Skip to content
Publicly Available Published by De Gruyter May 22, 2020

Dynamic profile and clinical implications of hematological parameters in hospitalized patients with coronavirus disease 2019

Xiurong Ding, Yanhua Yu, Bichao Lu, Jianbo Huo, Ming Chen, Yanfang Kang, Jinli Lou and Zhizhong Liu

Abstract

Objectives

As people across the world suffer from coronavirus disease 2019 (COVID-19), further studies are needed to facilitate evaluating the severity and prognosis of COVID-19 patients. In the study, we aimed to dissect the dynamic profile and clinical implications of hematological findings in hospitalized patients with COVID-19.

Methods

We retrospectively analyzed the hematological findings of 72 patients with COVID-19 admitted from January 21 to February 17, 2020. The final date of follow-up was March 20, 2020. Dynamic profile of vital hematological parameters in severe and non-severe patients was presented at different time points (day 1, 5, 7, 9, 11, 13, 15 after admission), and the correlation of hematological parameters with hospitalization time was indicated.

Results

Of 72 patients with COVID-19, lymphopenia and leukopenia occurred in 39 (54.2%) and 20 (27.8%) patients with COVID-19, respectively. Fifteen (20.8%) patients were defined as severe cases and 57 (79.2%) were non-severe cases. Compared to non-severe patients, leukocyte count, neutrophil count and neutrophil-to-lymphocyte ratio (NLR) were significantly higher, whereas lymphocyte count was declined in severe patients at each time point. A growing trend in platelet count was found in non-severe patients over the follow-up period. In addition, a positive correlation of NLR with hospitalization time was detected from day 5 after admission.

Conclusions

Dynamic changes in vital hematological parameters from severe and non-severe patients had been characterized in the course of hospitalization. During hospitalization, NLR was found to have certain relevance to the hospitalization days and a role in forecasting disease prognosis for patients with COVID-19.

Introduction

Since early December 2019, several cases of pneumonia of unknown etiology had been reported in Wuhan, Hubei province, China, and promptly drew much attention [1], [2]. Severe acute respiratory infection symptoms occurred in most patients, and some rapidly developed into acute respiratory distress syndrome (ARDS) and acute respiratory failure. In early January 2020, a novel coronavirus was identified from an infected patient using next-generation sequencing by the Chinese Center for Disease Control and Prevention [3], and was formally named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [4]; meanwhile, the disease was named coronavirus disease 2019 (COVID-19) by the World Health Organization (WHO). The SARS-CoV-2 was the seventh member of the family of coronaviruses that infect humans and partly resembled severe acute respiratory syndrome coronavirus (SARS-CoV) [3], [5]. Human-to-human transmission of SARS-CoV-2 had been testified by solid evidence [6], [7], [8], [9], resulting in rapid spread from Wuhan to other areas. Recently, the WHO had made the assessment that COVID-19 could be characterized as a pandemic, thus calling for extended research on COVID-19. Laboratory findings of patients with COVID-19 had been briefly described in previous studies [2], [9], [10], reminding the importance of laboratory characteristics for hospitalized patients with COVID-19 and necessity for further research. Here, we aimed to further analyze the dynamic profile of vital hematological parameters during hospitalization and their correlation with the hospitalization time of patients with COVID-19.

Materials and methods

Participants and data collection

In this retrospective study, a total of 72 patients with COVID-19 admitted to Beijing YouAn Hospital (one of the designated hospitals for COVID-19 in Beijing, China) from January 21 to February 17, 2020, were enrolled according to the coronavirus pneumonia diagnosis and treatment program (6th trial edition) issued by the National Health Commission of the People’s Republic of China (http://www.nhc.gov.cn/). The diagnostic criteria of COVID-19 were based on positive results of sequencing or real-time reverse transcription polymerase chain reaction (real-time RT-PCR) assay of nasal or pharyngeal swab specimens. Patients with COVID-19 were defined as severe (including the critical severe type) and non-severe (including the mild and common types) cases according to the coronavirus pneumonia diagnosis and treatment program, and the criteria of clinical classification were described as follows: (1) mild type, slight clinical symptoms with no signs of pneumonia by radiologic examination; (2) common type, fever and respiratory tract symptoms appeared and pneumonia was found by radiologic examination; (3) severe type, meet any of the following: (a) tachypnea with a respiration rate ≥30 beats/min; (b) resting oxygen saturation ≤93%; (c) arterial partial oxygen pressure (PaO2)/fraction of inspired oxygen (FiO2) ≤300 mmHg; (d) patients whose imaging showed that the pulmonary involvement significantly aggravated by more than 50% within 24–48 h were managed as severe type; (4) critical severe type, meet any of the following: (a) respiratory failure occurred and mechanical ventilation was required; (b) shock happened; (c) patients complicated with organ failure required ICU admission. This study was approved by the Ethics Commission of Beijing YouAn Hospital (No. 2020023), and all of the patients had signed the informed consent.

The clinical data, radiological and laboratory results on admission or during hospitalization were extracted from the electronic medical records by March 20, 2020, the final date of follow-up. We selected patients with complete hematological results on day 1, 5, 7, 9, 11, 13 and 15 after admission in order to map the dynamic profile of hematological results at different time points. In addition, complete hematological results on day 1, 5 and 7 after admission, as well as the age, gender and major comorbidities, were collected to dissect their correlation with the duration of hospitalization.

Laboratory measurement and confirmation of COVID-19

Complete blood count was completed using ADVIA 2120i hematology analyzer (Siemens, Germany). Real-time RT-PCR assays for the enrolled patients were performed by Clinical Laboratory Center, Beijing YouAn Hospital, in accordance with the technical guidance established by the WHO (https://www.who.int/emergencies/diseases/novel-coronavirus-2019/technical-guidance/laboratory-guidance).

Statistical analysis

All statistical analyses were performed using SPSS software (Version 22.0, SPSS Inc). Continuous variables were expressed as the medians and interquartile ranges (IQR), as appropriate. Categorical variables were presented as counts and percentages in each category. The Mann-Whitney U test was applied to analyze continuous variables, and chi square (χ2) and Fisher’s exact tests were used for categorical variables as appropriate. Multivariate linear regression was used to analyze the correlation between multiple variables (NLR, age, gender, comorbidity) and duration of hospitalization. A p-value (two-sided) less than 0.05 was considered statistically significant.

Results

Baseline characteristics of the participants

By March 20, 2020, 15 (20.8%) of 72 patients were defined as severe cases and 57 (79.2%) were non-severe cases. Sixty-eight (94.4%) patients were discharged and four (5.6%) died. The median duration of hospitalization was 13 days (IQR, 10–18; range from 6 to 45 days). Of 72 patients with COVID-19, the median age was 49 years (IQR, 37–64) and 33 (45.8%) were men (Table 1); 37 (51.4%) had recently been to Wuhan City and 23 (31.9%) had contact with patients with COVID-19. The most common symptoms were fever (61 [84.7%]) and cough (44 [61.1%]). Less common symptoms included fatigue (18 [25.0%]), expectoration (9 [12.5%]), dyspnea (7 [9.7%]), headache (8 [11.1%]) and anorexia (8 [11.1%]). The most common comorbidities found in the infected patients included hypertension (9 [12.5%]), cardiovascular disease (6 [8.3%]) and diabetes (5 [6.9%]). Typical radiological findings on chest computed tomography (CT) were found in 58 (80.5%) patients with COVID-19 (Table 1).

Table 1:

Baseline characteristics of the patients with COVID-19.

Clinical characteristics COVID-19 (n=72)
Age, years 49 (37–64)
Sex
 Men 33/72 (45.8%)
 Women 39/72 (54.2%)
Exposure to source of transmission within 14 days
 Recently been to Wuhan 37/72 (51.4%)
 Contacted patients with COVID-19 23/72 (31.9%)
 Others 12/72 (16.7%)
Comorbidities
 Diabetes 5/72 (6.9%)
 Hypertension 9/72 (12.5%)
 Cardiovascular disease 6/72 (8.3%)
 Chronic obstructive pulmonary disease 2/72 (2.8%)
 Malignancy 3/72 (4.2%)
 Chronic liver disease 4/72 (5.6%)
Fever 61/72 (84.7%)
Cough 44/72 (61.1%)
Fatigue 18/72 (25.0%)
Expectoration 9/72 (12.5%)
Dyspnea 7/72 (9.7%)
Pharyngalgia 5/72 (6.9%)
Anorexia 8/72 (11.1%)
Nausea 4/72 (5.6%)
Diarrhea 1/72 (1.4%)
Abdominal pain 1/72 (1.4%)
Headache 8/72 (11.1%)
Dizziness 2/72 (2.8%)
Myalgia 4/72 (5.6%)
Bilateral patchy shadows or ground glass opacity by chest CT 58/72 (80.5%)

  1. Data are shown as median (interquartile range, IQR) and n/N (%). COVID-19, coronavirus disease 2019; CT, computed tomography.

Hematological findings of patients with COVID-19 on admission

Lymphopenia (<1.1×109/L) and thrombocytopenia (<125×109/L) occurred in 39 (54.2%) and 10 (13.9%) patients with COVID-19, respectively; 20 (27.8%) patients with COVID-19 had leukopenia (<3.5×109/L) and one (1.4%) had leukocytosis (>9.5×109/L) (Table 2). Compared to non-severe patients, the median age was significantly higher (67 years [IQR, 55–76] vs. 46 years [IQR, 35–60], p<0.001) and the duration of hospitalization was much longer (20 days [IQR, 14–36] vs. 13 [IQR, 11–16], p=0.002) in severe patients. Leukocyte count, count and percentage of neutrophil and neutrophil-to-lymphocyte ratio (NLR) were significantly increased, whereas the count and percentage of lymphocyte and monocyte percentage were declined in severe patients (p<0.05 for all). In addition, lymphopenia and thrombocytopenia were more likely to be observed in severe patients (11 [73.3%] and 4 [26.7%], respectively) compared to non-severe patients (28 [49.1%] and 6 [10.5%], respectively) (Table 2).

Table 2:

Hematological findings of patients with COVID-19 on admission.

Total (n=72) Severe (n=15) Non-severe (n=57) p-Valuea
Age, years 49 (37–64) 67 (55–76) 46 (35–60) <0.001
Sex 0.216
 Men 33/72 (45.8%) 9/15 (60.0%) 24/57 (42.1%)
 Women 39/72 (54.2%) 6/15 (40.0%) 33/57 (57.9%)
Duration of hospitalization, days 13 (10–18) 20 (14–36) 13 (11–16) 0.002
Laboratory parameters Normal range
Leukocyte count, ×109/L 3.5–9.5 4.0 (3.5–4.9) 6.3 (3.7–7.6) 4.0 (3.4–4.4) 0.019
 <3.5 20/72 (27.8%) 3/15 (20.0%) 17/57 (29.8%) 0.45
 >9.5 1/72 (1.4%) 1/15 (6.7%) 0/57 0.05
Neutrophil count, ×109/L 1.8–6.3 2.3 (1.8–3.2) 4.2 (2.2–5.8) 2.2 (1.8–2.8) 0.004
Neutrophil, % 40.0–75.0 61.4 (50.7–70.0) 72.2 (62.6–81.6) 57.2 (50.4–67.2) 0.001
Lymphocyte count, ×109/L 1.1–3.2 1.0 (0.8–1.4) 0.8 (0.6–1.0) 1.1 (0.8–1.4) 0.038
 <1.1 39/72 (54.2%) 11/15 (73.3%) 28/57 (49.1%) 0.094
Lymphocyte, % 20.0–50.0 28.4 (21.1–36.4) 19.1 (9.4–27.2) 30.4 (21.7–37.4) 0.003
Monocyte count, ×109/L 0.1–0.6 0.3 (0.2–0.4) 0.3 (0.2–0.4) 0.3 (0.2–0.4) 0.667
Monocyte, % 3.0–10.0 6.4 (5.0–8.0) 6.0 (3.9–6.7) 6.7 (5.3–8.7) 0.032
Neutrophil-to-lymphocyte ratio 2.0 (1.4–3.3) 3.6 (2.4–9.6) 1.9 (1.3–2.9) 0.002
Lymphocyte-to-monocyte ratio 4.1 (2.8–5.9) 3.2 (2.0–4.7) 4.3 (2.9–6.0) 0.102
Erythrocyte count, ×1012/L Male: 4.3–5.8

Female: 3.8–5.1
4.6 (4.3–4.9) 4.6 (4.4–5.0) 4.5 (4.2–4.8) 0.295
Hemoglobin, g/L Male: 130–175

Female: 115–150
140 (130–147) 139 (133–150) 140 (130–145) 0.51
Platelet count, ×109/L 125–350 180 (148–225) 160 (134–216) 180 (149–227) 0.515
 <125 10/72 (13.9%) 4/15 (26.7%) 6/57 (10.5%) 0.108
Mean platelet volume, fL 8.0–12.0 9.2 (7.4–10.1) 9.7 (9.2–11.6) 9.1 (7.1–10.0) 0.017

  1. Data are shown as median (interquartile range, IQR) and n/N (%). ap-Values comparing severe and non-severe patients result from χ2 test, Fisher’s exact test or Mann-Whitney U test. COVID-19, coronavirus disease 2019.

Dynamic profile of vital hematological parameters in patients with COVID-19

As not all the subjects in the study simultaneously had hematological results at specific time points (day 1, 5, 7, 9, 11, 13 and 15 after admission), only 17 non-severe patients and 14 severe patients with complete hematological results at these time points were selected to compare the dynamic changes in hematological results between severe and non-severe patients. Vital hematological results were displayed at different time points in Figure 1. Compared to non-severe patients, leukocyte count, neutrophil count and NLR were significantly increased (Figure 1A, B, E), while lymphocyte count was much lower (Figure 1C) in severe patients at each time point after admission.

Figure 1: Dynamic changes in vital hematological parameters in patients with COVID-19.
Timeline plots display the dynamic profile of five hematological parameters (leukocyte count, neutrophil count, lymphocyte count, neutrophil-to-lymphocyte ratio and platelet count) in 31 hospitalized patients with COVID-19 (14 severe cases and 17 non-severe cases). Dashed lines indicate the upper or lower normal limit of each parameter. *p<0.05, **p<0.01 for severe vs. non-severe patients at the same time point. ap<0.01 for day 15 vs. day 1 after admission in non-severe patients.

Figure 1:

Dynamic changes in vital hematological parameters in patients with COVID-19.

Timeline plots display the dynamic profile of five hematological parameters (leukocyte count, neutrophil count, lymphocyte count, neutrophil-to-lymphocyte ratio and platelet count) in 31 hospitalized patients with COVID-19 (14 severe cases and 17 non-severe cases). Dashed lines indicate the upper or lower normal limit of each parameter. *p<0.05, **p<0.01 for severe vs. non-severe patients at the same time point. ap<0.01 for day 15 vs. day 1 after admission in non-severe patients.

In non-severe patients, the median count of lymphocyte was below the lower limit (1.1×109/L) of normal range in the early stage of hospitalization, and reached the lower limit until day 11 after admission. Despite a slight ascent from day 13 after admission, lymphocyte count in severe patients was far below the lower limit and generally declined during the follow-up (Figure 1C). An upward trend in NLR from severe patients was observed during hospitalization, though it partly descended from day 11 after admission. Conversely, NLR in non-severe patients remained stable at a much lower level (Figure 1E).

A growing trend was found in platelet count from non-severe patients during hospitalization. The median count of platelet on day 15 (379×109/L [IQR, 310–426] vs. 166×109/L [IQR, 132–232], p=0.008) was obviously higher than that on day 1 after admission in non-severe patients, yet no such alteration was found in severe patients (180×109/L [IQR, 91–279] vs. 160×109/L [IQR, 111–206], p=0.893) (Figure 1D). Over the follow-up period, platelet count on day 11 (278×109/L [IQR, 211–348] vs. 120×109/L [IQR, 66–147], p=0.04) and day 15 (379×109/L [IQR, 310–426] vs. 180×109/L [IQR, 91–279], p=0.008) in non-severe patients was significantly higher than that in severe patients. In addition, synchronous trends were noticed in leukocyte count and neutrophil count during the follow-up (Figure 1A, B).

The correlation of dynamic NLR with duration of hospitalization

Because not all the hospitalized patients with COVID-19 had complete hematological results on day 5 and day 7 after admission, hematological results on day 1, 5 and 7 after admission were collected in only 54 patients. NLR at each of the time points (day 1, 5, 7 after admission) was included in the multivariate regression analysis, as well as major confounding factors such as age, gender and comorbidities (Table 3). On admission, only age was found to be positively correlated with hospitalization time (p=0.018, adjusted R2=0.291). On day 5 after admission, NLR and cardiovascular disease instead of age were positively correlated with hospitalization time (p=0.001 and 0.036, respectively; adjusted R2=0.448). Furthermore, NLR on day 7 after admission was still found to be correlated with hospitalization time (p=0.014, adjusted R2=0.384).

Table 3:

Correlation of dynamic NLR with the duration of hospitalization.

Variables Day 1 (n=54)
Day 5 (n=54)
Day 7 (n=49)a
Standardized beta p-Value Adjusted R2 Standardized beta p-Value Adjusted R2 Standardized beta p-Value Adjusted R2
Age 0.36 0.018 0.291 0.172 0.197 0.448 −0.044 0.851 0.384
Gender 0.153 0.217 0.169 0.127 0.148 0.354
Hypertension 0.103 0.503 0.113 0.399 0.122 0.53
Cardiovascular disease 0.25 0.051 0.24 0.036 0.255 0.15
Diabetes 0.022 0.879 −0.018 0.889 0.042 0.822
NLR 0.054 0.73 0.436 0.001 0.538 0.014

  1. aCase number descends to 49 due to lack of hematological results. NLR, neutrophil-to-lymphocyte ratio.

Discussion

To date, clinical features including epidemiological, clinical, laboratory, radiological and treatment data of patients with COVID-19 had been described in many studies [2], [9], [10], [11], [12], in which most of the laboratory findings were shown as the significant differences between severe and non-severe patients on admission, and the role of several laboratory parameters on admission in assessing the disease severity of COVID-19. Here, we mainly dissected the dynamic profile of vital hematological results during hospitalization and their correlations with hospitalization time.

Like severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS) [13], [14], changes in circulating blood cells were also important features in patients with COVID-19, including abnormalities in the number and function of lymphocytes. Lymphopenia on admission was reported in numerous studies, of which the proportion varied from 36.9% to 83.2% [2], [9], [15], [16]. In the study, lymphopenia was found in 54.2% of patients on admission. Lymphocyte count in severe patients was much lower compared with non-severe patients on admission and appeared more difficult to recover during hospitalization. Disturbance of the immune system including lymphopenia, imbalance of lymphocyte subgroups and cytokine storm had been found to occur in patients with COVID-19, and the extent of disturbance might be linked to the severity of COVID-19 [2], [17], [18].

NLR had become another important laboratory parameter of patients with COVID-19 due to the characteristic changes in lymphocyte count. The NLR was considered to be a marker for evaluation of the progression and prognosis in patients with infection and tumors, and increased NLR indicated poor prognosis [19], [20], [21]. Similar clinical value had been proven as NLR was applied in COVID-19. Elevated NLR on admission was considered an independent risk factor for severe illness and mortality of COVID-19 patients [12], [22], [23]. Likewise, the obviously augmented difference in NLR between severe and non-severe patients during hospitalization in this study also suggested the association between NLR and disease severity or clinical course. Through multivariate analysis, we further found that only age was correlated with the hospitalization time on admission, yet the correlation of NLR with hospitalization time gradually emerged after admission, which was significantly enhanced since day 5 after admission. Therefore, our findings suggested that dynamic NLR had implications for better evaluating the hospitalization time of patients with COVID-19.

In addition, lower platelet count had been observed in patients with more severe COVID-19 [24], as reported in patients with SARS [25], [26], and thrombocytopenia was associated with an increased risk of in-hospital mortality [27]. In the study, trends in platelet count during hospitalization varied in patients with different severity of COVID-19. Platelet count in non-severe patients obviously increased in the late stage of hospitalization, whereas it still sustained a lower profile in severe patients. It was suggested that platelet count combined with lymphocyte count and IL-6 served as markers for potential progression to critical illness [28]. Thus, monitoring dynamic platelet count presumably facilitated assessing the prognosis of hospitalized patients with COVID-19.

There were several limitations in the study. First, this was a single-center study with limited cases (especially severe cases). Second, hospitalization time in non-severe patients varied from 6 to 22 days, and the median duration was 13 days (IQR, 11–16). To match the laboratory data of severe patients, non-severe patients with complete hematological data and longer hospital stay were selected; thus, the results in Figure 1 might be influenced due to the limited cases.

Conclusions

In this single-center study of 72 patients with confirmed COVID-19 in Beijing, China, dynamic changes in vital hematological parameters (leukocyte count, neutrophil count, lymphocyte count, platelet count and NLR) had been characterized in the course of hospitalization and NLR from day 5 after admission was found to be positively correlated with the duration of hospitalization.


Corresponding authors: Jinli Lou, MD, Department of Clinical Laboratory, Beijing YouAn Hospital, Capital Medical University, No. 8, Xitoutiao, You’anmen wai, Beijing 100069, P.R. China, Phone: +8613581613049; and Zhizhong Liu, MD, Department of Clinical Laboratory, Beijing Bo’ai Hospital, China Rehabilitation Research Center, No. 10 Jiaomen North Road, Beijing 100068, P.R. China
aXiurong Ding, Yanhua Yu and Bichao Lu contributed equally to this work.

Acknowledgments

The authors would like to sincerely appreciate all the participants in the study, and thank all the stuff from Beijing YouAn Hospital and Beijing Bo’ai Hospital for their efforts in fighting against the epidemic coronavirus disease.

  1. Research funding: None declared.

  2. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  3. Competing interests: Authors state no conflict of interest.

  4. Informed consent: Informed consent was obtained from all individuals included in this study.

  5. Ethical approval: This study was approved by the Ethics Commission of Beijing YouAn Hospital (No. 2020023).

References

1. Lu H, Stratton CW, Tang YW. Outbreak of pneumonia of unknown etiology in Wuhan, China: the mystery and the miracle. J Med Virol 2020;92:401–2.10.1002/jmv.25678Search in Google Scholar

2. Huang C, Wang Y, Li X, Ren L, Zhao J, Hu Y, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 2020;395:497–506.10.1016/S0140-6736(20)30183-5Search in Google Scholar

3. Lu R, Zhao X, Li J, Niu P, Yang B, Wu H, et al. Genomic characterisation and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding. Lancet 2020;395:565–74.10.1016/S0140-6736(20)30251-8Search in Google Scholar

4. Gorbalenya AE, Baker SC, Baric RS, de Groot RJ, Drosten C, Gulyaeva AA, et al. The species severe acute respiratory syndrome-related coronavirus: classifying 2019-nCoV and naming it SARS-CoV-2. Nat Microbiol 2020;5:536–44.10.1038/s41564-020-0695-zSearch in Google Scholar

5. Zhu N, Zhang D, Wang W, Li X, Yang B, Song J, et al. A novel coronavirus from patients with pneumonia in China, 2019. N Engl J Med 2020;382:727–33.10.1056/NEJMoa2001017Search in Google Scholar

6. Riou J, Althaus CL. Pattern of early human-to-human transmission of Wuhan 2019 novel coronavirus (2019-nCoV), December 2019 to January 2020. Euro Surveill 2020;25:2000058.10.2807/1560-7917.ES.2020.25.4.2000058Search in Google Scholar

7. Lu CW, Liu XF, Jia ZF. 2019-nCoV transmission through the ocular surface must not be ignored. Lancet 2020;395:e39.10.1016/S0140-6736(20)30313-5Search in Google Scholar

8. Rothe C, Schunk M, Sothmann P, Bretzel G, Froeschl G, Wallrauch C, et al. Transmission of 2019-nCoV infection from an asymptomatic contact in Germany. N Engl J Med 2020;382:970–1.10.1056/NEJMc2001468Search in Google Scholar

9. Wang D, Hu B, Hu C, Zhu F, Liu X, Zhang J, et al. Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan, China. J Am Med Assoc 2020;323:1061–9.10.1001/jama.2020.1585Search in Google Scholar

10. Chen N, Zhou M, Dong X, Qu J, Gong F, Han Y, et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet 2020;395:507–13.10.1016/S0140-6736(20)30211-7Search in Google Scholar

11. Livingston E, Bucher K. Coronavirus disease 2019 (COVID-19) in Italy. J Am Med Assoc 2020. doi:10.1001/jama.2020.4344.10.1001/jama.2020.4344Search in Google Scholar

12. Yang AP, Liu JP, Tao WQ, Li HM. The diagnostic and predictive role of NLR, d-NLR and PLR in COVID-19 patients. Int Immunopharmacol 2020;84:106504.10.1016/j.intimp.2020.106504Search in Google Scholar

13. Lee N, Hui D, Wu A, Chan P, Cameron P, Joynt GM, et al. A major outbreak of severe acute respiratory syndrome in Hong Kong. N Engl J Med 2003;348:1986–94.10.1056/NEJMoa030685Search in Google Scholar

14. Assiri A, Al-Tawfiq JA, Al-Rabeeah AA, Al-Rabiah FA, Al-Hajjar S, Al-Barrak A, et al. Epidemiological, demographic, and clinical characteristics of 47 cases of middle east respiratory syndrome coronavirus disease from Saudi Arabia: a descriptive study. Lancet Infect Dis 2013;13:752–61.10.1016/S1473-3099(13)70204-4Search in Google Scholar

15. Fan BE, Chong VC, Chan SS, Lim GH, Lim KG, Tan GB, et al. Hematologic parameters in patients with COVID-19 infection. Am J Hematol 2020;95:E131-4.10.1002/ajh.25774Search in Google Scholar

16. Guan WJ, Ni ZY, Hu Y, Liang WH, Ou CQ, He JX, et al. Clinical characteristics of coronavirus disease 2019 in China. N Engl J Med 2020;382:1708–20.10.1056/NEJMoa2002032Search in Google Scholar

17. Qin C, Zhou L, Hu Z, Zhang S, Yang S, Tao Y, et al. Dysregulation of immune response in patients with COVID-19 in Wuhan, China. Clin Infect Dis 2020. doi:10.1093/cid/ciaa248.10.1093/cid/ciaa248Search in Google Scholar

18. Ye Q, Wang B, Mao J. The pathogenesis and treatment of the ‘cytokine storm’ in COVID-19. J Infect 2020;S0163-4453:30165-1.10.1016/j.jinf.2020.03.037Search in Google Scholar

19. Guthrie GJ, Charles KA, Roxburgh CS, Horgan PG, McMillan DC, Clarke SJ. The systemic inflammation-based neutrophil-lymphocyte ratio: experience in patients with cancer. Crit Rev Oncol Hematol 2013;88:218–30.10.1016/j.critrevonc.2013.03.010Search in Google Scholar

20. Liu H, Zhang H, Wan G, Sang Y, Chang Y, Wang X, et al. Neutrophil-lymphocyte ratio: a novel predictor for short-term prognosis in acute-on-chronic hepatitis B liver failure. J Viral Hepat 2014;21:499–507.10.1111/jvh.12160Search in Google Scholar

21. Han Q, Wen X, Wang L, Han X, Shen Y, Cao J, et al. Role of hematological parameters in the diagnosis of influenza virus infection in patients with respiratory tract infection symptoms. J Clin Lab Anal 2020;00:e23191.10.1002/jcla.23191Search in Google Scholar

22. Liu Y, Du X, Chen J, Jin Y, Peng L, Wang HH, et al. Neutrophil-to-lymphocyte ratio as an independent risk factor for mortality in hospitalized patients with COVID-19. J Infect 2020;S0163-4453:30208-5.10.1016/j.jinf.2020.04.002Search in Google Scholar

23. Zhang Y, Zheng L, Liu L, Zhao M, Xiao J, Zhao Q. Liver impairment in COVID-19 patients: a retrospective analysis of 115 cases from a single centre in Wuhan city, China. Liver Int 2020. doi:10.1111/liv.14455.10.1111/liv.14455Search in Google Scholar

24. Lippi G, Plebani M, Henry BM. Thrombocytopenia is associated with severe coronavirus disease 2019 (COVID-19) infections: a meta-analysis. Clin Chim Acta 2020;506:145–8.10.1016/j.cca.2020.03.022Search in Google Scholar

25. Peiris JS, Lai ST, Poon LL, Guan Y, Yam LY, Lim W, et al. Coronavirus as a possible cause of severe acute respiratory syndrome. Lancet 2003;361:1319–25.10.1016/S0140-6736(03)13077-2Search in Google Scholar

26. Wong RS, Wu A, To KF, Lee N, Lam CW, Wong CK, et al. Haematological manifestations in patients with severe acute respiratory syndrome: retrospective analysis. Br Med J 2003;326:1358–62.10.1136/bmj.326.7403.1358Search in Google Scholar

27. Yang X, Yang Q, Wang Y, Wu Y, Xu J, Yu Y, et al. Thrombocytopenia and its association with mortality in patients with COVID-19. J Thromb Haemost 2020. doi:10.1111/jth.14848.10.1111/jth.14848Search in Google Scholar

28. Henry BM, de Oliveira MH, 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;58:1021–8.10.1515/cclm-2020-0369Search in Google Scholar

Received: 2020-03-31
Accepted: 2020-05-10
Published Online: 2020-05-22
Published in Print: 2020-07-28

©2020 Walter de Gruyter GmbH, Berlin/Boston

Scroll Up Arrow