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

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

The authors report no funding.

SciScore for 10.1101/2020.04.06.20056127: (What is this?)

Please note, not all rigor criteria are appropriate for all manuscripts.

Table 1: Rigor

Institutional Review Board Statement	IRB: Ethics approval: The study protocol was reviewed and approved by the Ethics committee of Wuhan Pulmonary Hospital (WPE
Randomization	We have also uploaded a dummy date set with de-identified and randomly modified patient data, as well as all the source code used for the current analysis as well as the interactive web application to: https://github.com/mindy-fang/COVID-19.
Blinding	not detected.
Power Analysis	Meanwhile we will keep updating our reference panel as we include more patients, so that the algorithm would gain more and more statistical power over time.
Sex as a biological variable	Statistical analysis results and the interactive web tool: Among the 310 discharged cases, 155 were male and 155 were female.

Table 2: Resources

No key resources detected.

Results from OddPub: Thank you for sharing your code and data.

Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

Our algorithm has the following limitations: It may not predict accurately for younger patients, or patients with no symptoms, since our training data contains relatively old and severe patients; Secondly, the model was built based on a sample size of 340, which is not a large number. But we will keep updating our web application as we collect more data. In addition, the reference panel used in our analysis were infected population in Wuhan, China. Although there was no clear evidence that the underlying mechanism of the T cell depletion under the COVID-19 infection is similar or different across ethnicity groups, it is only natural to assume that the baseline values and the degree of T cell depletion would be slightly different. We found that the T-lymphocytes, B-lymphocytes, and NK cells did differ among people in different regions28-30. We encourage researchers around the world to download the source code and customize it with their own data, as they accumulate more experience and knowledge with patients from their own hospitals or regions. To end our writing with the most recent update (March 13, 2020), the epidemic in Wuhan has gradually passed its peak. All the cabin hospitals were closed, and other hospitals in Wuhan started to return to their normal track. Medical volunteer teams have returned to their home cities. We give thanks to all the support that we have received during our most difficult time and hope the situation would improve quickly for other parts of the ...

Results from TrialIdentifier: No clinical trial numbers were referenced.

Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

Results from JetFighter: We did not find any issues relating to colormaps.

Results from rtransparent:

Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
No protocol registration statement was detected.

Read the original source