). Available at SSRN: https://ssrn.com/abstract=3557995 or http://dx.doi.org/10.2139/ssrn.3557995

Qibin Liu

Wuhan Institute for Tuberculosis Control - Wuhan Pulmonary Hospital ( email )

China

Xuemin Fang

Kanagawa University of Human Services - Graduate School of Health Innovation ( email )

Kawasaki City, Kanagawa 210-0821
Japan

Shinichi Tokuno

Kanagawa University of Human Services - Graduate School of Health Innovation ( email )

Kawasaki City, Kanagawa 210-0821
Japan

Ungil Chung

Kanagawa University of Human Services - Graduate School of Health Innovation ( email )

Kawasaki City, Kanagawa 210-0821
Japan

Xianxiang Chen

Wuhan Institute for Tuberculosis Control - Wuhan Pulmonary Hospital ( email )

China

Xiyong Dai

Wuhan Institute for Tuberculosis Control - Wuhan Pulmonary Hospital ( email )

China

Xiaoyu Liu

Wuhan Institute for Tuberculosis Control - Wuhan Pulmonary Hospital ( email )

China

Feng Xu

Wuhan Institute for Tuberculosis Control - Wuhan Pulmonary Hospital ( email )

China

Bing Wang

Wuhan Institute for Tuberculosis Control - Wuhan Pulmonary Hospital ( email )

China

Peng Peng (Contact Author)

Wuhan Institute for Tuberculosis Control - Department of Hospital Affairs ( email )

Wuhan
China

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