Characteristics of lymphocyte subsets and their predicting values for the severity of COVID-19 patients

Authors: J. Wang et al.

Link to paper: https://www.medrxiv.org/content/10.1101/2020.05.01.20086421v2

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.

Summary 

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.