Cookies on this website
We use cookies to ensure that we give you the best experience on our website. If you click 'Continue' we'll assume that you are happy to receive all cookies and you won't see this message again. Click 'Find out more' for information on how to change your cookie settings.

Authors: J. Wang et al.

Link to paper:

Journal/ Pre-Print: medRxiv

Tags; Immunology/Immunity, Clinical/ Diagnostics

Research Highlights

1. Absolute counts of CD3+, CD4+ and CD8+ T cells were significantly lower whilst neutrophils were increased in moderate and severe cases, compared to mild cases.

2. CRP and IL-6 levels gradually decreased during recovery while T-lymphocyte subsets of severe patients gradually increase to moderate levels during recovery.

3. Numbers of CD3+ and CD4+ T cells can be used as predictive indicators of severity as shown by ROC curve analysis.


In a retrospective study, 16 COVID-19 patients were clustered by disease severity and analysed in terms of T cells subsets and inflammatory markers. The authors showed an association between reduced CD3+, CD4+, and CD8+ T cell counts and increased disease severity. CRP and IL-6 levels decreased during recovery and ROC curve analysis showed that CD3+ and CD4+ T cell counts could successfully predict disease severity in their study. IL-6 levels and lymphopenia in the context COVID-19 patients has been discussed previously in multiple publications. The novelty of this study was that it used this data to predict disease severity. However, a bigger sample size is needed to confirm this interesting finding.

Impact for SARS-CoV2/COVID19 research efforts

Adds to knowledge of clinical symptoms and pathogenesis of SARS-Cov2/COVID19

Confirms and extends previous finding regarding lymphopenia in SARS-CoV2/COVID19 positive individuals

Study Type

Patient Case study 

Strengths and limitations of the paper

Novelty: Using CD3+ and CD4+ counts as a prediction of disease severity

Standing in the field: These findings are consistent with what has been previously reported about inflammation markers and lymphopenia in COVID-19 patients. Generating possible predictions of disease severity is highly relevant as this could facilitate targeted, improved treatment.

Appropriate statistics: All statistical tests are adequately explained in the method section of the preprint

Viral model used: Patients (Yunnan Provincial Hospital of Infectious Disease) confirmed positive for SARS-CoV-2 by RT-qPCR.

Translatability: Could potentially be used to predict severity which could help improve treatment and reduce mortality.

Main limitations: Small sample size, not age or sex matched (range of age was 3-69). First, the study mentions which symptoms are found in each group, however, the study also mentions several symptoms and normal/abnormal CT imaging without any relation to a specific group. Patients were also treated with different and/or multiple therapies which is not accounted for in the data analysis. The study mentions the increase of T-lymphocytes in recovering patients and that lymphocytes of severely affected patients reach those of moderately affected patients. However, whilst there is some evidence o support ths, it would have been interesting to see a clearer time-course for changes in T-cells over time compared to a baseline. Therefore, healthy controls and a higher sample size is needed.