Abstract
Background:
The conclusions about the relationship between eosinophil counts and the severity of coronavirus disease 2019 (COVID-19) were controversial, so we updated the evidences and reassessed it.
Methods:
We searched the PubMed, Cochrane library, Excerpta Medica Database, and Web of Science to compare the eosinophil counts about non-severe disease group (mild pneumonia, moderate pneumonia, non-critical disease and recovery group) and severe disease group (severe pneumonia, critical pneumonia, critical disease and death group) in COVID-19.
Results:
A total of 1228 patients from 10 studies were included. Compared with non-severe group, severe group had strikingly lower average eosinophil counts (SMD 0.65, 95% confidence intervals [CI] 0.29–1.01; P < .001). The result of subgroup analysis of different countries showed SMD 0.66, 95% CI 0.26–1.06; P < .001. Another subgroup analysis between mild-moderate pneumonia versus severe-critical pneumonia showed SMD 0.69, 95% CI 0.25–1.13; P < .001, and no significant risk of publication bias (Begg test 0.063 and Egger test 0.057) in this subgroup. The heterogeneity was substantial, but the sensitivity analyses showed no significant change when individual study was excluded, which suggested the crediblity and stablity of our results.
Conclusions:
The eosinophil counts had important value as an indicator of severity in patients with COVID-19.
PROSPERO registration number:
CRD42020205497.
Keywords: coronavirus disease 2019, eosinophils, non-severe disease, severe disease, meta-analysis
1. Introduction
A new form of respiratory and systemic disorder named coronavirus disease 2019 (COVID-19) is caused by the severe acute respiratory syndrome coronavirus 2,[1,2] a new and more infectious virus than that of severe acute respiratory syndrome and Middle East respiratory syndrome. The clinical symptoms of COVID-19 are fever, cough, fatigue, extremity pain, and gastrointestinal symptoms.[3] Severe patients often experience progressive dyspnea and/or refractory hypoxemia 1 week after onset, and rapidly develop acute respiratory distress syndrome, septic shock, multiple organ failure and other manifestations.[4,5] There is no doubt that it has become one of the most concerned issues today because of its extremely high transmission capacity and mortality rate. According to Guidelines for the Diagnosis and Treatment of COVID-19, the clinical typing of COVID-19 are 4 types: mild, moderate, severe and critical pneumonia. So far, over 64 million people has infected and caused over 1,480,000 deaths (as of December 2, 2020) over the world. Since it breaks out, the global economy is badly disrupted and global health care systems is suffered a multitude of challenges.
One of the first lines to against virus, the eosinophil should be noticed.[6] A previous systematic literature included 3 studies showed that the peripheral blood eosinophil counts may not be associated with the progression of COVID-19.[7] However, several related clinical studies had been conducted since then, these findings were not entirely consistent. Yang et al thought the low percentage of eosinophil was not a biomarker of pneumonia severity.[8] While more scholars got the conclusions that the eosinophil counts were helpful to predict the severe COVID-19 cases.[9–20] Considering these divergent conclusions, it is worth exploring whether eosinopenia is related to the severity of the disease in COVID-19, so we meta-analyze the relevant literature.
2. Methods and analysis
2.1. Study inclusion/exclusion criteria
We included cohort studies and case-control studies to assess the relationship between eosinophil counts and the severity of COVID-19. Exclusion criteria:
-
1.
overlapping or duplicate studies;
-
2.
insufficient necessary data for detailed analysis;
-
3.
review articles, meta-analysis articles, case reports, letters, conference summary;
-
4.
animal research and basic research.
2.1.1. Participants
The study population included laboratory-confirmed with COVID-19, without limitation of age, gender, or racial.
2.1.2. Interventions
The experimental group (the severe disease group) included the severe pneumonia, critical pneumonia, critical disease and death group.
2.1.3. Comparisons
The control group (the non-severe disease group) were the mild pneumonia, moderate pneumonia, non-critical disease and recovery group.
2.1.4. Outcomes
2.1.4.1. Main outcome
Evaluated the relationship between eosinophil counts and the severity of COVID-19.
2.1.4.2. Additional outcomes
The subgroup analysis in different countries and another subgroup analysis between mild-moderate pneumonia vs severe-critical pneumonia were assessed.
2.2. Search strategy
We performed electronic searches in PubMed, Cochrane Library, EMBASE and Web of Science to search published studies without date (until 23 Aug, 2020) or language restrictions, using the following terms: (a) “Eosinophils” OR “White Blood Cells” OR “Leukocytes” OR “Granulocytes”; combined with (b) “2019 nCoV” OR “2019 novel coronavirus” OR “new coronavirus” OR “novel coronavirus” OR “novel corona virus” OR “SARS CoV-2” OR “Wuhan corona virus ” OR “COVID-19” without date (until August 23, 2020) or language restrictions. Searches were re-run prior to the final analysis. The flow chart of search result was presented in Figure 1.
Figure 1.
PRISMA 2009 Flow Diagram.
2.3. Screening
The articles found in the search were imported into Endnote X9 software. Duplicates were eliminated. Two investigators (RH and LCX) independently screened the titles and abstracts due to the inclusion and exclusion criteria, the irrelevant articles were removed. Finally, the eligible articles were chosen by full-text screening. Disagreements were resolved by discussion between these 2 authors and the third author (JPH) resolved any disagreements.
2.4. Data extraction
For all the eligible studies, 2 reviewers (RH and LCX) independently extracted the information: the first author, year, study country, demographic information (age and sex), disease severity, study size and outcomes. If the data given in the original article was nonconsistent forms, we would use Wan's calculation formula to convert the data to mean ± SD.[21]
2.5. Risk of bias assessment
Two reviewers (RH and LCX) independently took the Newcastle–Ottawa Quality Assessment Scale[22] for quality estimation of literature. Nine questions would be evaluated, and each satisfactory answer were received 1 point, the maximum score was 9. If the score ≥7, we considered the article was high methodological quality. Two reviewers (RH and LCX) cross-checked the assessing process and the final score were obtained after discussing with the third reviewer (JPH).
-
1.
Selection: Representativeness of the exposed cohort; Selection of the non-exposed cohort; Ascertainment of exposure (the proof of COVID-19 and the eosinophil counts measurement); Demonstration that outcome of interest was not present at start of study.
-
2.
Comparability: Comparability of cohorts on the basis of the design or analysis. (a) Study controls for the eosinophil counts; (b) Study controls for any additional factor (age, gender, exposure history, comorbidity, etc).
-
3.
Outcome: Assessment of outcome; was follow-up long enough for outcomes to occur? (death or recurrence); Adequacy of follow-up of cohorts.
2.6. Statistical analysis
2.6.1. Assessment of heterogeneity and data syntheses
The standard mean differences and corresponding 95% confidence intervals (CIs) were calculated. Heterogeneity presumption was investigated using the Q-test and I2 statistic. If there was significant heterogeneity among studies (P < .05 or I2 > 50%), a random-effects model was used; otherwise, a fixed-effects mode was applied. Two-sided P < .05 was considered statistically significant. Analyses were conducted using Stata 15.0 statistical software packages.
2.6.2. Subgroup and sensitivity analyses
If there was significant heterogeneity among studies, the subgroup analysis of different countries and another subgroup analysis between mild-moderate pneumonia vs severe-critical pneumonia were assessed. Sensitivity analysis were performed by sequentially omitting each trial.
2.6.3. Assessment of reporting bias
Egger test and Begg test were created to estimate publication bias with the strict definition of clinical classification about COVID-19(mild-moderate pneumonia vs severe-critical pneumonia).
3. Results
3.1. Description of studies
At the beginning, 12 articles were finally eligible according to the search strategies,[9–20] but 2 articles were unable to extract value data to calculate SMD[14,18] and finally we included 10 articles assessed 1228 patients considered in this meta-analysis. The sample size ranged from 9 to 231. All the studies were originated from Asia. The details of these articles included in the meta-analysis were summarized in Table 1.
Table 1.
Basic characteristics of included studies.
| Disease severity and primary data# | |||||||
| Author | Year | Country | Age | Men (%) | Non-severe group | Severe group | Score## |
| J. Chen | 2020 | China | 45.81 ± 14.84 | 87 (51.5%) | Common group (n = 145):0.03 (0.02-0.06) | Severe group (n = 24):0.01 (0.00-0.02) | ∗∗∗∗∗∗∗∗ |
| D. Liao | 2020 | China | 63.33 ± 14.88 | 206 (54.0%) | Moderate disease(n = 149):0.04(0.01–0.10) | Severe disease(n = 145):0.05 (0.01–0.11) | ∗∗∗∗∗∗∗∗ |
| Critical disease(n = 86):0.01 (0–0.03) | |||||||
| D. W. Sun | 2020 | China | 63.86 ± 12.71 | 29 (50.9%) | Non-severe type (n = 12):0.160 (0.123–0.228) | Severe type (n = 45):0.030(0.005–0.050) | ∗∗∗∗∗∗∗∗ |
| S. Sun | 2020 | China | 49.33 ± 12.01 | 60 (51.7%) | Common (n = 89):0.03 (0.01–0.05) | Severe (n = 27):0.01 (0.00–0.02) | ∗∗∗∗∗∗∗∗ |
| Y. Sun | 2020 | China | 45.5 ± 17.59 | Not reported | Mild disease (n = 8):0.14 ± 0.06 | Severe disease (n = 10):0.01 ± 0.00 | ∗∗∗∗∗∗∗∗ |
| Moderate disease (n = 36):0.03 ± 0.04 | Critically ill (n = 9):0.09 ± 0.14 | ||||||
| C. Z. Wang | 2020 | China | 39 ± 10.44 | 23 (51.1%) | Moderate (n = 35):0.04 ± 0.06 | Severe (n = 10):0.00 ± 0.01 | ∗∗∗∗∗∗∗∗ |
| J. J. Zhang | 2020 | China | 56.5 ± 11.88 | 71 (50.7%) | Nonsevere patients (n = 82):0.02 (0.008-0.05) | Severe patients (n = 56):0.01 (0.0-0.06) | ∗∗∗∗∗∗∗ |
| G. Q. Qian | 2020 | China | 47.87 ± 15.37 | 37 (40.7%) | mild (n = 82):0.02(0.01-0.06) | severe (n = 9):0.01(0-0.01) | ∗∗∗∗∗∗∗∗ |
| Z. Wang | 2020 | China | 46.33 ± 20.44 | 32 (46.0%) | SpO2 ≥ 90%(n = 55):0.01(0.00-0.02) | SpO2 ≤ 90%(n = 14):0.00(0.00-0.01) | ∗∗∗∗∗∗∗∗ |
| M. S. Asghar | 2020 | Pakistan | 52.58 ± 15.68 | 69 (69.0%) | Recovery (n = 78):1.72 ± 1.64 | Death (n = 22):0.73 ± 1.09 | ∗∗∗∗∗∗∗∗ |
#Reported variously as mean ± SD or median, and interquartile range (IQR) values.## Newcastle–Ottawa Quality Assessment Scale (NOS) for quality estimation of literature.
3.2. Results of the meta-analysis
A comparison of the peripheral blood eosinophil counts for non-severe and severe disease in COVID-19 was performed in 10 studies. Compared with non-severe disease group, severe disease group had strikingly lower average eosinophil counts (SMD 0.65, 95% CI 0.29–1.01; P < .001, I2 = 83.7%) (Fig. 2). Considering the high heterogeneity, 2 subgroups were studied. Between different countries, the result showed SMD 0.66, 95% CI 0.26–1.06; P < .001, I2 = 85.2% (Fig. 3). Another subgroup was according to the strict definition of clinical classification about COVID-19 (mild-moderate pneumonia vs severe-critical pneumonia), the result showed SMD 0.69, 95% CI 0.25–1.13; P < .001, I2 = 87.1% (Fig. 4). The sensitivity analyses showed no significant change when individual study was excluded using random-effects model, which suggested the crediblity and stablity of our results (Fig. 5). Begg funnel plot and Egger test were used to assess publication bias in the interested subgroup, the Begg funnel plot showed no evidence of significant publication bias (P = .063) nor did Egger (P = .057) (Fig. 6).
Figure 2.
Forest of the peripheral blood eosinophil counts between patients with non-severe and severe disease group. SMD = standard mean difference.
Figure 3.
Forest of the peripheral blood eosinophil counts between patients with different countries. SMD = standard mean difference.
Figure 4.
Forest of the peripheral blood eosinophil counts between patients with the strict definition of clinical classification about COVID-19(mild-moderate pneumonia versus severe-critical pneumonia). SMD = standard mean difference.
Figure 5.
Sensitivity analysis of the peripheral blood eosinophil counts between patients with non-severe and severe disease group.
Figure 6.
Begg test and Egger test for publication bias with the strict definition of clinical classification about COVID-19(mild-moderate pneumonia versus severe-critical pneumonia).
4. Discussion
In the present study, we updated the evidences and observed that the peripheral blood eosinophil counts levels in patients with severe disease group were significantly lower than those with non-severe disease group in COVID-19. On the basis of this meta-analysis, we suggest that the eosinophil counts levels have important value as an indicator of severity in patients with COVID-19. It may also be used to monitor the progression of this disease as early as possible.
Despite eosinophils are the second least granulocyte subpopulation in the peripheral blood, the scientific community is getting increasingly interested in it because of its complex pathophysiological roles to against bacterial and viral pathogens. Evidences are showing that eosinophils protect body from viral infections, especially against RNA viruses.[23] The TLR7 receptor, which recognizes viruses and recognizes single stranded RNA (ssRNA), is one of the most important viral receptors in eosinophils, demonstrating the role of eosinophils in virus recognition is unarguable.[6]
COVID-19 is becoming one of the worst infection disease outbreaks known, it is transmitted by droplets, aerosol, contact and fecal-oral route infection, and tend to transmit among family clusters[24,25] or cause outbreaks in hospitals. It is important to identify the key clinical features of COVID-19 patients, which may help to detect and isolate the infected individuals as early as possible, and minimize the spread of the disease. Some scholars had studied the cases of COVID-19 and showed that the severe disease had abnormalities in many laboratory parameters, and some of them can be used as predictors of disease severity, such as levels of lactate dehydrogenase,[26,27] lymphocyte and subset counts,[27–31] interleukin-6,[32,33] procalcitonin,[34] D-dimer,[35] C-reactive protein[27,33] and so on. But compared to other biomarkers, the eosinophil was often overlooked, it had long been thought to be associated with allergy diseases and parasitic infections traditionally. Actually, it was versatile cell, and some scholars believed it was positioned centrally within immune and inflammatory networks, its new roles as neoplasm surveillance, tissue remodeling and the restructuring of adipose tissue were emerging.[36] The eosinophil cells may act as a positive predictor in early stages during the coronavirus infection.[37]
It has been reported previously that in infected patients, increased inflammation leads to lower counts of eosinophils.[38] Increased count of eosinophils was associated with a better prognosis for COVID-19, including the lower incidence of complications and mortality. While the recovery of lymphocytes had no effect on the prognosis.[39] In surviving acute lung injury patients, the number of eosinophils in the lungs showed an increased compared to the non-survivors.[40] Similarly, the number of peripheral blood eosinophils also increased in surviving acute respiratory distress syndrome patients. Therefore, the scholars further investigated its molecular mechanism and found that in the initiation of acute lung injury, CD101− eosinophils increased more rapidly and briefly than the neutrophils and secreted Protectin-D1 through Alox15-mediated to reduce the accumulation of inflammatory cells and reduce inflammatory factors, thus playing a role in fighting lung inflammation.[41]
Whether the glucocorticoids can save the lives of patients with severe in COVID-19 is still controversial. The hormones can prevent the release of eosinophils in the bone marrow and reduce them in the peripheral blood. Given that the eosinophils play a “protective role” in the onset of COVID-19, early use of hormones will quickly inhibit their proliferation, differentiation, migration and “protective effect” against diseases.
Several limitations of our study should be discussed. Firstly, the number of studies included was relatively low although the definition of clinical classification about COVID-19 was not strict, which may reduce the reliability of results. Secondly, the data collection may be insufficient because only English language papers were included. Thirdly, because of the small sample size, it may not strongly confirm the association between eosinophil counts levels and severity of COVID-19, which needed more studies. Fourthly, the studies included in this analysis were all performed in Asia, and the results may not be representative of other parts of the world. The significant heterogeneity in these studies could not be neglected. The possible reasons for the observed heterogeneity were the difference in underlying comorbidities, different patient population, co-infection with other diseases and the variation in follow-up. The looser inclusion criteria resulted the variety of control interventions, which may also lead to heterogeneity. However, when we chose the random-effects model and performed subgroup analysis according to the strict definition, the marked heterogeneity was still remained.
In spite of the limitations mentioned above, the subgroup analysis and its sensitivity were remained consistent, demonstrating that eosinophil counts levels might be useful as an important parameter to recognize patients with severe COVID-19 in the disease course. If eosinophils are activated, they may have the potential to treat viral respiratory diseases.[42] We believe this analysis will contribute to the development of anti-COVID-19 drugs.
Author contributions
Writing – original draft: Rong Huang, Liangcai Xie, Junpeng He, Hong Dong, Tianchun Liu.
Footnotes
Abbreviations: 95% CI = 95% confidence intervals, COVID-19 = coronavirus disease 2019.
How to cite this article: Huang R, Xie L, He J, Dong H, Liu T. Association between the peripheral blood eosinophil counts and COVID-19: a meta-analysis. Medicine. 2021;100:23(e26047).
This study does not require ethical approval as the meta-analysis is based on published research, and the original data are anonymous.
The authors have no funding and conflicts of interests to disclose.
Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.
References
- [1].Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y. Clinical features of patients infected with 2019 novel coronavirus in Wuhan. China Lancet 2020;395:497–506. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [2].Carlos WG, Dela Cruz CS, Cao B, Pasnick S, Jamil S. Novel Wuhan (2019-nCoV) Coronavirus. American journal of respiratory and critical care medicine 2020;201:P7–8. [DOI] [PubMed] [Google Scholar]
- [3].Alexpandi R, Mesquita JFD, Pandian SK, Ravi AV. Quinolines-based SARS-CoV-2 3CLpro and RdRp inhibitors and spike-RBD-ACE2 inhibitor for drug-repurposing against COVID-19: an in silico analysis. Front Microbiol 2020;11:1796. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [4].Wu CM, Chen XY, Cai YP, Xia JA, Zhou X, Xu S. Risk factors associated with acute respiratory distress syndrome anddeath in patients with coronavirus disease 2019 pneumonia in Wuhan. China JAMA Intern Med 2020;180:934–43. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [5].Zhou YL, Zhang ZC, Tian J, et al. Risk factors associated with disease progression in a cohort of patients infected with the 2019 novel coronavirus. Anna Palliat Med 2020;9:428–36. [DOI] [PubMed] [Google Scholar]
- [6].Rodrigo-Muñoz JM, Sastre B, Cañas JA, Gil-Martínez M, Redondo N, Del Pozo V. Eosinophil response against classical and emerging respiratory viruses: COVID-19. J Investig Allergol Clin Immunol 2021;31:94–107. [DOI] [PubMed] [Google Scholar]
- [7].Lippi G, Henry BM. Eosinophil count in severe coronavirus disease 2019. Qjm 2020;113:511–2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [8].Yang J, Zhao X, Liu X, Sun W, Zhou L, Wang Y. Clinical characteristics and eosinophils in young SARS-CoV-2-Positive Chinese travelers returning to Shanghai. Front Public Health 2020;8:368. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [9].Sun S, Cai X, Wang H, et al. Abnormalities of peripheral blood system in patients with COVID-19 in Wenzhou, China. Clin Chim Acta 2020;507:174–80. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [10].Sun Y, Dong Y, Wang L, Xie H, Li B, Chang C. Characteristics and prognostic factors of disease severity in patients with COVID-19: the Beijing experience. J Autoimmun 2020;112:102473. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [11].Zhang JJ, Dong X, Cao YY, Yuan YD, Yang YB, Yan YQ. Clinical characteristics of 140 patients infected with SARS-CoV-2 in Wuhan. China Allergy 2020;75:1730–41. [DOI] [PubMed] [Google Scholar]
- [12].Wang ZL, Yang BH, Li QW, Lu W, Zhang RG. Clinical features of 69 cases with coronavirus disease 2019 in Wuhan. China Clin Infect Dis 2020;71:769–77. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [13].Chen J, Pan Y, Li G, Xu W, Zhang L, Yuan S. Distinguishing between COVID-19 and influenza during the early stages by measurement of peripheral blood parameters. J Med Virol 2021;93:1029–37. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [14].Qin C, Zhou L, Hu Z, Zhang S, Yang S, Tao Y. Dysregulation of immune response in patients with coronavirus 2019 (COVID-19) in Wuhan. China Clin Infect Dis 2020;71:762–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [15].Qian GQ, Yang NB, Ding F, Ma AHY, Wang ZY, Shen YF. Epidemiologic and clinical characteristics of 91 hospitalized patients with COVID-19 in Zhejiang, China: a retrospective, multi-centre case series. QJM 2020;113:474–81. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [16].Liao D, Zhou F, Luo L, Xu M, Wang H, Xia J. Haematological characteristics and risk factors in the classification and prognosis evaluation of COVID-19: a retrospective cohort study. Lancet Haematol 2020;7:e671–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [17].Wang CZ, Deng RR, Gou LY, Fu ZX, Zhang XM, Shao F. Preliminary study to identify severe from moderate cases of COVID-19 using combined hematology parameters. Ann Translat Med 2020;8:593. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [18].Xie G, Ding F, Han L, Yin D, Lu H, Zhang M. The role of peripheral blood eosinophil counts in COVID-19 patients. Allergy 2021;76:471–82. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [19].Sun DW, Zhang D, Tian RH, Li Y, Wang YS, Cao J. The underlying changes and predicting role of peripheral blood inflammatory cells in severe COVID-19 patients: a sentinel? Clin Chim Acta 2020;508:122–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [20].Asghar MS, Kazmi SJH, Khan NA, Akram M, Khan SA, Rasheed U. Clinical profiles, characteristics, and outcomes of the first 100 admitted COVID-19 patients in Pakistan: a single-center retrospective study in a tertiary care hospital of Karachi. Cureus 2020;12:e8712. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [21].Wan X, Wang WQ, Liu JM, Tong TJ. Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range. BMC Med Res Methodol 2014;14:135. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [22].Stang A. Critical evaluation of the Newcastle-Ottawa scale for the assessment of the quality of nonrandomized studies in meta-analyses. Eur J Epidemiol 2010;25:603–5. [DOI] [PubMed] [Google Scholar]
- [23].Lamichhane PP, Samarasinghe AE. The role of innate leukocytes during influenza virus infection. J Immunol Res 2019;2019:8028725. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [24].Chen N, Zhou M, Dong X, Qu J, Gong F, Han Y. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet 2020;395:507–13. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [25].Chan JF, Yuan S, Kok KH, To KK, Chu H, Yang J. A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: a study of a family cluster. Lancet 2020;395:514–23. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [26].Chen XY, Huang MY, Xiao ZW, Yang S, Chen XQ. Lactate dehydrogenase elevations is associated with severity of COVID-19: a meta-analysis. Crit Care 2020;24:459. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [27].Zhang ZL, Hou YL, Li DT, Li FZ. Laboratory findings of COVID-19: a systematic review and meta-analysis. Scand J Clin Lab Invest 2020;80:441–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [28].Henry BM, de Oliveira MHS, Benoit S, Plebani M, Lippi G. Hematologic, biochemical and immune biomarker abnormalities associated with severe illness and mortality in coronavirus disease 2019 (COVID-19): a meta-analysis. Clin Chem Lab Med 2020;58:1021–8. [DOI] [PubMed] [Google Scholar]
- [29].Zhang H, Wu T. CD4+T, CD8+T counts and severe COVID-19: a meta-analysis. J Infection 2020;81:e82–4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [30].Wang F, Hou HY, Luo Y, Tang GX, Wu SJ, Huang M. The laboratory tests and host immunity of COVID-19 patients with different severity of illness. JCI Insight 2020;5:e137799. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [31].Wang F, Nie JY, Wang HZ, Zhao Q, Xiong Y, Deng LP. Characteristics of peripheral lymphocyte subset alteration in COVID-19 pneumonia. J Infectious Dis 2020;221:1762–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [32].Ulhaq ZS, Soraya GV. Interleukin-6 as a potential biomarker of COVID-19 progression. Med Mal Infect 2020;50:382–3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [33].Liu F, Li L, Xu MD, Wu J, Luo D, Zhu YS. Prognostic value of interleukin-6, C-reactive protein, and procalcitonin in patients with COVID-19. J Clin Virol 2020;127:104370. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [34].Lippi G, Plebani M. Procalcitonin in patients with severe coronavirus disease 2019 (COVID-19): a meta-analysis. Clin Chim Acta 2020;505:190–1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [35].Zhang LT, Yan XS, Fan QK, Liu HY, Liu XT, Liu ZJ. D-dimer levels on admission to predict in-hospital mortality in patients with Covid-19. J Thromb Haemost 2020;18:1324–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [36].Chusid MJ. Eosinophils: friends or foes. J Allergy Clin Immunol Pract 2018;6:1439–44. [DOI] [PubMed] [Google Scholar]
- [37].Formica V, Minieri M, Bernardini S, Ciotti M, D’Agostini C, Roselli M. Complete blood count might help to identify subjects with high probability of testing positive to SARS-CoV-2. Clin Med 2020;20:e114–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [38].Ho KM, Towler SC. A comparison of eosinopenia and C-reactive protein as a marker of bloodstream infections in critically ill patients: a case control study. Anaesth Intensive Care 2009;37:450–6. [DOI] [PubMed] [Google Scholar]
- [39].Willetts L, Parker K, Wesselius LJ, Protheroe CA, Jaben E, Graziano P. Immunodetection of occult eosinophils in lung tissue biopsies may help predict survival in acute lung injury. Respir Res 2011;12:116. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [40].González MM, Gonzalo ES, Lopez IC, Fernández FA, Pérez JLB, Monge DM. The prognostic value of eosinophil recovery in COVID-19: a multicentre, retrospective cohort study on patients hospitalised in Spanish hospitals. J Clin Med 2021;10:305. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [41].Zhu C, Weng QY, Zhou LR, Cao C, Li F, Wu YF. Homeostatic and early-recruited CD101 - eosinophils suppress endotoxin-induced acute lung injury. Eur Respir J 2020;56:1902354. [DOI] [PubMed] [Google Scholar]
- [42].Henriksen DP, Bodtger U, Sidenius K, Maltbaek N, Pedersen L, Madsen H. Efficacy, adverse events, and inter-drug comparison of mepolizumab and reslizumab anti-IL-5 treatments of severe asthma–a systematic review and meta-analysis. Eur Clin Respir J 2018;5:1536097. [DOI] [PMC free article] [PubMed] [Google Scholar]