About: 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

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: 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 Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	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
Creator	Peng, Peng Chen, Xianxiang Xu, Feng Liu, Xiaoyu Wang, Bing »more»
source	MedRxiv
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 patients condition change during the treatment process. Our web visualization application can be used as a supplementary tool for the evaluation.
has issue date	2020-04-11(xsd:dateTime)
bibo:doi	10.1101/2020.04.06.20056127
has license	medrxiv
sha1sum (hex)	0f721af678b7d9b8abb4b6446af908e6f19705d2
schema:url	https://doi.org/10.1101/2020.04.06.20056127
resource representing a document's title	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
resource representing a document's body	covid:0f721af678b7d9b8abb4b6446af908e6f19705d2#body_text
is schema:about of	named entity 'Hospital' named entity 'China' named entity 'patients' named entity 'CHINA' named entity 'PULMONARY' »more»

Faceted Search & Find service v1.13.91 as of Mar 24 2020

Alternative Linked Data Documents: Sponger | ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3229 as of Jul 10 2020, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (94 GB total memory)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2025 OpenLink Software