Longitudinal hematologic and immunologic variations associated with the progression of COVID-19 patients in China

Chen, Ruchong; Sang, Ling; Jiang, Mei; Yang, Zujun; Jia, Nan; Fu, Wanyi; Xie, Jiaxing; Guan, Weijun; Liang, Wenhua; Ni, Zhengyi; Hu, Yu; Liu, Lei; Shan, Hong; Lei, Chunliang; Peng, Yuchen; Li, Wei; Liu, Yong; Hu, Yahua; Peng, Peng; Wang, Jianming; Liu, Jiyang; Chen, Zhong; Li, Gang; Zheng, Zhijian; Qiu, Shao-qin; Luo, Jie; Ye, Changjiang; Zhu, Shaoyong; Zheng, Jinping; Zhang, Nuofu; Li, Yimin; He, Jianxing; Li, Jing; Li, Shiyue; Zhong, Nanshan

doi:10.1016/j.jaci.2020.05.003

Cited by 329 publications

(407 citation statements)

References 51 publications

Supporting

Mentioning

356

Contrasting

Unclassified

Order By: Relevance

“…CD4+ and/or CD8+ T‐cell counts from COVID‐19 patients with different disease severity status were reported in all 20 publications, and 10 of them also included CD19+ B cell and CD16 + CD56+ NK cell counts. These 20 peer‐reviewed publications were selected for meta‐analysis in this brief report (6‐25).…”

Section: Methodsmentioning

confidence: 99%

Lymphocyte Subset Counts in COVID‐19 Patients: A Meta‐Analysis

et al. 2020

View full text Add to dashboard Cite

A reduced peripheral blood absolute lymphocyte count with an elevated neutrophil count has been a consistent observation in hospitalized coronavirus disease 2019 (COVID‐19) patients. In this brief meta‐analysis, the reduction of lymphocyte subset counts in COVID‐19 patients was investigated across 20 peer‐reviewed studies meeting criteria for reporting lymphocyte subset counts and COVID‐19 disease severity. CD4+ T cell, CD8+ T cell, B cell, NK cell, and total lymphocyte cell counts all showed statistically significant reduction in patients with severe/critical COVID‐19 disease compared to mild/moderate disease. T‐cell subsets showed the largest standardized magnitude of change. In some studies, multivariate analysis has shown that CD4 and/or CD8 T‐cells counts are independently predictive of patient outcomes. © 2020 International Society for Advancement of Cytometry

show abstract

Section: Methodsmentioning

confidence: 99%

Lymphocyte Subset Counts in COVID‐19 Patients: A Meta‐Analysis

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Fig 2g). However, no signi cant differences were found in other indicators, CD4 + /CD8 + ratio (MD, 0.17; 95%CI, -0.14 to 0.49; I 2 , 98%; p<0.001, Fig 2d) and Treg cell (×10 6 , MD, -0.13; 95%CI, -1.40 to 1.14; I2, 90%; p<0.001, Fig 2e).…”

Section: Data Synthesis and Analysismentioning

confidence: 87%

Association of immunological features with COVID-19 severity: a systematic review and meta-analysis

Zhang

Chen

et al. 2020

Preprint

View full text Add to dashboard Cite

Abstract BackgroundWe aim to explore the association of immunological features with COVID-19 severity.MethodsWe conducted a meta-analysis to estimate mean difference (MD) of immune cells and cytokines levels with COVID-19 severity in PubMed and Web of Science.ResultsA total of 16 studies with 1689 COVID-19 patients were included. Compared with mild cases, severe cases showed significantly lower levels of immune cells, CD3+ T cell (× 106, MD, -413.87; 95%CI, -611.39 to -216.34), CD4+ T cell (× 106, MD, -225.89; 95%CI, -306.36 to -145.43), CD8+ T cell (× 106, MD, -138.59; 95%CI, -176.36 to -100.82), B cell (× 106/L; MD, -23.87; 95%CI, -43.97 to -3.78) and NK cell (× 106/L; MD, -57.12; 95%CI, -81.18 to -33.06), and significantly higher levels of cytokines, TNF-α (pg/ml; MD, 0.34; 95%CI, 0.09 to 0.59), IL-5 (pg/ml; MD, 14.2; 95%CI, 3.99 to 24.4), IL-6 (pg/ml; MD, 13.07; 95%CI, 9.80 to 16.35), and IL-10 (pg/ml; MD, 2.04; 95%CI, 1.32 to 2.75). However, no significant differences were found in other indicators, IFN-γ (pg/ml; MD, 0.26; 95%CI, -0.05 to 0.56), IL-2 (pg/ml; MD, 0.05; 95%CI, -0.49 to 0.60), IL-4 (pg/ml; MD, -0.03; 95%CI, -0.68 to 0.62), Treg cell (× 106, MD, -0.13; 95%CI, -1.40 to 1.14), and CD4+/CD8+ ratio (MD, 0.17; 95%CI, -0.14 to 0.49).ConclusionOur meta-analysis revealed significant lower levels in immune cells (CD3+ T, CD4+ T, CD8+ T, B and NK cells) and significant higher levels in cytokines (TNF-α, IL-5, IL-6 and IL-10) in severe cases compared with mild cases of COVID-19. Measurement of immunological features could help to assess disease severity for effective triage of COVID-19 patients.

show abstract

“…Intensifying multidisciplinary treatments, such as enhanced nutritional support, anticoagulation (low molecular weight heparin [LMWH]), anti-in ammatory (γ-globulin, etc. ), respiratory support (mechanical ventilation), and replacement therapy (continuous renal replace therapy [CRRT]) are adopted to save lives of critical COVID-19 patients 1,29,30 . But the implementation of the above-mentioned intensive treatments usually started after the occurrence of critical illness.…”

Section: Discussionmentioning

confidence: 99%

Predictive Risk Factors at Admission and a "Burning Point" During Hospitalization Serve as Sequential Alerts for Critical Illness in COVID-19 Patients

Zhou

Wang

et al. 2020

Preprint

View full text Add to dashboard Cite

Background In critically ill COVID-19 patients, the crucial turning point before critical illness onset (CIO) remain largely unknown, and the combination of baseline risk factors with the turning point during hospitalization was rarely reported.Methods In this retrospective cohort study, 1150 consecutively admitted patients with confirmed COVID-19 were enrolled, including 296 critical and 854 non-critical patients. We compared the differences of all the clinically tested indicators and their dynamic changes between critical and non-critical patients. Three prediction models were established and validated based on the risk factors at admission, and an online baseline predictive tool was developed. Linear mixed model (LMM) was applied for longitudinal data analysis in 296 critical patients throughout the hospitalization, to predict the likelihood and possible time of critical illness in COVID-19 patients. A crucial turning point, where several indicators will experience a greater and significantly continuous change before CIO, was defined as “burning point” in our study. This point indicates the deterioration of patient’s condition before CIO.Results We established a novel two-checkpoint system to predict critical illness for COVID-19 patients in which the first checkpoint happened at patient admission was assessed by a baseline prediction model to project the likelihood of critical illness based on the variables selected from random forest and LASSO regression analysis, including age, SOFA score, neutrophil-to-lymphocyte ratio (NLR), D-dimer, lactate dehydrogenase (LDH), International Normalized Ratio (INR), and pneumonia area derived from CT images, which yields an AUC of 0.960 (95% confidence interval, 0.941-0.972) and 0.958 (0.936-0.980) in the training and testing sets, respectively. This model has been translated into a public web-based risk calculator. Furthermore, the second checkpoint (designated as “burning point” in our study) could be identified as early as 5 days preceding the CIO, and 12 (IQR, 7-17) days after illness onset. Seven most significant and representative “burning point” indicators were SOFA score, NLR, C-reactive protein (CRP), glucose, D-dimer, LDH, and blood urea nitrogen (BUN).Conclusions With this two-checkpoint prediction system, the deterioration of COVID-19 patients could be early identified and more intensive treatments could be started in advance to reduce the incidence of critical illness.

show abstract

Longitudinal hematologic and immunologic variations associated with the progression of COVID-19 patients in China

Cited by 329 publications

References 51 publications

Lymphocyte Subset Counts in COVID‐19 Patients: A Meta‐Analysis

Lymphocyte Subset Counts in COVID‐19 Patients: A Meta‐Analysis

Association of immunological features with COVID-19 severity: a systematic review and meta-analysis

Predictive Risk Factors at Admission and a "Burning Point" During Hospitalization Serve as Sequential Alerts for Critical Illness in COVID-19 Patients

Contact Info

Product

Resources

About