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Leucocyte Subsets Effectively Predict the Clinical Outcome of Patients with COVID-19 Pneumonia: A Retrospective Case-Control Study

21 Pages Posted: 1 Apr 2020

See all articles by Zhiquan Hu

Zhiquan Hu

Huazhong University of Science and Technology (Formerly Tongi Medical University) - Department of Urology

Jiahua Gan

Huazhong University of Science and Technology (Formerly Tongi Medical University) - Department of Urology

Jingjing Li

Huazhong University of Science and Technology (Formerly Tongi Medical University) - Department of Clinical Laboratory

Chunjin Ke

Huazhong University of Science and Technology (Formerly Tongi Medical University) - Department of Urology

Jihua Tian

Huazhong University of Science and Technology (Formerly Tongi Medical University) - Department of Urology

Le Li

Huazhong University of Science and Technology (Formerly Tongi Medical University) - Department of Urology

Xing Zeng

Huazhong University of Science and Technology (Formerly Tongi Medical University) - Department of Urology

Chunguang Yang

Huazhong University of Science and Technology (Formerly Tongi Medical University) - Department of Urology

More...

Abstract

Background: Clinical characteristics evaluation and risk factors identification of the coronavirus disease 2019 (COVID-19) have been well studied, while effective predictors for clinical outcome and research on underlying mechanism are limited.

Methods: Hospitalized COVID-19 pneumonia patients with definitive clinical outcome (cured or died) were retrospectively studied. The diagnostic performance of the leucocyte subsets and other parameters were compared using the area under the receiver operating characteristic curve value (AUC). Further, the correlations between leucocyte subsets and inflammation related factors associated with clinical outcome were subsequently investigated.

Findings: Among 95 subjects included, 56 patients were cured and 39 cases died. Older age, higher leucocytes count or neutrophil count, lower albumin levels, higher serum LDH levels, higher BUN levels, higher PT indicated poor outcome in patients with COVID-19 pneumonia. Lymphocyte subset (Lymphocyte, T cells, Th cells, Ts cells, NK cells, T cells+B cells+NK cells) counts were positively associated with cure rate (AUC: 0.777, P<0.001; AUC: 0.925, P<0.001; AUC: 0.900, P<0.001; AUC: 0.902, P<0.001; AUC: 0.877, P = 0.001; AUC: 0.918, P <0.001, resp.). Neutrophil-to-lymphocyte ratio (NLR), neutrophil to T lymphocyte count ratio (NTR), neutrophil percentage to T lymphocyte ratio (NpTR) effectively predicted the mortality (AUC: 0.900, P<0.001; AUC: 0.905, P<0.001; AUC: 0.932, P <0.001, resp.). Binary logistic regression showed that NpTR was independent prognostic factors for mortality. Serum IL6 level was positively correlated with lymphocyte count, neutrophil count and eosinophil count, while negatively correlated with lymphocyte count.

Interpretation: These results indicate that leucocyte subsets predict the clinical outcome of patients with COVID-19 pneumonia in high efficiency. Non-self-limiting inflammatory response is involved in the development of fatal pneumonia.

Funding Statement: This work was supported by National Natural Science Foundation of China (No.81702989).

Declaration of Interests: The authors declare no conflict of interest.

Ethics Approval Statement: This study was reviewed and approved by the ethics committee of Tongji Hospital of Huazhong University of Science and Technology (IRB ID: TJ- IRB20200343).

Keywords: SARS-CoV-2; COVID-19; Coronavirus; Prognostic value; Leucocyte; Cytokine

Suggested Citation

Hu, Zhiquan and Gan, Jiahua and Li, Jingjing and Ke, Chunjin and Tian, Jihua and Li, Le and Zeng, Xing and Yang, Chunguang, Leucocyte Subsets Effectively Predict the Clinical Outcome of Patients with COVID-19 Pneumonia: A Retrospective Case-Control Study (3/20/2020). Available at SSRN: https://ssrn.com/abstract=3559566 or http://dx.doi.org/10.2139/ssrn.3559566

Zhiquan Hu

Huazhong University of Science and Technology (Formerly Tongi Medical University) - Department of Urology

China

Jiahua Gan

Huazhong University of Science and Technology (Formerly Tongi Medical University) - Department of Urology

China

Jingjing Li

Huazhong University of Science and Technology (Formerly Tongi Medical University) - Department of Clinical Laboratory

China

Chunjin Ke

Huazhong University of Science and Technology (Formerly Tongi Medical University) - Department of Urology

China

Jihua Tian

Huazhong University of Science and Technology (Formerly Tongi Medical University) - Department of Urology

China

Le Li

Huazhong University of Science and Technology (Formerly Tongi Medical University) - Department of Urology

China

Xing Zeng

Huazhong University of Science and Technology (Formerly Tongi Medical University) - Department of Urology

China

Chunguang Yang (Contact Author)

Huazhong University of Science and Technology (Formerly Tongi Medical University) - Department of Urology ( email )

China

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