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Comparative analysis of laboratory indexes of severe and non-severe patients infected with COVID-19
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Highlights
• The values of laboratory indicators in 35 articles were integrated.
• The laboratory indexes of severe and non-severe patients infected with COVID-19 has been compared.
• Indicators that may most effectively predict a non-severe COVID-19 patient develop into a severe patient have been concluded.
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title
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Highlights
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• The values of laboratory indicators in 35 articles were integrated.
• The laboratory indexes of severe and non-severe patients infected with COVID-19 has been compared.
• Indicators that may most effectively predict a non-severe COVID-19 patient develop into a severe patient have been concluded.
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The values of laboratory indicators in 35 articles were integrated.
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The laboratory indexes of severe and non-severe patients infected with COVID-19 has been compared.
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Indicators that may most effectively predict a non-severe COVID-19 patient develop into a severe patient have been concluded.
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abstract
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Background
The pandemic coronavirus disease 2019 (COVID-19) has threaten the global health. The characteristics of laboratory findings of coronavirus are of great significance for clinical diagnosis and treatment. We found indicators that may most effectively predict a non-severe COVID-19 patient develop into a severe patient.
Methods
We conducted a meta-analysis to compare the laboratory findings of severe patients with non-severe patients with COVID-19 from searched articles.
Results
Through the analysis of laboratory examination information of patients with COVID-19 from 35 articles (5912 patients), we demonstrated that severe cases possessed higher levels of leukocyte (1.20-fold), neutrophil (1.33-fold), CRP (3.04-fold), PCT (2.00-fold), ESR (1.44-fold), AST (1.40-fold), ALT (1.34-fold), LDH (1.54-fold), CK (1.44-fold), CK-MB (1.39-fold), total bilirubin (1.14-fold), urea (1.28-fold), creatine (1.09-fold), PT (1.03-fold) and D-dimer (2.74-fold), as well as lower levels of lymphocytes (1.44-fold), eosinophil (2.00-fold), monocyte (1.08-fold), Hemoglobin (1.53-fold), PLT (1.15-fold), albumin (1.15-fold), and APTT (1.02-fold). Lymphocyte subsets and series of inflammatory cytokines were also different in severe cases with the non-severe ones, including lower levels of CD4 T cells (2.10-fold) and CD8 T cells (2.00-fold), higher levels of IL-1β (1.02-fold), IL-6 (1.93-fold) and IL-10 (1.55-fold).
Conclusions
Some certain laboratory inspections could predict the progress of the COVID-19 changes, especially lymphocytes, CRP, PCT, ALT, AST, LDH, D-dimer, CD4 T cells and IL6, which provide valuable signals for preventing the deterioration of the disease.
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Background
The pandemic coronavirus disease 2019 (COVID-19) has threaten the global health. The characteristics of laboratory findings of coronavirus are of great significance for clinical diagnosis and treatment. We found indicators that may most effectively predict a non-severe COVID-19 patient develop into a severe patient.
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title
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Background
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p
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The pandemic coronavirus disease 2019 (COVID-19) has threaten the global health. The characteristics of laboratory findings of coronavirus are of great significance for clinical diagnosis and treatment. We found indicators that may most effectively predict a non-severe COVID-19 patient develop into a severe patient.
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sec
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Methods
We conducted a meta-analysis to compare the laboratory findings of severe patients with non-severe patients with COVID-19 from searched articles.
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title
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Methods
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p
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We conducted a meta-analysis to compare the laboratory findings of severe patients with non-severe patients with COVID-19 from searched articles.
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sec
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Results
Through the analysis of laboratory examination information of patients with COVID-19 from 35 articles (5912 patients), we demonstrated that severe cases possessed higher levels of leukocyte (1.20-fold), neutrophil (1.33-fold), CRP (3.04-fold), PCT (2.00-fold), ESR (1.44-fold), AST (1.40-fold), ALT (1.34-fold), LDH (1.54-fold), CK (1.44-fold), CK-MB (1.39-fold), total bilirubin (1.14-fold), urea (1.28-fold), creatine (1.09-fold), PT (1.03-fold) and D-dimer (2.74-fold), as well as lower levels of lymphocytes (1.44-fold), eosinophil (2.00-fold), monocyte (1.08-fold), Hemoglobin (1.53-fold), PLT (1.15-fold), albumin (1.15-fold), and APTT (1.02-fold). Lymphocyte subsets and series of inflammatory cytokines were also different in severe cases with the non-severe ones, including lower levels of CD4 T cells (2.10-fold) and CD8 T cells (2.00-fold), higher levels of IL-1β (1.02-fold), IL-6 (1.93-fold) and IL-10 (1.55-fold).
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title
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Results
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p
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Through the analysis of laboratory examination information of patients with COVID-19 from 35 articles (5912 patients), we demonstrated that severe cases possessed higher levels of leukocyte (1.20-fold), neutrophil (1.33-fold), CRP (3.04-fold), PCT (2.00-fold), ESR (1.44-fold), AST (1.40-fold), ALT (1.34-fold), LDH (1.54-fold), CK (1.44-fold), CK-MB (1.39-fold), total bilirubin (1.14-fold), urea (1.28-fold), creatine (1.09-fold), PT (1.03-fold) and D-dimer (2.74-fold), as well as lower levels of lymphocytes (1.44-fold), eosinophil (2.00-fold), monocyte (1.08-fold), Hemoglobin (1.53-fold), PLT (1.15-fold), albumin (1.15-fold), and APTT (1.02-fold). Lymphocyte subsets and series of inflammatory cytokines were also different in severe cases with the non-severe ones, including lower levels of CD4 T cells (2.10-fold) and CD8 T cells (2.00-fold), higher levels of IL-1β (1.02-fold), IL-6 (1.93-fold) and IL-10 (1.55-fold).
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Conclusions
Some certain laboratory inspections could predict the progress of the COVID-19 changes, especially lymphocytes, CRP, PCT, ALT, AST, LDH, D-dimer, CD4 T cells and IL6, which provide valuable signals for preventing the deterioration of the disease.
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title
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Conclusions
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p
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Some certain laboratory inspections could predict the progress of the COVID-19 changes, especially lymphocytes, CRP, PCT, ALT, AST, LDH, D-dimer, CD4 T cells and IL6, which provide valuable signals for preventing the deterioration of the disease.
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1 Introduction
Since December 2019, the rapid propagation of a novel coronavirus (SARS-CoV-2) has broken out in China, and SARS-CoV-2 causes a novel pneumonia named COVID-19 [1]. SARS-CoV-2 is a β-coronavirus with a genome highly homologous to bats, which probably originated from wild animals [2]. Interpersonal transmission is the main cause of infection [3]. The World Health Organization (WHO) has declared it as a public health emergency of international concern [4]. As of May 3, 2020, a total of 3,405,914 cases were confirmed and 240,573 cases died globally [5]. The clinical features of severe COVID-19 are similar to those of severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS). It can cause acute respiratory distress syndrome (ARDS), acute heart injury, and even death. Its main clinical features are fever, cough and sore throat. According to the clinical classification method, the patients were divided into four types: ordinary type, mild type, severe type and critically ill type according to the severity of the disease [6], [7]. In addition, the correlation between specific laboratory diagnosis and disease severity deserves attention. Several studies have reported different laboratory findings at the beginning of the outbreak of COVID-19 [8], [9], [10]. The purpose of this survey is to reveal the characteristics of laboratory findings of COVID-19 through the included articles, especially the changes of severe and critically ill patients, so as to provide more information for COVID-19 's diagnosis.
2 Methods
2.1 Literature search and selection
PubMed and Web of Science were used to search for related articles. The key words are “2019-nCoV” “COVID-19” “SARS-CoV-2” “clinical characteristics” and “laboratory findings”. To ensure the comprehensiveness and accuracy of the study, we also consulted the references of the included literature. The searches were performed three times to identify articles published before April 27, 2020. Then we screen the articles according to the abstract, eliminate the articles that obviously do not meet the inclusion criteria, and then read the full text for re-screening. Articles that provided values of laboratory indicators for severe and non-severe patients, including blood routine, inflammatory factors, biochemical and immune-related indexes were included. Pre-printed articles are also included. Articles published repeatedly, translated articles, studies did not include the laboratory indicators needed for meta-analysis; research data were missing were excluded. In addition, conference summaries, reviews and meta-analysis were excluded.
2.2 Analysis content
Statistically analyzed the data related to laboratory indexes (blood routine, inflammatory markers, biochemical detection indexes, blood coagulation function and immune indexes) to compare the differences between severe and non-severe patients and summarize indicators with statistical significance and clinical value. These laboratory indicators were usually showed as the mean and standard deviation, but sometimes were median and interquartile range (IQR). The sample mean was estimated by Luo et al.'s method [11] and variance by Wan et al.'s [12] from the sample size, median and IQR. For these laboratory indexes, the inverse variance method for pooling was used to calculate the overall mean from studies reporting a single. The I2 statistic is a test used to quantify heterogeneity and values of I2 > 50% indicated that heterogeneity existed. When statistical heterogeneity was identified, the random effects model will be used. The meta package (ver 4.11–0; https://cran.r-project.org/) was used to conduct the overall mean. In addition, we analyzed the correlation and regularity of diverse laboratory indexes in patients with COVID-19 to find the considerable advantages of combined analysis in the diagnosis and treatment of patients' condition.
2.3 Risk of bias assessment
We will apply the following criteria to assess the risk of bias for each included study. 1. A clear purpose of the study; 2. Including continuous patients; 3. Expected collection of data; 4. The end point adapted to the research goal; 5. A fair assessment of the end point of the study; 6. A follow-up period commensurate with the objectives of the study; 7. Comprehensive laboratory indicators; 8. Sufficient numbers of patients. The project score is 0 (not reported), 1 (reported but insufficient), or 2 (reported and sufficient). The global ideal score for non-comparative studies is 16. In addition, we will draft funnel-plots for laboratory indicators with significant differences between severe and non-severe patients with COVID-19 if there are sufficient included studies (at least 10) and observe the symmetry of the funnel-plots to judge the publication bias.
3 Results
3.1 Characteristics of included studies
The process of study selection is displayed in Fig. 1 . A total of 715 publications were retrieved, including 645 articles on PubMed, 70 articles on Web of Science. Among these studies, 65 records were excluded due to duplication of records/titles. 615 were removed because they did not meet the inclusion criteria based on title and/or abstract. Finally, we obtained the laboratory test results of 35 articles describing 5912 COVID-19 confirmed patients (up to May 2020). The basic characteristics of the articles included in the study are shown in Table 1 .
Fig. 1 Flow diagram for selection of studies.
Table 1 Summary the characteristics of 35 studies that described the risk factors with COVID-19 patients.
Author Journal Year Country/ region Number of total patients Number of non-severe patients Number of severe patients Age, median (IQI) or mean (SD)
Qin C [13] Clin. Infect. Dis. 2020 Shangha, China 452 166 286 58 (47–67)
Chen X [74] Clin. Infect. Dis. 2020 Wuhan, China 48 21 27 64.6 ± 18.1
Wang R [75] Int. J. Infect. Dis. 2020 Anhui, China 125 100 25 38.8 ± 13.8
Gao Y [68] J. Med. Virol. 2020 Anhui, China 43 28 15
Zheng YL [17] J. Clin. Virol. 2020 Chengdu, China 99 67 32 49.4 ± 18.45
Ma J [15] J. Infect. 2020 Wuhan, China 37 17 20 62 (59–70)
Wang DL [18] Lancet 2020 Jiangsu, China 620 567 53 44.4 ± 17.2
Liu W [56] Chin. Med. J. 2020 Wuhan, China 78 67 11 38 (33–57)
Yang AP [76] Int. Immunopharmacol. 2020 China 93 69 24 46.4 ± 17.6
Li KH [77] Invest Radiol 2020 Chongqing, China 83 58 25 45.5 ± 12.3
Zhang JJ [62] Allergy 2020 Wuhan, China 140 82 58 57 (25–87)
Huang CL [10] Lancet 2020 Wuhan, China 41 28 13 49 (41–58)
Wang DW [64] JAMA 2020 Wuhan, China 138 102 36 56 (42–68)
Liu M [14] Zhonghua Jie He He Hu Xi Za Zhi 2020 Wuhan, China 30 26 4 35 ± 8
Mo PZ [78] Clin. Infect. Dis. 2020 Wuhan, China 155 70 85 54 (42–66)
Peng YD [79] Zhonghua Xin Xue Guan Bing Za Zhi 2020 Shanghai, China 112 96 16 62 (55–67)
Feng Y [80] Am. J. Respir. Crit. Care Med. 2020 China 476 352 124 53 (40–64)
Cai QX [81] Allergy 2020 Shenzhen, China 298 240 58 47.5 (33–61)
Li H [82] J. Infect. 2020 Wuhan, China 132 60 72 62 ± 12.7
Zheng F [83] Eur Rev Med Pharmacol Sci 2020 Hunan, China 161 131 30 45 (33.5–57)
Wang KK [84] Lancet 2020 Hong Kong, China 23 13 10
Guan W [85] New Engl J Med 2020 Wuhan, China 1099 926 173 47 (35–58)
Gong J [86] Clin. Infect. Dis. 2020 Wuhan and Guangdong, China 189 161 28 49 (35–63)
Lei SQ [87] E Clin Med 2020 China 34 19 15 55 (43–63)
Deng Q [88] Int. J. Cardiol. 2020 Wuhan, China 112 45 67 65 (49–70)
Mao L [89] JAMA Neurol. 2020 Wuhan, China 214 126 88 52.7 ± 15.5
Du RH [90] Ann. Am. Thorac. Soc. 2020 Wuhan, China 109 58 51 70.7 ± 10.9
Xie HS [91] Liver Int. 2020 Wuhan, China 79 51 28 60 (48–66)
Wu J [92] J. Intern. Med. 2020 China 280 197 83 43 ± 19
Chen G [93] J. Clin. Investig. 2020 Wuhan, China 21 10 11 56 (50–65)
Wan S [94] J. Med. Virol. 2020 Chongqing, China 135 95 40 47 (36–55)
Pan HQ [42] Lancet Infect. Dis. 2020 Wuhan, China 221 166 55 55 (39–66.5)
Bo XU [16] J. Infect. 2020 Wuhan, China 187 80 107 62 (48.5–71)
GQQ [95] Int. J. Med. 2020 Zhejiang, China 91 82 9 50 (36.5–57)
Lo LI [96] Int. J. Biol. Sci. 2020 Macau, China 10 6 4 54 (27–64)
Among these articles, 34 articles described the results of blood routine and infection-related biomarkers, 26 articles described the results of biochemical tests, 23 articles provided test results for blood coagulation and 5 articles described the results of immunoassay. A total of 5,912 patients (mean age:54.80; 95%CI (52.50–57.20)), including 4337 non-severe patients (mean age:48.50; 95%CI (45.70–51.30)) and 1663 severe patients (mean age:61.00; 95%CI (59.10–62.90)). More than half of them were male (3072/5912(51.96%)). 2531 of these patients with underlying disease, including hypertension, diabetes, cardiovascular disease, cerebrovascular disease, chronic obstructive pulmonary disease (COPD), chronic kidney disease and liver disease, Malignant tumors and patients with low immunity.
3.2 Analysis of laboratory indicators
Based on the comprehensive collation of the laboratory data provided in the selected 35 articles, the average value and variation range of various indexes of total patients, non-severe patients and severe patients were obtained and shown in Table 3 . In addition, the Funnel plot of important laboratory indicators are shown in Fig. 2, Fig. 3, Fig. 4, Fig. 5, Fig. 6, Fig. 7, Fig. 8, Fig. 9, Fig. 10 .
Table 2 The quality assessment of included studies.
Author 1 2 3 4 5 6 7 8 Score
Qin C [13] 2 2 2 2 2 0 2 2 14
Chen X [74] 2 2 2 2 2 0 1 1 12
Wang R [75] 2. 2 2 2 2 2 2 2 16
Gao Y [68] 2 2 2 2 2 0 2 1 13
Zheng YL [17] 2 2 2 2 2 2 2 1 15
Ma J [15] 2 2 2 2 2 0 2 1 13
Wang DL [18] 2 2 2 2 2 2 2 2 16
Liu W [56] 2 2 2 2 2 0 2 1 13
Yang AP [76] 2 2 2 2 2 1 1 1 13
Li KH [77] 2 2 2 2 2 0 2 1 13
Zhang JJ [62] 2 2 2 2 2 0 2 2 14
Huang CL [10] 2 2 2 2 2 0 2 1 13
Wang DW [64] 2 2 2 2 2 1 2 2 15
Liu M [14] 2 2 2 2 2 0 2 1 13
Mo PZ [78] 2 2 2 2 2 1 2 2 15
Peng YD [79] 2 2 2 2 2 2 2 1 15
Feng Y [80] 2 2 2 2 2 0 2 2 14
Cai QX [81] 2 2 2 2 2 2 2 2 16
Li H [82] 2 2 2 2 2 2 1 2 15
Zheng F [83] 2 2 2 2 2 0 2 2 14
Wang KK [84] 2 2 2 2 2 2 2 1 15
Guan W [85] 2 2 2 2 2 2 2 2 16
Gong J [86] 2 2 2 2 2 2 2 2 16
Lei SQ [87] 2 2 2 2 2 2 2 1 15
Deng Q [88] 2 2 2 2 2 2 1 2 15
Mao L [89] 2 2 2 2 2 0 1 2 13
Du RH [90] 2 2 2 2 2 2 2 2 16
Xie HS [91] 2 2 2 2 2 0 2 1 13
Wu J [92] 2 2 2 2 2 0 2 2 14
Chen G [93] 2 2 2 2 2 0 1 1 12
Wan S [94] 2 2 2 2 2 1 2 2 15
Pan HQ [42] 2 2 2 2 2 1 2 2 15
Bo XU [16] 2 2 2 2 2 2 2 2 16
GQQ [95] 2 2 2 2 2 0 2 1 13
Lo LI [96] 2 2 2 2 2 2 1 1 14
1. A clear purpose of the study; 2. Including continuous patients; 3. Expected collection of data; 4. The end point adapted to the research goal; 5. A fair assessment of the end point of the study; 6. A follow-up period commensurate with the objectives of the study; 7. Comprehensive laboratory indicators; 8. Sufficient numbers of patients.
Table3 Results of Laboratory findings of severe and non-severe patients infected with COVID-19.
Variables Classification Number of articles included Number of patients included Mean (95% CI) P-value
Blood routine WBC×109 /l Non-severe patients 34 4236 5.07 (4.90, 5.24) <0.01
Severe patients 1703 6.06 (5.67, 6.46)
All patients 5939 5.39 (5.22, 5.55)
Neutrophils×109 /l Non-severe patients 29 3005 3.71 (3.36, 4.06) <0.01
Severe patients 1368 4.94 (4.30, 5.58)
All patients 4373 4.22 (3.94, 4.51)
Lymphocytes×109 /l Non-severe patients 34 4228 1.15 (1.08, 1.22) <0.01
Severe patients 1703 0.80 (0.75, 0.84)
All patients 5931 0.98 (0.92, 1.04)
Eosinophil×109 /l Non-severe patients 5 1035 0.04 (0.03, 0.05) 0.003
Severe patients 451 0.02 (0.01, 0.02)
All patients 1486 0.03 (0.02, 0.04)
Monocyte×109 /l Non-severe patients 13 1397 0.41 (0.40, 0.42) 0.086
Severe patients 700 0.38 (0.35, 0.41)
All patients 2097 0.40 (0.39, 0.42)
PLT×109 /l Non-severe patients 20 3068 184.19 (178.04, 190.33) 0.340
Severe patients 1019 212.58 (154.63, 270.53)
All patients 4087 201.44 (175.75, 227.12)
Hbg/l Non-severe patients 14 2652 131.36 (128.33, 134.39) 0.164
Severe patients 711 126.73 (121.16, 132.30)
All patients 3363 128.98 (126.17, 131.78)
Inflammation-related factors ESR mm/60 min Non-severe patients 8 1390 28.16 (20.13, 36.2) 0.111
Severe patients 695 40.54 (27.61, 53.4)
All patients 2085 33.94 (28.10, 39.7)
CRP mg/l Non-severe patients 26 2939 19.83 (16.67, 23.0) <0.01
Severe patients 1415 60.91 (49.24, 72.5)
All patients 4354 36.99 (33.31, 40.67)
PCT ng/ml Non-severe patients 22 2325 0.07 (0.05, 0.09) <0.01
Severe patients 1222 0.14 (0.11, 0.17)
All patients 3547 0.10 (0.08, 0.12)
Blood biochemistry ALT U/l Non-severe patients 26 2769 24.85 (22.69, 27.0) <0.01
Severe patients 1027 33.78 (29.54, 38.0)
All patients 3796 28.31 (26.25, 30.3)
AST U/l Non-severe patients 26 2731 26.24 (25.29, 28.18) <0.01
Severe patients 1019 36.78 (33.69, 39.87)
All patients 3750 30.66 (29.19, 32.13)
LDH U/l Non-severe patients 19 2306 224.20 (205.33, 243.07) <0.01
Severe patients 811 344.48 (307.08, 381.88)
All patients 3117 271.82 (254.13, 289.52)
CK U/l Non-severe patients 17 2241 77.69 (69.68, 85.70) <0.01
Severe patients 849 111.92 (98.24, 125.61)
All patients 3090 90.92 (83.54, 98.29)
CK-MB U/l Non-severe patients 9 1329 8.76 (4.74, 12.79) 0.246
Severe patients 550 12.26 (7.93, 16.58)
All patients 1879 10.51 (8.33, 12.70)
Albumin g/l Non-severe patients 10 985 39.41 (37.95, 40.87) <0.01
Severe patients 431 34.29 (32.79, 35.80)
All patients 1416 36.75 (35.17, 38.32)
Creatinine μmol/l Non-severe patients 25 2696 66.97 (64.65, 69.28) <0.01
Severe patients 1000 72.94 (69.23, 76.66)
All patients 3696 69.61 (67.57, 71.65)
Urea mmol/l Non-severe patients 16 1929 4.36 (4.12, 4.59) <0.01
Severe patients 703 5.59 (5.39, 6.51)
All patients 2632 4.98 (4.72, 5.23)
Total bilirubin mmol/l Non-severe patients 14 2065 10.38 (9.78, 10.99) 0.017
Severe patients 564 11.86 (10.81, 12.91)
All patients 2629 10.92 (10.39, 11.45)
Blood coagulation function APTTs Non-severe patients 12 1422 33.49 (31.17, 35.82) 0.724
Severe patients 407 32.92 (30.78, 35.06)
All patients 1829 33.23 (31.59, 34.86)
PT s Non-severe patients 12 1388 12.45 (11.98, 12.91) 0.319
Severe patients 504 12.80 (12.29, 13.30)
All patients 1892 12.63 (12.31, 12.94)
D dimer mg/l Non-severe patients 23 2503 0.47 (0.40, 0.53) <0.01
Severe patients 1043 1.29 (0.03, 0.54)
All patients 3546 0.61 (0.54, 0.67)
Lymphocyte subsets CD4 T cells /μL Non-severe patients 5 475 561.81 (485.46, 638.15) <0.01
Severe patients 208 266.79 (204.51, 329.07)
All patients 683 407.03 (310.24, 503.83)
CD8 T cells /μL Non-severe patients 5 475 349.01 (292.61, 405.40) <0.01
Severe patients 208 174.61 (125.95, 223.27)
All patients 683 266.65 (198.49, 334.81)
Cytokines IL-1β pg/ml Non-severe patients 2 246 5.01 (4.96, 5.06) 0.055
Severe patients 393 5.11 (5.02, 5.20)
All patients 639 5.06 (4.98, 5.15)
IL-6 pg/ml Non-severe patients 5 312 13.22 (6.88, 19.57) 0.027
Severe patients 455 25.58 (16.69, 34.47)
All patients 767 19.66 (13.44, 25.89)
IL-10 pg/ml Non-severe patients 2 246 5.71 (5.51, 5.90) <0.01
Severe patients 393 8.87 (7.15, 10.59)
All patients 639 7.23 (6.18, 8.28)
SPSS25 was used. Comparison between severe and non-severe patients with t test or Mann-Whitney U test.
Fig. 2 Meta-analysis of lymphocytes.
Fig. 3 Meta-analysis of CRP.
Fig. 4 Meta-analysis of PCT.
Fig. 5 Meta-analysis of ALT.
Fig. 6 Meta-analysis of AST.
Fig. 7 Meta-analysis of LDH.
Fig. 8 Meta-analysis of D-dimer.
Fig. 9 Meta-analysis of CD4 T cells.
Fig. 10 Meta-analysis of IL-6.
3.2.1 Blood routine examination
Leukopenia was observed in 21.92% (363/1656) patients with lymphocytopenia in 29.02% (886/3053) patients. Elevated neutrophils were observed in 19.85% (81/408) patients. 14.73% (75/509) and 12.68% (78/615) patients were accompanied by a decrease in Hemoglobin and platelet count (PLT) respectively. Most importantly, there were several significant differences between severe patients and non-severe patients, including higher leukocyte (1.20-fold; 6.06 vs 5.07 × 109/l; P < 0.01) and neutrophil (1.33-fold; 4.94 vs 3.71 × 109/l; P < 0.01), lower lymphocyte (1.44-fold; 1.15 vs 0.80 × 109/l; P < 0.01), eosinophils (2.00-fold; 0.04 vs 0.02 × 109/l; P = 0.03), monocytes (1.08-fold; 0.38 vs 0.41 × 109/l; P = 0.041), PLT (1.15-fold; 212.58 vs 184.19 × 109/l; P = 0.987) and hemoglobin (1.53-fold; 131.36vs 126.73 × 109 g/l; P = 0.163).
3.2.2 Inflammatory biomarkers examination
Increased C-reactive protein (CRP) concentration appeared in 57.40% (1494/2603) patients, procalcitonin (PCT) increased in 12.20% (256/2099) patients, and 39.26% (117/298) patients had an increase in erythrocyte sedimentation rate (ESR). Moreover, higher levels of CRP (3.04-fold; 60.91 vs 19.83 mg/l; P < 0.01), PCT (2.00-fold; 0.14vs 0.07 ng/ml; P < 0.01) and ESR (1.44-fold; 40.54 vs 28.16 mm/60 min; P = 0.096) were observed in severe patients in comparison with non-severe patients.
3.2.3 Blood biochemical examination
3.2.3.1 Cardiac markers examination
Our statistics showed that the related indexes of myocardial injury increased in different numbers of patients with COVID-19. (respectively creatine kinase (CK) (7.74% (157/2029)); aspartate aminotransferase (AST) (14.87% (388/2609)); lactate dehydrogenase (LDH) (24.50% (468/1910)). Several significant differences were noted between severe and non-severe patients, especially higher values of AST (1.40-fold; 36.78 vs 26.24 U/l; P < 0.01), LDH (1.54-fold; 344.48 vs 224.20 U/l; P < 0.01), CK (1.44-fold; 111.92 vs 77.69 U/l; P < 0.01) and CK-MB (1.39-fold; 12.26 vs 8.76 U/l; P = 0.317).
3.2.3.2 Liver function
The increase of alanine aminotransferase (ALT) (12.27% (296/2412)) and AST (14.87% (388/2609)) with COVID-19 has been observed. Moreover, the decrease of albumin (143/221 (64.70%)) was more common while the increase of total bilirubin (TBIL) was relatively rare in the majority of patients (109/1558 (6.70%)). Comparing with non-severe patients, higher ALT (1.34-fold; 33.78 vs 24.85 U/l; P < 0.01), AST (1.40-fold; 36.78 vs 26.24 U/l; P < 0.01), TBIL (1.14-fold; 11.86 vs 10.38 U/l; P = 0.024) and lower albumin (1.15-fold; 39.41vs 34.29 g/l; P < 0.01) of severe patients has been worked out.
3.2.3.3 Renal function
The increase of creatinine (2.41% (40/1659)) and urea (13.50% (47/348)) were observed among the included patients with COVID-19. Besides, albumin reduction (64.70% (143/221)) was very common. More importantly, higher levels of creatinine (1.09-fold;72.94 vs 66.97 μmol; P < 0.01), urea (1.28-fold; 5.59 vs 4.36 mmol; P < 0.01) and lower concentrations of albumin (1.15-fold; 39.41 vs 34.29 g/l; P < 0.01) of severe patients were summed up in comparison with non-severe patients.
3.2.4 Blood coagulation function
Prothrombin time (PT) prolonged in 22.65% (53/234) patients and shortened in 10.68% (25/234) patients while activated partial thromboplastin time (APTT) prolonged in 21.79% (51/234) patients and shortened in 5.56% (13/234) patients. D dimer increased in 28.94% (534/1845) patients. Abnormal coagulation function is more obvious in severe patients, including shorter APTT (1.02-fold;33.49 vs 32.92 s; P = 0.804), increased D-dimer (2.74-fold; 1.29 vs 0.47 mg/l; P < 0.01) and longer PT (1.03-fold; 12.80 vs 12.45 s; P = 0.407).
3.2.5 Immunological examination
3.2.5.1 Antibody detection
The values of antibodies and complements in blood serum in Qin's [13] study showed that immunoglobulins (IgA, IgG and IgM) and complement proteins (C3 and C4) of COVID-19 patients are within the normal range. Compared with the non-severe group, the IgM of the severe group was only slightly decreased, and there was no significant difference in other immunoglobulins and complement, which was consistent with the results of Feng's study [14].
3.2.5.2 Lymphocyte subsets
The total number of B cells, T cells and NK cells significantly decreased in patients with COVID-19 (852.9 /uL), and more evident in the severe cases (1.37-fold; 743.6 vs 1020.1 /uL; P = 0.032) compared to the non-severe group [13]. Lower levels of CD4 T cells (2.10-fold; 561.81 vs 266.79 cell/μl; P < 0.01), CD8 T cells (2.00-fold; 349.01 vs 174.61cell/μl; P < 0.01) were summarized in severe patients comparing with non-severe patients from 5 articles [14], [15], [16], [17], [18]. In addition, lower CD3 T cells (1.70-fold; 1070.23 vs 628.20 cell/μl; P < 0.01) in severe patients was noted in Liu et al. [18].
3.2.5.3 Cytokine
Series of inflammatory cytokines were also increaseted in severe cases than the non-severe ones, including interleukin (IL)-1β (1.02-fold; 5.11 vs 5.01 pg/ml; P = 0.098), IL-6 (1.93-fold; 25.58 vs 13.22 pg/ml; P = 0.043), IL-10 (1.55-fold; 8.87 vs 5.71 pg/ml; P < 0.01). In addition, Qin et al. found higher levels of IL-2R (1.14-fold; 757.0 vs 663.5 U/ml; P < 0.01), IL-8 (1.34-fold; 18.4 vs 13.7 pg/ml; P < 0.01) and TNF-α (1.04-fold; 8.7 vs 8.4 pg/ml; P = 0.037) in severe patients in comparison with non-severe patients [13]. Studies also reported that GSCF, IP-10, MCP1and MIP1A in severe patients were higher [10].
4 Quality assessment
Judging by the evaluation score, all of the included articles were classified as high quality and there was no considerable publication bias ( Table 2 ). The quality assessment graph and the reporting bias of important laboratory indicators are exhibited in Supplementary materials.
5 Reports on laboratory indicators of COVID-19 patients worldwide except China
As the pandemic spreads to other countries and viral gene mutations, the characteristics of laboratory indicators of COVID-19 patients worldwide need to be grasped. Since only China has made a clear classification of the severity of patients with COVID-19, the definition of severe patients in countries expect China is simply summarized as staying in ICU. We decided to analyze the laboratory data of patients in representative articles about foreign countries to clarify the differences between foreign patients and Chinese patients with COVID-19.
In 21 critically ill patients with COVID-19 in Washington State, 67% and 38% of these patients had lymphopenia and abnormalities of liver function tests at admission respectively [19]. Higher concentrations of IL-6 and D-dimer at admission were independently associated with in-hospital mortality, which has been confirmed in 1150 patients in New York [20]. On a cohort of 300 COVID-19 patients from Italy, patients demonstrated lymphopenia in many cases [21]. In another group of Italian cases, the frequency of granulocyte morphological anomalies has been highlighted, especially in patients with severe ARDS at admission [22]. The values of leukocyte, IL-6, LDH, CK and D-dimer continued to increase in 50 COVID-19 patients with ARDS during hospitalization in a German report [23]. About Singapore, lymphopenia was present in 39% patients (7/16) and an elevated CRP in 38% patients (6/16), while kidney function remained normal [24]. Lower blood counts of leukocytes, platelets, neutrophils, lymphocytes, eosinophils, and basophils (all P < 0.001) in COVID-19 patients were significant predictors of SARS-CoV-2 positive test [25]. In addition, compared with less severe diseases, CRP is higher and the lymphocyte count is lower has been found in a study in Norway [26].
Overall, we found that foreign patients have similarities to the changes in laboratory indicators of Chinese patients, typically including a decrease in leukocytes and lymphocytes and an increase in inflammation-related factors. The abnormality of blood coagulation, liver and kidney function and immune function also appeared in foreign patients, especially in severe and critically ill patients. However, due to differences in viral gene variation and detection time, the diversity in the laboratory indicators of patients around the world is inevitable, and more data required to confirm.
6 Discussion
Facing the huge threat of COVID--19 to human health, laboratory evaluation and early prediction of patients' condition should be paid to more attention. At present, the characteristics of laboratory examination results of hospitalized patients were reported, but the discrepancies were observed between these reports due to the different proportions of severe patients in each study.
Among 5912 patients who underwent laboratory examinations on admission, lymphopenia was typical, which might be risk factors for disease progression of COVID-19 [27]. The PLT-to-lymphocyte ratio (PLR) and the neutrophil/lymphocyte ratio (NLR) may provide new indexes for the monitoring the changes of patients with COVID-19 [28], [29], [30]. The NLR was > 5 in severe patients critically ill patients, proving that severe patients are more likely to develop leukocytosis and lymphocytopenia [29]. Neutrophils and eosinophils may be used to predict the recovery probability [31], [32]. The decrease of hemoglobin and PLT were significantly associated with the severity of the disease [33], [34]. In addition, the combined parameter NLR&RDW-SD can help clinically to predict the severity of COVID-19 patients [35]. In conclusion, blood routine examination is of great value in the diagnosis and prognosis of COVID-19.
High infection-related biomarkers (i.e. PCT, ESR, and CRP) have been observed in our study. CRP is a good predictor of adverse consequences and related to inflammation of tissues and organs [36], [37], [38]. A simple death risk index (ACP) consisting of age and CRP was developed by Lu et al [39], by which the short-term mortality associated with COVID-19 can be predicted. Higher serum hypersensitive C-reactive protein (hs-CRP) is an important marker of poor prognosis in COVID-19 patients and can be used to predict the risk of death in severe patients, which reflects the persistent state of inflammation [40]. Increased PCT, SAA and ESR were identified as powerful factors to predict disease progression of patients with COVID-19 [41], [42], [43], [44]. In addition, the combined detection of IL-6, ESR and CRP improve the efficiency of predicting the development of patients' condition [45]. On account of the common co-infection in children, the increase of PCT is more obvious than that in adults, so it should be used as an important index for the detection of children [46]. Thus, infection-related biomarkers are risk factors for disease progression.
In term of biochemical indicators, patients with organ dysfunction, (including ARDS, acute renal injury, heart injury, liver dysfunction, pneumothorax, etc.) are prone to exhibit abnormal results of blood biochemical examination [47]. Increased serum N-terminal proB-type natriuretic peptide (NT-proBNP), cardiac troponin-I (cTnI), myoglobin and creatinine were related factors of critical COVID-19 with heart damage [48], [49]. Cardiac injury defined by the increase of hs-cTnI and D-dimer on admission and patients with high BNP is associated with a higher risk of mortality [50], [51], [52]. LDH, AST/ALT ratio, TBIL could be identified as powerful predictive factors for early recognition of liver injury and were positively correlated with death risk of COVID-19 patients [53], [54], [55]. Albumin, serum urea nitrogen and creatine were risk factor s for assessing kidney damage and disease progression [55], [56]. Many patients have abnormal urine analysis on admission, including proteinuria or hematuria, which indicates that urine analysis can better reveal the potential kidney damage of COVID-19 patients to reflect and predict the severity of the disease [57], [58]. In short, the cardiac biomarkers, liver and kidney function examination for severe and critically ill patients can evaluate the degree of extrapulmonary damage caused by complications.
Furthermore, the level of lactic acid, plasma angiotensin II, amylase and lipase can also be used as indicators to estimate the course of the disease [49], [59]. Plasma angiotensin II level linearly correlated with virus titer and the degree of lung injury was increased in one study [49]. Other than the high expression of angiotensin-converting enzyme 2 (ACE2) in the pancreatic tissue of COVID-19 patients, the increase of serum amylase and lipase were found [60]. In addition, the detection of electrolyte and blood glucose indexes is of great significance for patients with underlying diseases of electrolyte balance disorder and glucose metabolism disorder.
The changes in blood coagulation, especially disseminated intravascular coagulation (DIC), which is common in critical diseases, should also been paid enough attention [61], [62], [63], [64]. Severe patients may exhibit blood coagulation disorders, including increased D-dimer, prolonged PT and shortened APTT, which is consistent with reports [62], [63]. D-dimer is associated with the severity of COVID-19 [65]. Fibrinogen can be significantly increased in the early stages of severe patients, but notably decreased in the later stages, this may be the reason why serious people are more likely to suffer from cerebrovascular disease [66], [67]. Bleeding and coagulation dysfunction and even DIC combined with COVID-19 is a process of dynamic change. Monitoring the blood coagulation function of patients is beneficial to the early diagnosis, prevention and treatment of the disease. In addition, The combined detection of IL-6 and D-dimer had important clinical value for early prediction of the severity of COVID-19 patients due to its high sensitivity and specificity [68].
Our analysis showed that lower levels of CD4 and CD8 and higher levels of inflammatory cytokines (IL-1β, IL-6, IL-10) in severe patients, which made important impacts in predicting the state of the illness changes from mild to severe. The decrease of CD4 and CD8 in peripheral blood and the increase of IL-6 are the high-risk factors of cytokine release syndrome-like (CRSL) [59], [69], [70]. CD3 + T cells, IP-10, MCP-3 and IL-1ra were also closely related to the severity and progression of COVID-19 [54], [71]. Diao et al. [69] found that the number of T cells was negatively correlated with the concentration of serum IL-6, IL-10 and TNF-α. In addition, the immune response phenotype based on late IgG response can be used as a simple complementary tool to distinguish between severe and non-severe COVID-19 patients and to further predict their clinical outcomes [72]. Overall, close monitoring of the T lymphocyte subsets and cytokines might provide valuable information on the patient’s condition change during the treatment process [73].
7 Limitations
Although our analysis showed the characteristics of laboratory findings of COVID-19 patients, relatively few patients were included in the analysis. In addition, the recruited participants in our study were hospitalized before April 27, 2020 and more laboratory tests of COVID-19 patients should be investigated.
8 Conclusion
Some certain laboratory inspections could predict the progress of the COVID-19 changes, especially, lymphocytes, CRP, PCT, ALT, AST, LDH, D-dimer, CD4 T cells and IL6, which provide valuable signals for preventing the deterioration of the disease.
CRediT authorship contribution statement
Jinfeng Bao: Conceptualization, Methodology, Software, Investigation, Writing - original draft. Chenxi Li: Validation, Formal analysis, Visualization, Software. Kai Zhang: Validation, Formal analysis, Visualization. Haiquan Kang: Resources, Writing - review & editing, Supervision, Data curation. Wensen Chen: Resources, Writing - review & editing, Supervision, Data curation. Bing Gu: .
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1 Introduction
Since December 2019, the rapid propagation of a novel coronavirus (SARS-CoV-2) has broken out in China, and SARS-CoV-2 causes a novel pneumonia named COVID-19 [1]. SARS-CoV-2 is a β-coronavirus with a genome highly homologous to bats, which probably originated from wild animals [2]. Interpersonal transmission is the main cause of infection [3]. The World Health Organization (WHO) has declared it as a public health emergency of international concern [4]. As of May 3, 2020, a total of 3,405,914 cases were confirmed and 240,573 cases died globally [5]. The clinical features of severe COVID-19 are similar to those of severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS). It can cause acute respiratory distress syndrome (ARDS), acute heart injury, and even death. Its main clinical features are fever, cough and sore throat. According to the clinical classification method, the patients were divided into four types: ordinary type, mild type, severe type and critically ill type according to the severity of the disease [6], [7]. In addition, the correlation between specific laboratory diagnosis and disease severity deserves attention. Several studies have reported different laboratory findings at the beginning of the outbreak of COVID-19 [8], [9], [10]. The purpose of this survey is to reveal the characteristics of laboratory findings of COVID-19 through the included articles, especially the changes of severe and critically ill patients, so as to provide more information for COVID-19 's diagnosis.
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Introduction
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Since December 2019, the rapid propagation of a novel coronavirus (SARS-CoV-2) has broken out in China, and SARS-CoV-2 causes a novel pneumonia named COVID-19 [1]. SARS-CoV-2 is a β-coronavirus with a genome highly homologous to bats, which probably originated from wild animals [2]. Interpersonal transmission is the main cause of infection [3]. The World Health Organization (WHO) has declared it as a public health emergency of international concern [4]. As of May 3, 2020, a total of 3,405,914 cases were confirmed and 240,573 cases died globally [5]. The clinical features of severe COVID-19 are similar to those of severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS). It can cause acute respiratory distress syndrome (ARDS), acute heart injury, and even death. Its main clinical features are fever, cough and sore throat. According to the clinical classification method, the patients were divided into four types: ordinary type, mild type, severe type and critically ill type according to the severity of the disease [6], [7]. In addition, the correlation between specific laboratory diagnosis and disease severity deserves attention. Several studies have reported different laboratory findings at the beginning of the outbreak of COVID-19 [8], [9], [10]. The purpose of this survey is to reveal the characteristics of laboratory findings of COVID-19 through the included articles, especially the changes of severe and critically ill patients, so as to provide more information for COVID-19 's diagnosis.
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2 Methods
2.1 Literature search and selection
PubMed and Web of Science were used to search for related articles. The key words are “2019-nCoV” “COVID-19” “SARS-CoV-2” “clinical characteristics” and “laboratory findings”. To ensure the comprehensiveness and accuracy of the study, we also consulted the references of the included literature. The searches were performed three times to identify articles published before April 27, 2020. Then we screen the articles according to the abstract, eliminate the articles that obviously do not meet the inclusion criteria, and then read the full text for re-screening. Articles that provided values of laboratory indicators for severe and non-severe patients, including blood routine, inflammatory factors, biochemical and immune-related indexes were included. Pre-printed articles are also included. Articles published repeatedly, translated articles, studies did not include the laboratory indicators needed for meta-analysis; research data were missing were excluded. In addition, conference summaries, reviews and meta-analysis were excluded.
2.2 Analysis content
Statistically analyzed the data related to laboratory indexes (blood routine, inflammatory markers, biochemical detection indexes, blood coagulation function and immune indexes) to compare the differences between severe and non-severe patients and summarize indicators with statistical significance and clinical value. These laboratory indicators were usually showed as the mean and standard deviation, but sometimes were median and interquartile range (IQR). The sample mean was estimated by Luo et al.'s method [11] and variance by Wan et al.'s [12] from the sample size, median and IQR. For these laboratory indexes, the inverse variance method for pooling was used to calculate the overall mean from studies reporting a single. The I2 statistic is a test used to quantify heterogeneity and values of I2 > 50% indicated that heterogeneity existed. When statistical heterogeneity was identified, the random effects model will be used. The meta package (ver 4.11–0; https://cran.r-project.org/) was used to conduct the overall mean. In addition, we analyzed the correlation and regularity of diverse laboratory indexes in patients with COVID-19 to find the considerable advantages of combined analysis in the diagnosis and treatment of patients' condition.
2.3 Risk of bias assessment
We will apply the following criteria to assess the risk of bias for each included study. 1. A clear purpose of the study; 2. Including continuous patients; 3. Expected collection of data; 4. The end point adapted to the research goal; 5. A fair assessment of the end point of the study; 6. A follow-up period commensurate with the objectives of the study; 7. Comprehensive laboratory indicators; 8. Sufficient numbers of patients. The project score is 0 (not reported), 1 (reported but insufficient), or 2 (reported and sufficient). The global ideal score for non-comparative studies is 16. In addition, we will draft funnel-plots for laboratory indicators with significant differences between severe and non-severe patients with COVID-19 if there are sufficient included studies (at least 10) and observe the symmetry of the funnel-plots to judge the publication bias.
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Methods
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2.1 Literature search and selection
PubMed and Web of Science were used to search for related articles. The key words are “2019-nCoV” “COVID-19” “SARS-CoV-2” “clinical characteristics” and “laboratory findings”. To ensure the comprehensiveness and accuracy of the study, we also consulted the references of the included literature. The searches were performed three times to identify articles published before April 27, 2020. Then we screen the articles according to the abstract, eliminate the articles that obviously do not meet the inclusion criteria, and then read the full text for re-screening. Articles that provided values of laboratory indicators for severe and non-severe patients, including blood routine, inflammatory factors, biochemical and immune-related indexes were included. Pre-printed articles are also included. Articles published repeatedly, translated articles, studies did not include the laboratory indicators needed for meta-analysis; research data were missing were excluded. In addition, conference summaries, reviews and meta-analysis were excluded.
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Literature search and selection
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PubMed and Web of Science were used to search for related articles. The key words are “2019-nCoV” “COVID-19” “SARS-CoV-2” “clinical characteristics” and “laboratory findings”. To ensure the comprehensiveness and accuracy of the study, we also consulted the references of the included literature. The searches were performed three times to identify articles published before April 27, 2020. Then we screen the articles according to the abstract, eliminate the articles that obviously do not meet the inclusion criteria, and then read the full text for re-screening. Articles that provided values of laboratory indicators for severe and non-severe patients, including blood routine, inflammatory factors, biochemical and immune-related indexes were included. Pre-printed articles are also included. Articles published repeatedly, translated articles, studies did not include the laboratory indicators needed for meta-analysis; research data were missing were excluded. In addition, conference summaries, reviews and meta-analysis were excluded.
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2.2 Analysis content
Statistically analyzed the data related to laboratory indexes (blood routine, inflammatory markers, biochemical detection indexes, blood coagulation function and immune indexes) to compare the differences between severe and non-severe patients and summarize indicators with statistical significance and clinical value. These laboratory indicators were usually showed as the mean and standard deviation, but sometimes were median and interquartile range (IQR). The sample mean was estimated by Luo et al.'s method [11] and variance by Wan et al.'s [12] from the sample size, median and IQR. For these laboratory indexes, the inverse variance method for pooling was used to calculate the overall mean from studies reporting a single. The I2 statistic is a test used to quantify heterogeneity and values of I2 > 50% indicated that heterogeneity existed. When statistical heterogeneity was identified, the random effects model will be used. The meta package (ver 4.11–0; https://cran.r-project.org/) was used to conduct the overall mean. In addition, we analyzed the correlation and regularity of diverse laboratory indexes in patients with COVID-19 to find the considerable advantages of combined analysis in the diagnosis and treatment of patients' condition.
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Analysis content
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Statistically analyzed the data related to laboratory indexes (blood routine, inflammatory markers, biochemical detection indexes, blood coagulation function and immune indexes) to compare the differences between severe and non-severe patients and summarize indicators with statistical significance and clinical value. These laboratory indicators were usually showed as the mean and standard deviation, but sometimes were median and interquartile range (IQR). The sample mean was estimated by Luo et al.'s method [11] and variance by Wan et al.'s [12] from the sample size, median and IQR. For these laboratory indexes, the inverse variance method for pooling was used to calculate the overall mean from studies reporting a single. The I2 statistic is a test used to quantify heterogeneity and values of I2 > 50% indicated that heterogeneity existed. When statistical heterogeneity was identified, the random effects model will be used. The meta package (ver 4.11–0; https://cran.r-project.org/) was used to conduct the overall mean. In addition, we analyzed the correlation and regularity of diverse laboratory indexes in patients with COVID-19 to find the considerable advantages of combined analysis in the diagnosis and treatment of patients' condition.
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2.3 Risk of bias assessment
We will apply the following criteria to assess the risk of bias for each included study. 1. A clear purpose of the study; 2. Including continuous patients; 3. Expected collection of data; 4. The end point adapted to the research goal; 5. A fair assessment of the end point of the study; 6. A follow-up period commensurate with the objectives of the study; 7. Comprehensive laboratory indicators; 8. Sufficient numbers of patients. The project score is 0 (not reported), 1 (reported but insufficient), or 2 (reported and sufficient). The global ideal score for non-comparative studies is 16. In addition, we will draft funnel-plots for laboratory indicators with significant differences between severe and non-severe patients with COVID-19 if there are sufficient included studies (at least 10) and observe the symmetry of the funnel-plots to judge the publication bias.
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Risk of bias assessment
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We will apply the following criteria to assess the risk of bias for each included study. 1. A clear purpose of the study; 2. Including continuous patients; 3. Expected collection of data; 4. The end point adapted to the research goal; 5. A fair assessment of the end point of the study; 6. A follow-up period commensurate with the objectives of the study; 7. Comprehensive laboratory indicators; 8. Sufficient numbers of patients. The project score is 0 (not reported), 1 (reported but insufficient), or 2 (reported and sufficient). The global ideal score for non-comparative studies is 16. In addition, we will draft funnel-plots for laboratory indicators with significant differences between severe and non-severe patients with COVID-19 if there are sufficient included studies (at least 10) and observe the symmetry of the funnel-plots to judge the publication bias.
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3 Results
3.1 Characteristics of included studies
The process of study selection is displayed in Fig. 1 . A total of 715 publications were retrieved, including 645 articles on PubMed, 70 articles on Web of Science. Among these studies, 65 records were excluded due to duplication of records/titles. 615 were removed because they did not meet the inclusion criteria based on title and/or abstract. Finally, we obtained the laboratory test results of 35 articles describing 5912 COVID-19 confirmed patients (up to May 2020). The basic characteristics of the articles included in the study are shown in Table 1 .
Fig. 1 Flow diagram for selection of studies.
Table 1 Summary the characteristics of 35 studies that described the risk factors with COVID-19 patients.
Author Journal Year Country/ region Number of total patients Number of non-severe patients Number of severe patients Age, median (IQI) or mean (SD)
Qin C [13] Clin. Infect. Dis. 2020 Shangha, China 452 166 286 58 (47–67)
Chen X [74] Clin. Infect. Dis. 2020 Wuhan, China 48 21 27 64.6 ± 18.1
Wang R [75] Int. J. Infect. Dis. 2020 Anhui, China 125 100 25 38.8 ± 13.8
Gao Y [68] J. Med. Virol. 2020 Anhui, China 43 28 15
Zheng YL [17] J. Clin. Virol. 2020 Chengdu, China 99 67 32 49.4 ± 18.45
Ma J [15] J. Infect. 2020 Wuhan, China 37 17 20 62 (59–70)
Wang DL [18] Lancet 2020 Jiangsu, China 620 567 53 44.4 ± 17.2
Liu W [56] Chin. Med. J. 2020 Wuhan, China 78 67 11 38 (33–57)
Yang AP [76] Int. Immunopharmacol. 2020 China 93 69 24 46.4 ± 17.6
Li KH [77] Invest Radiol 2020 Chongqing, China 83 58 25 45.5 ± 12.3
Zhang JJ [62] Allergy 2020 Wuhan, China 140 82 58 57 (25–87)
Huang CL [10] Lancet 2020 Wuhan, China 41 28 13 49 (41–58)
Wang DW [64] JAMA 2020 Wuhan, China 138 102 36 56 (42–68)
Liu M [14] Zhonghua Jie He He Hu Xi Za Zhi 2020 Wuhan, China 30 26 4 35 ± 8
Mo PZ [78] Clin. Infect. Dis. 2020 Wuhan, China 155 70 85 54 (42–66)
Peng YD [79] Zhonghua Xin Xue Guan Bing Za Zhi 2020 Shanghai, China 112 96 16 62 (55–67)
Feng Y [80] Am. J. Respir. Crit. Care Med. 2020 China 476 352 124 53 (40–64)
Cai QX [81] Allergy 2020 Shenzhen, China 298 240 58 47.5 (33–61)
Li H [82] J. Infect. 2020 Wuhan, China 132 60 72 62 ± 12.7
Zheng F [83] Eur Rev Med Pharmacol Sci 2020 Hunan, China 161 131 30 45 (33.5–57)
Wang KK [84] Lancet 2020 Hong Kong, China 23 13 10
Guan W [85] New Engl J Med 2020 Wuhan, China 1099 926 173 47 (35–58)
Gong J [86] Clin. Infect. Dis. 2020 Wuhan and Guangdong, China 189 161 28 49 (35–63)
Lei SQ [87] E Clin Med 2020 China 34 19 15 55 (43–63)
Deng Q [88] Int. J. Cardiol. 2020 Wuhan, China 112 45 67 65 (49–70)
Mao L [89] JAMA Neurol. 2020 Wuhan, China 214 126 88 52.7 ± 15.5
Du RH [90] Ann. Am. Thorac. Soc. 2020 Wuhan, China 109 58 51 70.7 ± 10.9
Xie HS [91] Liver Int. 2020 Wuhan, China 79 51 28 60 (48–66)
Wu J [92] J. Intern. Med. 2020 China 280 197 83 43 ± 19
Chen G [93] J. Clin. Investig. 2020 Wuhan, China 21 10 11 56 (50–65)
Wan S [94] J. Med. Virol. 2020 Chongqing, China 135 95 40 47 (36–55)
Pan HQ [42] Lancet Infect. Dis. 2020 Wuhan, China 221 166 55 55 (39–66.5)
Bo XU [16] J. Infect. 2020 Wuhan, China 187 80 107 62 (48.5–71)
GQQ [95] Int. J. Med. 2020 Zhejiang, China 91 82 9 50 (36.5–57)
Lo LI [96] Int. J. Biol. Sci. 2020 Macau, China 10 6 4 54 (27–64)
Among these articles, 34 articles described the results of blood routine and infection-related biomarkers, 26 articles described the results of biochemical tests, 23 articles provided test results for blood coagulation and 5 articles described the results of immunoassay. A total of 5,912 patients (mean age:54.80; 95%CI (52.50–57.20)), including 4337 non-severe patients (mean age:48.50; 95%CI (45.70–51.30)) and 1663 severe patients (mean age:61.00; 95%CI (59.10–62.90)). More than half of them were male (3072/5912(51.96%)). 2531 of these patients with underlying disease, including hypertension, diabetes, cardiovascular disease, cerebrovascular disease, chronic obstructive pulmonary disease (COPD), chronic kidney disease and liver disease, Malignant tumors and patients with low immunity.
3.2 Analysis of laboratory indicators
Based on the comprehensive collation of the laboratory data provided in the selected 35 articles, the average value and variation range of various indexes of total patients, non-severe patients and severe patients were obtained and shown in Table 3 . In addition, the Funnel plot of important laboratory indicators are shown in Fig. 2, Fig. 3, Fig. 4, Fig. 5, Fig. 6, Fig. 7, Fig. 8, Fig. 9, Fig. 10 .
Table 2 The quality assessment of included studies.
Author 1 2 3 4 5 6 7 8 Score
Qin C [13] 2 2 2 2 2 0 2 2 14
Chen X [74] 2 2 2 2 2 0 1 1 12
Wang R [75] 2. 2 2 2 2 2 2 2 16
Gao Y [68] 2 2 2 2 2 0 2 1 13
Zheng YL [17] 2 2 2 2 2 2 2 1 15
Ma J [15] 2 2 2 2 2 0 2 1 13
Wang DL [18] 2 2 2 2 2 2 2 2 16
Liu W [56] 2 2 2 2 2 0 2 1 13
Yang AP [76] 2 2 2 2 2 1 1 1 13
Li KH [77] 2 2 2 2 2 0 2 1 13
Zhang JJ [62] 2 2 2 2 2 0 2 2 14
Huang CL [10] 2 2 2 2 2 0 2 1 13
Wang DW [64] 2 2 2 2 2 1 2 2 15
Liu M [14] 2 2 2 2 2 0 2 1 13
Mo PZ [78] 2 2 2 2 2 1 2 2 15
Peng YD [79] 2 2 2 2 2 2 2 1 15
Feng Y [80] 2 2 2 2 2 0 2 2 14
Cai QX [81] 2 2 2 2 2 2 2 2 16
Li H [82] 2 2 2 2 2 2 1 2 15
Zheng F [83] 2 2 2 2 2 0 2 2 14
Wang KK [84] 2 2 2 2 2 2 2 1 15
Guan W [85] 2 2 2 2 2 2 2 2 16
Gong J [86] 2 2 2 2 2 2 2 2 16
Lei SQ [87] 2 2 2 2 2 2 2 1 15
Deng Q [88] 2 2 2 2 2 2 1 2 15
Mao L [89] 2 2 2 2 2 0 1 2 13
Du RH [90] 2 2 2 2 2 2 2 2 16
Xie HS [91] 2 2 2 2 2 0 2 1 13
Wu J [92] 2 2 2 2 2 0 2 2 14
Chen G [93] 2 2 2 2 2 0 1 1 12
Wan S [94] 2 2 2 2 2 1 2 2 15
Pan HQ [42] 2 2 2 2 2 1 2 2 15
Bo XU [16] 2 2 2 2 2 2 2 2 16
GQQ [95] 2 2 2 2 2 0 2 1 13
Lo LI [96] 2 2 2 2 2 2 1 1 14
1. A clear purpose of the study; 2. Including continuous patients; 3. Expected collection of data; 4. The end point adapted to the research goal; 5. A fair assessment of the end point of the study; 6. A follow-up period commensurate with the objectives of the study; 7. Comprehensive laboratory indicators; 8. Sufficient numbers of patients.
Table3 Results of Laboratory findings of severe and non-severe patients infected with COVID-19.
Variables Classification Number of articles included Number of patients included Mean (95% CI) P-value
Blood routine WBC×109 /l Non-severe patients 34 4236 5.07 (4.90, 5.24) <0.01
Severe patients 1703 6.06 (5.67, 6.46)
All patients 5939 5.39 (5.22, 5.55)
Neutrophils×109 /l Non-severe patients 29 3005 3.71 (3.36, 4.06) <0.01
Severe patients 1368 4.94 (4.30, 5.58)
All patients 4373 4.22 (3.94, 4.51)
Lymphocytes×109 /l Non-severe patients 34 4228 1.15 (1.08, 1.22) <0.01
Severe patients 1703 0.80 (0.75, 0.84)
All patients 5931 0.98 (0.92, 1.04)
Eosinophil×109 /l Non-severe patients 5 1035 0.04 (0.03, 0.05) 0.003
Severe patients 451 0.02 (0.01, 0.02)
All patients 1486 0.03 (0.02, 0.04)
Monocyte×109 /l Non-severe patients 13 1397 0.41 (0.40, 0.42) 0.086
Severe patients 700 0.38 (0.35, 0.41)
All patients 2097 0.40 (0.39, 0.42)
PLT×109 /l Non-severe patients 20 3068 184.19 (178.04, 190.33) 0.340
Severe patients 1019 212.58 (154.63, 270.53)
All patients 4087 201.44 (175.75, 227.12)
Hbg/l Non-severe patients 14 2652 131.36 (128.33, 134.39) 0.164
Severe patients 711 126.73 (121.16, 132.30)
All patients 3363 128.98 (126.17, 131.78)
Inflammation-related factors ESR mm/60 min Non-severe patients 8 1390 28.16 (20.13, 36.2) 0.111
Severe patients 695 40.54 (27.61, 53.4)
All patients 2085 33.94 (28.10, 39.7)
CRP mg/l Non-severe patients 26 2939 19.83 (16.67, 23.0) <0.01
Severe patients 1415 60.91 (49.24, 72.5)
All patients 4354 36.99 (33.31, 40.67)
PCT ng/ml Non-severe patients 22 2325 0.07 (0.05, 0.09) <0.01
Severe patients 1222 0.14 (0.11, 0.17)
All patients 3547 0.10 (0.08, 0.12)
Blood biochemistry ALT U/l Non-severe patients 26 2769 24.85 (22.69, 27.0) <0.01
Severe patients 1027 33.78 (29.54, 38.0)
All patients 3796 28.31 (26.25, 30.3)
AST U/l Non-severe patients 26 2731 26.24 (25.29, 28.18) <0.01
Severe patients 1019 36.78 (33.69, 39.87)
All patients 3750 30.66 (29.19, 32.13)
LDH U/l Non-severe patients 19 2306 224.20 (205.33, 243.07) <0.01
Severe patients 811 344.48 (307.08, 381.88)
All patients 3117 271.82 (254.13, 289.52)
CK U/l Non-severe patients 17 2241 77.69 (69.68, 85.70) <0.01
Severe patients 849 111.92 (98.24, 125.61)
All patients 3090 90.92 (83.54, 98.29)
CK-MB U/l Non-severe patients 9 1329 8.76 (4.74, 12.79) 0.246
Severe patients 550 12.26 (7.93, 16.58)
All patients 1879 10.51 (8.33, 12.70)
Albumin g/l Non-severe patients 10 985 39.41 (37.95, 40.87) <0.01
Severe patients 431 34.29 (32.79, 35.80)
All patients 1416 36.75 (35.17, 38.32)
Creatinine μmol/l Non-severe patients 25 2696 66.97 (64.65, 69.28) <0.01
Severe patients 1000 72.94 (69.23, 76.66)
All patients 3696 69.61 (67.57, 71.65)
Urea mmol/l Non-severe patients 16 1929 4.36 (4.12, 4.59) <0.01
Severe patients 703 5.59 (5.39, 6.51)
All patients 2632 4.98 (4.72, 5.23)
Total bilirubin mmol/l Non-severe patients 14 2065 10.38 (9.78, 10.99) 0.017
Severe patients 564 11.86 (10.81, 12.91)
All patients 2629 10.92 (10.39, 11.45)
Blood coagulation function APTTs Non-severe patients 12 1422 33.49 (31.17, 35.82) 0.724
Severe patients 407 32.92 (30.78, 35.06)
All patients 1829 33.23 (31.59, 34.86)
PT s Non-severe patients 12 1388 12.45 (11.98, 12.91) 0.319
Severe patients 504 12.80 (12.29, 13.30)
All patients 1892 12.63 (12.31, 12.94)
D dimer mg/l Non-severe patients 23 2503 0.47 (0.40, 0.53) <0.01
Severe patients 1043 1.29 (0.03, 0.54)
All patients 3546 0.61 (0.54, 0.67)
Lymphocyte subsets CD4 T cells /μL Non-severe patients 5 475 561.81 (485.46, 638.15) <0.01
Severe patients 208 266.79 (204.51, 329.07)
All patients 683 407.03 (310.24, 503.83)
CD8 T cells /μL Non-severe patients 5 475 349.01 (292.61, 405.40) <0.01
Severe patients 208 174.61 (125.95, 223.27)
All patients 683 266.65 (198.49, 334.81)
Cytokines IL-1β pg/ml Non-severe patients 2 246 5.01 (4.96, 5.06) 0.055
Severe patients 393 5.11 (5.02, 5.20)
All patients 639 5.06 (4.98, 5.15)
IL-6 pg/ml Non-severe patients 5 312 13.22 (6.88, 19.57) 0.027
Severe patients 455 25.58 (16.69, 34.47)
All patients 767 19.66 (13.44, 25.89)
IL-10 pg/ml Non-severe patients 2 246 5.71 (5.51, 5.90) <0.01
Severe patients 393 8.87 (7.15, 10.59)
All patients 639 7.23 (6.18, 8.28)
SPSS25 was used. Comparison between severe and non-severe patients with t test or Mann-Whitney U test.
Fig. 2 Meta-analysis of lymphocytes.
Fig. 3 Meta-analysis of CRP.
Fig. 4 Meta-analysis of PCT.
Fig. 5 Meta-analysis of ALT.
Fig. 6 Meta-analysis of AST.
Fig. 7 Meta-analysis of LDH.
Fig. 8 Meta-analysis of D-dimer.
Fig. 9 Meta-analysis of CD4 T cells.
Fig. 10 Meta-analysis of IL-6.
3.2.1 Blood routine examination
Leukopenia was observed in 21.92% (363/1656) patients with lymphocytopenia in 29.02% (886/3053) patients. Elevated neutrophils were observed in 19.85% (81/408) patients. 14.73% (75/509) and 12.68% (78/615) patients were accompanied by a decrease in Hemoglobin and platelet count (PLT) respectively. Most importantly, there were several significant differences between severe patients and non-severe patients, including higher leukocyte (1.20-fold; 6.06 vs 5.07 × 109/l; P < 0.01) and neutrophil (1.33-fold; 4.94 vs 3.71 × 109/l; P < 0.01), lower lymphocyte (1.44-fold; 1.15 vs 0.80 × 109/l; P < 0.01), eosinophils (2.00-fold; 0.04 vs 0.02 × 109/l; P = 0.03), monocytes (1.08-fold; 0.38 vs 0.41 × 109/l; P = 0.041), PLT (1.15-fold; 212.58 vs 184.19 × 109/l; P = 0.987) and hemoglobin (1.53-fold; 131.36vs 126.73 × 109 g/l; P = 0.163).
3.2.2 Inflammatory biomarkers examination
Increased C-reactive protein (CRP) concentration appeared in 57.40% (1494/2603) patients, procalcitonin (PCT) increased in 12.20% (256/2099) patients, and 39.26% (117/298) patients had an increase in erythrocyte sedimentation rate (ESR). Moreover, higher levels of CRP (3.04-fold; 60.91 vs 19.83 mg/l; P < 0.01), PCT (2.00-fold; 0.14vs 0.07 ng/ml; P < 0.01) and ESR (1.44-fold; 40.54 vs 28.16 mm/60 min; P = 0.096) were observed in severe patients in comparison with non-severe patients.
3.2.3 Blood biochemical examination
3.2.3.1 Cardiac markers examination
Our statistics showed that the related indexes of myocardial injury increased in different numbers of patients with COVID-19. (respectively creatine kinase (CK) (7.74% (157/2029)); aspartate aminotransferase (AST) (14.87% (388/2609)); lactate dehydrogenase (LDH) (24.50% (468/1910)). Several significant differences were noted between severe and non-severe patients, especially higher values of AST (1.40-fold; 36.78 vs 26.24 U/l; P < 0.01), LDH (1.54-fold; 344.48 vs 224.20 U/l; P < 0.01), CK (1.44-fold; 111.92 vs 77.69 U/l; P < 0.01) and CK-MB (1.39-fold; 12.26 vs 8.76 U/l; P = 0.317).
3.2.3.2 Liver function
The increase of alanine aminotransferase (ALT) (12.27% (296/2412)) and AST (14.87% (388/2609)) with COVID-19 has been observed. Moreover, the decrease of albumin (143/221 (64.70%)) was more common while the increase of total bilirubin (TBIL) was relatively rare in the majority of patients (109/1558 (6.70%)). Comparing with non-severe patients, higher ALT (1.34-fold; 33.78 vs 24.85 U/l; P < 0.01), AST (1.40-fold; 36.78 vs 26.24 U/l; P < 0.01), TBIL (1.14-fold; 11.86 vs 10.38 U/l; P = 0.024) and lower albumin (1.15-fold; 39.41vs 34.29 g/l; P < 0.01) of severe patients has been worked out.
3.2.3.3 Renal function
The increase of creatinine (2.41% (40/1659)) and urea (13.50% (47/348)) were observed among the included patients with COVID-19. Besides, albumin reduction (64.70% (143/221)) was very common. More importantly, higher levels of creatinine (1.09-fold;72.94 vs 66.97 μmol; P < 0.01), urea (1.28-fold; 5.59 vs 4.36 mmol; P < 0.01) and lower concentrations of albumin (1.15-fold; 39.41 vs 34.29 g/l; P < 0.01) of severe patients were summed up in comparison with non-severe patients.
3.2.4 Blood coagulation function
Prothrombin time (PT) prolonged in 22.65% (53/234) patients and shortened in 10.68% (25/234) patients while activated partial thromboplastin time (APTT) prolonged in 21.79% (51/234) patients and shortened in 5.56% (13/234) patients. D dimer increased in 28.94% (534/1845) patients. Abnormal coagulation function is more obvious in severe patients, including shorter APTT (1.02-fold;33.49 vs 32.92 s; P = 0.804), increased D-dimer (2.74-fold; 1.29 vs 0.47 mg/l; P < 0.01) and longer PT (1.03-fold; 12.80 vs 12.45 s; P = 0.407).
3.2.5 Immunological examination
3.2.5.1 Antibody detection
The values of antibodies and complements in blood serum in Qin's [13] study showed that immunoglobulins (IgA, IgG and IgM) and complement proteins (C3 and C4) of COVID-19 patients are within the normal range. Compared with the non-severe group, the IgM of the severe group was only slightly decreased, and there was no significant difference in other immunoglobulins and complement, which was consistent with the results of Feng's study [14].
3.2.5.2 Lymphocyte subsets
The total number of B cells, T cells and NK cells significantly decreased in patients with COVID-19 (852.9 /uL), and more evident in the severe cases (1.37-fold; 743.6 vs 1020.1 /uL; P = 0.032) compared to the non-severe group [13]. Lower levels of CD4 T cells (2.10-fold; 561.81 vs 266.79 cell/μl; P < 0.01), CD8 T cells (2.00-fold; 349.01 vs 174.61cell/μl; P < 0.01) were summarized in severe patients comparing with non-severe patients from 5 articles [14], [15], [16], [17], [18]. In addition, lower CD3 T cells (1.70-fold; 1070.23 vs 628.20 cell/μl; P < 0.01) in severe patients was noted in Liu et al. [18].
3.2.5.3 Cytokine
Series of inflammatory cytokines were also increaseted in severe cases than the non-severe ones, including interleukin (IL)-1β (1.02-fold; 5.11 vs 5.01 pg/ml; P = 0.098), IL-6 (1.93-fold; 25.58 vs 13.22 pg/ml; P = 0.043), IL-10 (1.55-fold; 8.87 vs 5.71 pg/ml; P < 0.01). In addition, Qin et al. found higher levels of IL-2R (1.14-fold; 757.0 vs 663.5 U/ml; P < 0.01), IL-8 (1.34-fold; 18.4 vs 13.7 pg/ml; P < 0.01) and TNF-α (1.04-fold; 8.7 vs 8.4 pg/ml; P = 0.037) in severe patients in comparison with non-severe patients [13]. Studies also reported that GSCF, IP-10, MCP1and MIP1A in severe patients were higher [10].
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3.1 Characteristics of included studies
The process of study selection is displayed in Fig. 1 . A total of 715 publications were retrieved, including 645 articles on PubMed, 70 articles on Web of Science. Among these studies, 65 records were excluded due to duplication of records/titles. 615 were removed because they did not meet the inclusion criteria based on title and/or abstract. Finally, we obtained the laboratory test results of 35 articles describing 5912 COVID-19 confirmed patients (up to May 2020). The basic characteristics of the articles included in the study are shown in Table 1 .
Fig. 1 Flow diagram for selection of studies.
Table 1 Summary the characteristics of 35 studies that described the risk factors with COVID-19 patients.
Author Journal Year Country/ region Number of total patients Number of non-severe patients Number of severe patients Age, median (IQI) or mean (SD)
Qin C [13] Clin. Infect. Dis. 2020 Shangha, China 452 166 286 58 (47–67)
Chen X [74] Clin. Infect. Dis. 2020 Wuhan, China 48 21 27 64.6 ± 18.1
Wang R [75] Int. J. Infect. Dis. 2020 Anhui, China 125 100 25 38.8 ± 13.8
Gao Y [68] J. Med. Virol. 2020 Anhui, China 43 28 15
Zheng YL [17] J. Clin. Virol. 2020 Chengdu, China 99 67 32 49.4 ± 18.45
Ma J [15] J. Infect. 2020 Wuhan, China 37 17 20 62 (59–70)
Wang DL [18] Lancet 2020 Jiangsu, China 620 567 53 44.4 ± 17.2
Liu W [56] Chin. Med. J. 2020 Wuhan, China 78 67 11 38 (33–57)
Yang AP [76] Int. Immunopharmacol. 2020 China 93 69 24 46.4 ± 17.6
Li KH [77] Invest Radiol 2020 Chongqing, China 83 58 25 45.5 ± 12.3
Zhang JJ [62] Allergy 2020 Wuhan, China 140 82 58 57 (25–87)
Huang CL [10] Lancet 2020 Wuhan, China 41 28 13 49 (41–58)
Wang DW [64] JAMA 2020 Wuhan, China 138 102 36 56 (42–68)
Liu M [14] Zhonghua Jie He He Hu Xi Za Zhi 2020 Wuhan, China 30 26 4 35 ± 8
Mo PZ [78] Clin. Infect. Dis. 2020 Wuhan, China 155 70 85 54 (42–66)
Peng YD [79] Zhonghua Xin Xue Guan Bing Za Zhi 2020 Shanghai, China 112 96 16 62 (55–67)
Feng Y [80] Am. J. Respir. Crit. Care Med. 2020 China 476 352 124 53 (40–64)
Cai QX [81] Allergy 2020 Shenzhen, China 298 240 58 47.5 (33–61)
Li H [82] J. Infect. 2020 Wuhan, China 132 60 72 62 ± 12.7
Zheng F [83] Eur Rev Med Pharmacol Sci 2020 Hunan, China 161 131 30 45 (33.5–57)
Wang KK [84] Lancet 2020 Hong Kong, China 23 13 10
Guan W [85] New Engl J Med 2020 Wuhan, China 1099 926 173 47 (35–58)
Gong J [86] Clin. Infect. Dis. 2020 Wuhan and Guangdong, China 189 161 28 49 (35–63)
Lei SQ [87] E Clin Med 2020 China 34 19 15 55 (43–63)
Deng Q [88] Int. J. Cardiol. 2020 Wuhan, China 112 45 67 65 (49–70)
Mao L [89] JAMA Neurol. 2020 Wuhan, China 214 126 88 52.7 ± 15.5
Du RH [90] Ann. Am. Thorac. Soc. 2020 Wuhan, China 109 58 51 70.7 ± 10.9
Xie HS [91] Liver Int. 2020 Wuhan, China 79 51 28 60 (48–66)
Wu J [92] J. Intern. Med. 2020 China 280 197 83 43 ± 19
Chen G [93] J. Clin. Investig. 2020 Wuhan, China 21 10 11 56 (50–65)
Wan S [94] J. Med. Virol. 2020 Chongqing, China 135 95 40 47 (36–55)
Pan HQ [42] Lancet Infect. Dis. 2020 Wuhan, China 221 166 55 55 (39–66.5)
Bo XU [16] J. Infect. 2020 Wuhan, China 187 80 107 62 (48.5–71)
GQQ [95] Int. J. Med. 2020 Zhejiang, China 91 82 9 50 (36.5–57)
Lo LI [96] Int. J. Biol. Sci. 2020 Macau, China 10 6 4 54 (27–64)
Among these articles, 34 articles described the results of blood routine and infection-related biomarkers, 26 articles described the results of biochemical tests, 23 articles provided test results for blood coagulation and 5 articles described the results of immunoassay. A total of 5,912 patients (mean age:54.80; 95%CI (52.50–57.20)), including 4337 non-severe patients (mean age:48.50; 95%CI (45.70–51.30)) and 1663 severe patients (mean age:61.00; 95%CI (59.10–62.90)). More than half of them were male (3072/5912(51.96%)). 2531 of these patients with underlying disease, including hypertension, diabetes, cardiovascular disease, cerebrovascular disease, chronic obstructive pulmonary disease (COPD), chronic kidney disease and liver disease, Malignant tumors and patients with low immunity.
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The process of study selection is displayed in Fig. 1 . A total of 715 publications were retrieved, including 645 articles on PubMed, 70 articles on Web of Science. Among these studies, 65 records were excluded due to duplication of records/titles. 615 were removed because they did not meet the inclusion criteria based on title and/or abstract. Finally, we obtained the laboratory test results of 35 articles describing 5912 COVID-19 confirmed patients (up to May 2020). The basic characteristics of the articles included in the study are shown in Table 1 .
Fig. 1 Flow diagram for selection of studies.
Table 1 Summary the characteristics of 35 studies that described the risk factors with COVID-19 patients.
Author Journal Year Country/ region Number of total patients Number of non-severe patients Number of severe patients Age, median (IQI) or mean (SD)
Qin C [13] Clin. Infect. Dis. 2020 Shangha, China 452 166 286 58 (47–67)
Chen X [74] Clin. Infect. Dis. 2020 Wuhan, China 48 21 27 64.6 ± 18.1
Wang R [75] Int. J. Infect. Dis. 2020 Anhui, China 125 100 25 38.8 ± 13.8
Gao Y [68] J. Med. Virol. 2020 Anhui, China 43 28 15
Zheng YL [17] J. Clin. Virol. 2020 Chengdu, China 99 67 32 49.4 ± 18.45
Ma J [15] J. Infect. 2020 Wuhan, China 37 17 20 62 (59–70)
Wang DL [18] Lancet 2020 Jiangsu, China 620 567 53 44.4 ± 17.2
Liu W [56] Chin. Med. J. 2020 Wuhan, China 78 67 11 38 (33–57)
Yang AP [76] Int. Immunopharmacol. 2020 China 93 69 24 46.4 ± 17.6
Li KH [77] Invest Radiol 2020 Chongqing, China 83 58 25 45.5 ± 12.3
Zhang JJ [62] Allergy 2020 Wuhan, China 140 82 58 57 (25–87)
Huang CL [10] Lancet 2020 Wuhan, China 41 28 13 49 (41–58)
Wang DW [64] JAMA 2020 Wuhan, China 138 102 36 56 (42–68)
Liu M [14] Zhonghua Jie He He Hu Xi Za Zhi 2020 Wuhan, China 30 26 4 35 ± 8
Mo PZ [78] Clin. Infect. Dis. 2020 Wuhan, China 155 70 85 54 (42–66)
Peng YD [79] Zhonghua Xin Xue Guan Bing Za Zhi 2020 Shanghai, China 112 96 16 62 (55–67)
Feng Y [80] Am. J. Respir. Crit. Care Med. 2020 China 476 352 124 53 (40–64)
Cai QX [81] Allergy 2020 Shenzhen, China 298 240 58 47.5 (33–61)
Li H [82] J. Infect. 2020 Wuhan, China 132 60 72 62 ± 12.7
Zheng F [83] Eur Rev Med Pharmacol Sci 2020 Hunan, China 161 131 30 45 (33.5–57)
Wang KK [84] Lancet 2020 Hong Kong, China 23 13 10
Guan W [85] New Engl J Med 2020 Wuhan, China 1099 926 173 47 (35–58)
Gong J [86] Clin. Infect. Dis. 2020 Wuhan and Guangdong, China 189 161 28 49 (35–63)
Lei SQ [87] E Clin Med 2020 China 34 19 15 55 (43–63)
Deng Q [88] Int. J. Cardiol. 2020 Wuhan, China 112 45 67 65 (49–70)
Mao L [89] JAMA Neurol. 2020 Wuhan, China 214 126 88 52.7 ± 15.5
Du RH [90] Ann. Am. Thorac. Soc. 2020 Wuhan, China 109 58 51 70.7 ± 10.9
Xie HS [91] Liver Int. 2020 Wuhan, China 79 51 28 60 (48–66)
Wu J [92] J. Intern. Med. 2020 China 280 197 83 43 ± 19
Chen G [93] J. Clin. Investig. 2020 Wuhan, China 21 10 11 56 (50–65)
Wan S [94] J. Med. Virol. 2020 Chongqing, China 135 95 40 47 (36–55)
Pan HQ [42] Lancet Infect. Dis. 2020 Wuhan, China 221 166 55 55 (39–66.5)
Bo XU [16] J. Infect. 2020 Wuhan, China 187 80 107 62 (48.5–71)
GQQ [95] Int. J. Med. 2020 Zhejiang, China 91 82 9 50 (36.5–57)
Lo LI [96] Int. J. Biol. Sci. 2020 Macau, China 10 6 4 54 (27–64)
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Fig. 1 Flow diagram for selection of studies.
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Flow diagram for selection of studies.
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Table 1 Summary the characteristics of 35 studies that described the risk factors with COVID-19 patients.
Author Journal Year Country/ region Number of total patients Number of non-severe patients Number of severe patients Age, median (IQI) or mean (SD)
Qin C [13] Clin. Infect. Dis. 2020 Shangha, China 452 166 286 58 (47–67)
Chen X [74] Clin. Infect. Dis. 2020 Wuhan, China 48 21 27 64.6 ± 18.1
Wang R [75] Int. J. Infect. Dis. 2020 Anhui, China 125 100 25 38.8 ± 13.8
Gao Y [68] J. Med. Virol. 2020 Anhui, China 43 28 15
Zheng YL [17] J. Clin. Virol. 2020 Chengdu, China 99 67 32 49.4 ± 18.45
Ma J [15] J. Infect. 2020 Wuhan, China 37 17 20 62 (59–70)
Wang DL [18] Lancet 2020 Jiangsu, China 620 567 53 44.4 ± 17.2
Liu W [56] Chin. Med. J. 2020 Wuhan, China 78 67 11 38 (33–57)
Yang AP [76] Int. Immunopharmacol. 2020 China 93 69 24 46.4 ± 17.6
Li KH [77] Invest Radiol 2020 Chongqing, China 83 58 25 45.5 ± 12.3
Zhang JJ [62] Allergy 2020 Wuhan, China 140 82 58 57 (25–87)
Huang CL [10] Lancet 2020 Wuhan, China 41 28 13 49 (41–58)
Wang DW [64] JAMA 2020 Wuhan, China 138 102 36 56 (42–68)
Liu M [14] Zhonghua Jie He He Hu Xi Za Zhi 2020 Wuhan, China 30 26 4 35 ± 8
Mo PZ [78] Clin. Infect. Dis. 2020 Wuhan, China 155 70 85 54 (42–66)
Peng YD [79] Zhonghua Xin Xue Guan Bing Za Zhi 2020 Shanghai, China 112 96 16 62 (55–67)
Feng Y [80] Am. J. Respir. Crit. Care Med. 2020 China 476 352 124 53 (40–64)
Cai QX [81] Allergy 2020 Shenzhen, China 298 240 58 47.5 (33–61)
Li H [82] J. Infect. 2020 Wuhan, China 132 60 72 62 ± 12.7
Zheng F [83] Eur Rev Med Pharmacol Sci 2020 Hunan, China 161 131 30 45 (33.5–57)
Wang KK [84] Lancet 2020 Hong Kong, China 23 13 10
Guan W [85] New Engl J Med 2020 Wuhan, China 1099 926 173 47 (35–58)
Gong J [86] Clin. Infect. Dis. 2020 Wuhan and Guangdong, China 189 161 28 49 (35–63)
Lei SQ [87] E Clin Med 2020 China 34 19 15 55 (43–63)
Deng Q [88] Int. J. Cardiol. 2020 Wuhan, China 112 45 67 65 (49–70)
Mao L [89] JAMA Neurol. 2020 Wuhan, China 214 126 88 52.7 ± 15.5
Du RH [90] Ann. Am. Thorac. Soc. 2020 Wuhan, China 109 58 51 70.7 ± 10.9
Xie HS [91] Liver Int. 2020 Wuhan, China 79 51 28 60 (48–66)
Wu J [92] J. Intern. Med. 2020 China 280 197 83 43 ± 19
Chen G [93] J. Clin. Investig. 2020 Wuhan, China 21 10 11 56 (50–65)
Wan S [94] J. Med. Virol. 2020 Chongqing, China 135 95 40 47 (36–55)
Pan HQ [42] Lancet Infect. Dis. 2020 Wuhan, China 221 166 55 55 (39–66.5)
Bo XU [16] J. Infect. 2020 Wuhan, China 187 80 107 62 (48.5–71)
GQQ [95] Int. J. Med. 2020 Zhejiang, China 91 82 9 50 (36.5–57)
Lo LI [96] Int. J. Biol. Sci. 2020 Macau, China 10 6 4 54 (27–64)
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Summary the characteristics of 35 studies that described the risk factors with COVID-19 patients.
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Summary the characteristics of 35 studies that described the risk factors with COVID-19 patients.
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Author Journal Year Country/ region Number of total patients Number of non-severe patients Number of severe patients Age, median (IQI) or mean (SD)
Qin C [13] Clin. Infect. Dis. 2020 Shangha, China 452 166 286 58 (47–67)
Chen X [74] Clin. Infect. Dis. 2020 Wuhan, China 48 21 27 64.6 ± 18.1
Wang R [75] Int. J. Infect. Dis. 2020 Anhui, China 125 100 25 38.8 ± 13.8
Gao Y [68] J. Med. Virol. 2020 Anhui, China 43 28 15
Zheng YL [17] J. Clin. Virol. 2020 Chengdu, China 99 67 32 49.4 ± 18.45
Ma J [15] J. Infect. 2020 Wuhan, China 37 17 20 62 (59–70)
Wang DL [18] Lancet 2020 Jiangsu, China 620 567 53 44.4 ± 17.2
Liu W [56] Chin. Med. J. 2020 Wuhan, China 78 67 11 38 (33–57)
Yang AP [76] Int. Immunopharmacol. 2020 China 93 69 24 46.4 ± 17.6
Li KH [77] Invest Radiol 2020 Chongqing, China 83 58 25 45.5 ± 12.3
Zhang JJ [62] Allergy 2020 Wuhan, China 140 82 58 57 (25–87)
Huang CL [10] Lancet 2020 Wuhan, China 41 28 13 49 (41–58)
Wang DW [64] JAMA 2020 Wuhan, China 138 102 36 56 (42–68)
Liu M [14] Zhonghua Jie He He Hu Xi Za Zhi 2020 Wuhan, China 30 26 4 35 ± 8
Mo PZ [78] Clin. Infect. Dis. 2020 Wuhan, China 155 70 85 54 (42–66)
Peng YD [79] Zhonghua Xin Xue Guan Bing Za Zhi 2020 Shanghai, China 112 96 16 62 (55–67)
Feng Y [80] Am. J. Respir. Crit. Care Med. 2020 China 476 352 124 53 (40–64)
Cai QX [81] Allergy 2020 Shenzhen, China 298 240 58 47.5 (33–61)
Li H [82] J. Infect. 2020 Wuhan, China 132 60 72 62 ± 12.7
Zheng F [83] Eur Rev Med Pharmacol Sci 2020 Hunan, China 161 131 30 45 (33.5–57)
Wang KK [84] Lancet 2020 Hong Kong, China 23 13 10
Guan W [85] New Engl J Med 2020 Wuhan, China 1099 926 173 47 (35–58)
Gong J [86] Clin. Infect. Dis. 2020 Wuhan and Guangdong, China 189 161 28 49 (35–63)
Lei SQ [87] E Clin Med 2020 China 34 19 15 55 (43–63)
Deng Q [88] Int. J. Cardiol. 2020 Wuhan, China 112 45 67 65 (49–70)
Mao L [89] JAMA Neurol. 2020 Wuhan, China 214 126 88 52.7 ± 15.5
Du RH [90] Ann. Am. Thorac. Soc. 2020 Wuhan, China 109 58 51 70.7 ± 10.9
Xie HS [91] Liver Int. 2020 Wuhan, China 79 51 28 60 (48–66)
Wu J [92] J. Intern. Med. 2020 China 280 197 83 43 ± 19
Chen G [93] J. Clin. Investig. 2020 Wuhan, China 21 10 11 56 (50–65)
Wan S [94] J. Med. Virol. 2020 Chongqing, China 135 95 40 47 (36–55)
Pan HQ [42] Lancet Infect. Dis. 2020 Wuhan, China 221 166 55 55 (39–66.5)
Bo XU [16] J. Infect. 2020 Wuhan, China 187 80 107 62 (48.5–71)
GQQ [95] Int. J. Med. 2020 Zhejiang, China 91 82 9 50 (36.5–57)
Lo LI [96] Int. J. Biol. Sci. 2020 Macau, China 10 6 4 54 (27–64)
|
|
tr
|
Author Journal Year Country/ region Number of total patients Number of non-severe patients Number of severe patients Age, median (IQI) or mean (SD)
|
|
th
|
Author
|
|
th
|
Journal
|
|
th
|
Year
|
|
th
|
Country/ region
|
|
th
|
Number of total patients
|
|
th
|
Number of non-severe patients
|
|
th
|
Number of severe patients
|
|
th
|
Age, median (IQI) or mean (SD)
|
|
tr
|
Qin C [13] Clin. Infect. Dis. 2020 Shangha, China 452 166 286 58 (47–67)
|
|
td
|
Qin C [13]
|
|
td
|
Clin. Infect. Dis.
|
|
td
|
2020
|
|
td
|
Shangha, China
|
|
td
|
452
|
|
td
|
166
|
|
td
|
286
|
|
td
|
58 (47–67)
|
|
tr
|
Chen X [74] Clin. Infect. Dis. 2020 Wuhan, China 48 21 27 64.6 ± 18.1
|
|
td
|
Chen X [74]
|
|
td
|
Clin. Infect. Dis.
|
|
td
|
2020
|
|
td
|
Wuhan, China
|
|
td
|
48
|
|
td
|
21
|
|
td
|
27
|
|
td
|
64.6 ± 18.1
|
|
tr
|
Wang R [75] Int. J. Infect. Dis. 2020 Anhui, China 125 100 25 38.8 ± 13.8
|
|
td
|
Wang R [75]
|
|
td
|
Int. J. Infect. Dis.
|
|
td
|
2020
|
|
td
|
Anhui, China
|
|
td
|
125
|
|
td
|
100
|
|
td
|
25
|
|
td
|
38.8 ± 13.8
|
|
tr
|
Gao Y [68] J. Med. Virol. 2020 Anhui, China 43 28 15
|
|
td
|
Gao Y [68]
|
|
td
|
J. Med. Virol.
|
|
td
|
2020
|
|
td
|
Anhui, China
|
|
td
|
43
|
|
td
|
28
|
|
td
|
15
|
|
tr
|
Zheng YL [17] J. Clin. Virol. 2020 Chengdu, China 99 67 32 49.4 ± 18.45
|
|
td
|
Zheng YL [17]
|
|
td
|
J. Clin. Virol.
|
|
td
|
2020
|
|
td
|
Chengdu, China
|
|
td
|
99
|
|
td
|
67
|
|
td
|
32
|
|
td
|
49.4 ± 18.45
|
|
tr
|
Ma J [15] J. Infect. 2020 Wuhan, China 37 17 20 62 (59–70)
|
|
td
|
Ma J [15]
|
|
td
|
J. Infect.
|
|
td
|
2020
|
|
td
|
Wuhan, China
|
|
td
|
37
|
|
td
|
17
|
|
td
|
20
|
|
td
|
62 (59–70)
|
|
tr
|
Wang DL [18] Lancet 2020 Jiangsu, China 620 567 53 44.4 ± 17.2
|
|
td
|
Wang DL [18]
|
|
td
|
Lancet
|
|
td
|
2020
|
|
td
|
Jiangsu, China
|
|
td
|
620
|
|
td
|
567
|
|
td
|
53
|
|
td
|
44.4 ± 17.2
|
|
tr
|
Liu W [56] Chin. Med. J. 2020 Wuhan, China 78 67 11 38 (33–57)
|
|
td
|
Liu W [56]
|
|
td
|
Chin. Med. J.
|
|
td
|
2020
|
|
td
|
Wuhan, China
|
|
td
|
78
|
|
td
|
67
|
|
td
|
11
|
|
td
|
38 (33–57)
|
|
tr
|
Yang AP [76] Int. Immunopharmacol. 2020 China 93 69 24 46.4 ± 17.6
|
|
td
|
Yang AP [76]
|
|
td
|
Int. Immunopharmacol.
|
|
td
|
2020
|
|
td
|
China
|
|
td
|
93
|
|
td
|
69
|
|
td
|
24
|
|
td
|
46.4 ± 17.6
|
|
tr
|
Li KH [77] Invest Radiol 2020 Chongqing, China 83 58 25 45.5 ± 12.3
|
|
td
|
Li KH [77]
|
|
td
|
Invest Radiol
|
|
td
|
2020
|
|
td
|
Chongqing, China
|
|
td
|
83
|
|
td
|
58
|
|
td
|
25
|
|
td
|
45.5 ± 12.3
|
|
tr
|
Zhang JJ [62] Allergy 2020 Wuhan, China 140 82 58 57 (25–87)
|
|
td
|
Zhang JJ [62]
|
|
td
|
Allergy
|
|
td
|
2020
|
|
td
|
Wuhan, China
|
|
td
|
140
|
|
td
|
82
|
|
td
|
58
|
|
td
|
57 (25–87)
|
|
tr
|
Huang CL [10] Lancet 2020 Wuhan, China 41 28 13 49 (41–58)
|
|
td
|
Huang CL [10]
|
|
td
|
Lancet
|
|
td
|
2020
|
|
td
|
Wuhan, China
|
|
td
|
41
|
|
td
|
28
|
|
td
|
13
|
|
td
|
49 (41–58)
|
|
tr
|
Wang DW [64] JAMA 2020 Wuhan, China 138 102 36 56 (42–68)
|
|
td
|
Wang DW [64]
|
|
td
|
JAMA
|
|
td
|
2020
|
|
td
|
Wuhan, China
|
|
td
|
138
|
|
td
|
102
|
|
td
|
36
|
|
td
|
56 (42–68)
|
|
tr
|
Liu M [14] Zhonghua Jie He He Hu Xi Za Zhi 2020 Wuhan, China 30 26 4 35 ± 8
|
|
td
|
Liu M [14]
|
|
td
|
Zhonghua Jie He He Hu Xi Za Zhi
|
|
td
|
2020
|
|
td
|
Wuhan, China
|
|
td
|
30
|
|
td
|
26
|
|
td
|
4
|
|
td
|
35 ± 8
|
|
tr
|
Mo PZ [78] Clin. Infect. Dis. 2020 Wuhan, China 155 70 85 54 (42–66)
|
|
td
|
Mo PZ [78]
|
|
td
|
Clin. Infect. Dis.
|
|
td
|
2020
|
|
td
|
Wuhan, China
|
|
td
|
155
|
|
td
|
70
|
|
td
|
85
|
|
td
|
54 (42–66)
|
|
tr
|
Peng YD [79] Zhonghua Xin Xue Guan Bing Za Zhi 2020 Shanghai, China 112 96 16 62 (55–67)
|
|
td
|
Peng YD [79]
|
|
td
|
Zhonghua Xin Xue Guan Bing Za Zhi
|
|
td
|
2020
|
|
td
|
Shanghai, China
|
|
td
|
112
|
|
td
|
96
|
|
td
|
16
|
|
td
|
62 (55–67)
|
|
tr
|
Feng Y [80] Am. J. Respir. Crit. Care Med. 2020 China 476 352 124 53 (40–64)
|
|
td
|
Feng Y [80]
|
|
td
|
Am. J. Respir. Crit. Care Med.
|
|
td
|
2020
|
|
td
|
China
|
|
td
|
476
|
|
td
|
352
|
|
td
|
124
|
|
td
|
53 (40–64)
|
|
tr
|
Cai QX [81] Allergy 2020 Shenzhen, China 298 240 58 47.5 (33–61)
|
|
td
|
Cai QX [81]
|
|
td
|
Allergy
|
|
td
|
2020
|
|
td
|
Shenzhen, China
|
|
td
|
298
|
|
td
|
240
|
|
td
|
58
|
|
td
|
47.5 (33–61)
|
|
tr
|
Li H [82] J. Infect. 2020 Wuhan, China 132 60 72 62 ± 12.7
|
|
td
|
Li H [82]
|
|
td
|
J. Infect.
|
|
td
|
2020
|
|
td
|
Wuhan, China
|
|
td
|
132
|
|
td
|
60
|
|
td
|
72
|
|
td
|
62 ± 12.7
|
|
tr
|
Zheng F [83] Eur Rev Med Pharmacol Sci 2020 Hunan, China 161 131 30 45 (33.5–57)
|
|
td
|
Zheng F [83]
|
|
td
|
Eur Rev Med Pharmacol Sci
|
|
td
|
2020
|
|
td
|
Hunan, China
|
|
td
|
161
|
|
td
|
131
|
|
td
|
30
|
|
td
|
45 (33.5–57)
|
|
tr
|
Wang KK [84] Lancet 2020 Hong Kong, China 23 13 10
|
|
td
|
Wang KK [84]
|
|
td
|
Lancet
|
|
td
|
2020
|
|
td
|
Hong Kong, China
|
|
td
|
23
|
|
td
|
13
|
|
td
|
10
|
|
tr
|
Guan W [85] New Engl J Med 2020 Wuhan, China 1099 926 173 47 (35–58)
|
|
td
|
Guan W [85]
|
|
td
|
New Engl J Med
|
|
td
|
2020
|
|
td
|
Wuhan, China
|
|
td
|
1099
|
|
td
|
926
|
|
td
|
173
|
|
td
|
47 (35–58)
|
|
tr
|
Gong J [86] Clin. Infect. Dis. 2020 Wuhan and Guangdong, China 189 161 28 49 (35–63)
|
|
td
|
Gong J [86]
|
|
td
|
Clin. Infect. Dis.
|
|
td
|
2020
|
|
td
|
Wuhan and Guangdong, China
|
|
td
|
189
|
|
td
|
161
|
|
td
|
28
|
|
td
|
49 (35–63)
|
|
tr
|
Lei SQ [87] E Clin Med 2020 China 34 19 15 55 (43–63)
|
|
td
|
Lei SQ [87]
|
|
td
|
E Clin Med
|
|
td
|
2020
|
|
td
|
China
|
|
td
|
34
|
|
td
|
19
|
|
td
|
15
|
|
td
|
55 (43–63)
|
|
tr
|
Deng Q [88] Int. J. Cardiol. 2020 Wuhan, China 112 45 67 65 (49–70)
|
|
td
|
Deng Q [88]
|
|
td
|
Int. J. Cardiol.
|
|
td
|
2020
|
|
td
|
Wuhan, China
|
|
td
|
112
|
|
td
|
45
|
|
td
|
67
|
|
td
|
65 (49–70)
|
|
tr
|
Mao L [89] JAMA Neurol. 2020 Wuhan, China 214 126 88 52.7 ± 15.5
|
|
td
|
Mao L [89]
|
|
td
|
JAMA Neurol.
|
|
td
|
2020
|
|
td
|
Wuhan, China
|
|
td
|
214
|
|
td
|
126
|
|
td
|
88
|
|
td
|
52.7 ± 15.5
|
|
tr
|
Du RH [90] Ann. Am. Thorac. Soc. 2020 Wuhan, China 109 58 51 70.7 ± 10.9
|
|
td
|
Du RH [90]
|
|
td
|
Ann. Am. Thorac. Soc.
|
|
td
|
2020
|
|
td
|
Wuhan, China
|
|
td
|
109
|
|
td
|
58
|
|
td
|
51
|
|
td
|
70.7 ± 10.9
|
|
tr
|
Xie HS [91] Liver Int. 2020 Wuhan, China 79 51 28 60 (48–66)
|
|
td
|
Xie HS [91]
|
|
td
|
Liver Int.
|
|
td
|
2020
|
|
td
|
Wuhan, China
|
|
td
|
79
|
|
td
|
51
|
|
td
|
28
|
|
td
|
60 (48–66)
|
|
tr
|
Wu J [92] J. Intern. Med. 2020 China 280 197 83 43 ± 19
|
|
td
|
Wu J [92]
|
|
td
|
J. Intern. Med.
|
|
td
|
2020
|
|
td
|
China
|
|
td
|
280
|
|
td
|
197
|
|
td
|
83
|
|
td
|
43 ± 19
|
|
tr
|
Chen G [93] J. Clin. Investig. 2020 Wuhan, China 21 10 11 56 (50–65)
|
|
td
|
Chen G [93]
|
|
td
|
J. Clin. Investig.
|
|
td
|
2020
|
|
td
|
Wuhan, China
|
|
td
|
21
|
|
td
|
10
|
|
td
|
11
|
|
td
|
56 (50–65)
|
|
tr
|
Wan S [94] J. Med. Virol. 2020 Chongqing, China 135 95 40 47 (36–55)
|
|
td
|
Wan S [94]
|
|
td
|
J. Med. Virol.
|
|
td
|
2020
|
|
td
|
Chongqing, China
|
|
td
|
135
|
|
td
|
95
|
|
td
|
40
|
|
td
|
47 (36–55)
|
|
tr
|
Pan HQ [42] Lancet Infect. Dis. 2020 Wuhan, China 221 166 55 55 (39–66.5)
|
|
td
|
Pan HQ [42]
|
|
td
|
Lancet Infect. Dis.
|
|
td
|
2020
|
|
td
|
Wuhan, China
|
|
td
|
221
|
|
td
|
166
|
|
td
|
55
|
|
td
|
55 (39–66.5)
|
|
tr
|
Bo XU [16] J. Infect. 2020 Wuhan, China 187 80 107 62 (48.5–71)
|
|
td
|
Bo XU [16]
|
|
td
|
J. Infect.
|
|
td
|
2020
|
|
td
|
Wuhan, China
|
|
td
|
187
|
|
td
|
80
|
|
td
|
107
|
|
td
|
62 (48.5–71)
|
|
tr
|
GQQ [95] Int. J. Med. 2020 Zhejiang, China 91 82 9 50 (36.5–57)
|
|
td
|
GQQ [95]
|
|
td
|
Int. J. Med.
|
|
td
|
2020
|
|
td
|
Zhejiang, China
|
|
td
|
91
|
|
td
|
82
|
|
td
|
9
|
|
td
|
50 (36.5–57)
|
|
tr
|
Lo LI [96] Int. J. Biol. Sci. 2020 Macau, China 10 6 4 54 (27–64)
|
|
td
|
Lo LI [96]
|
|
td
|
Int. J. Biol. Sci.
|
|
td
|
2020
|
|
td
|
Macau, China
|
|
td
|
10
|
|
td
|
6
|
|
td
|
4
|
|
td
|
54 (27–64)
|
|
p
|
Among these articles, 34 articles described the results of blood routine and infection-related biomarkers, 26 articles described the results of biochemical tests, 23 articles provided test results for blood coagulation and 5 articles described the results of immunoassay. A total of 5,912 patients (mean age:54.80; 95%CI (52.50–57.20)), including 4337 non-severe patients (mean age:48.50; 95%CI (45.70–51.30)) and 1663 severe patients (mean age:61.00; 95%CI (59.10–62.90)). More than half of them were male (3072/5912(51.96%)). 2531 of these patients with underlying disease, including hypertension, diabetes, cardiovascular disease, cerebrovascular disease, chronic obstructive pulmonary disease (COPD), chronic kidney disease and liver disease, Malignant tumors and patients with low immunity.
|
|
sec
|
3.2 Analysis of laboratory indicators
Based on the comprehensive collation of the laboratory data provided in the selected 35 articles, the average value and variation range of various indexes of total patients, non-severe patients and severe patients were obtained and shown in Table 3 . In addition, the Funnel plot of important laboratory indicators are shown in Fig. 2, Fig. 3, Fig. 4, Fig. 5, Fig. 6, Fig. 7, Fig. 8, Fig. 9, Fig. 10 .
Table 2 The quality assessment of included studies.
Author 1 2 3 4 5 6 7 8 Score
Qin C [13] 2 2 2 2 2 0 2 2 14
Chen X [74] 2 2 2 2 2 0 1 1 12
Wang R [75] 2. 2 2 2 2 2 2 2 16
Gao Y [68] 2 2 2 2 2 0 2 1 13
Zheng YL [17] 2 2 2 2 2 2 2 1 15
Ma J [15] 2 2 2 2 2 0 2 1 13
Wang DL [18] 2 2 2 2 2 2 2 2 16
Liu W [56] 2 2 2 2 2 0 2 1 13
Yang AP [76] 2 2 2 2 2 1 1 1 13
Li KH [77] 2 2 2 2 2 0 2 1 13
Zhang JJ [62] 2 2 2 2 2 0 2 2 14
Huang CL [10] 2 2 2 2 2 0 2 1 13
Wang DW [64] 2 2 2 2 2 1 2 2 15
Liu M [14] 2 2 2 2 2 0 2 1 13
Mo PZ [78] 2 2 2 2 2 1 2 2 15
Peng YD [79] 2 2 2 2 2 2 2 1 15
Feng Y [80] 2 2 2 2 2 0 2 2 14
Cai QX [81] 2 2 2 2 2 2 2 2 16
Li H [82] 2 2 2 2 2 2 1 2 15
Zheng F [83] 2 2 2 2 2 0 2 2 14
Wang KK [84] 2 2 2 2 2 2 2 1 15
Guan W [85] 2 2 2 2 2 2 2 2 16
Gong J [86] 2 2 2 2 2 2 2 2 16
Lei SQ [87] 2 2 2 2 2 2 2 1 15
Deng Q [88] 2 2 2 2 2 2 1 2 15
Mao L [89] 2 2 2 2 2 0 1 2 13
Du RH [90] 2 2 2 2 2 2 2 2 16
Xie HS [91] 2 2 2 2 2 0 2 1 13
Wu J [92] 2 2 2 2 2 0 2 2 14
Chen G [93] 2 2 2 2 2 0 1 1 12
Wan S [94] 2 2 2 2 2 1 2 2 15
Pan HQ [42] 2 2 2 2 2 1 2 2 15
Bo XU [16] 2 2 2 2 2 2 2 2 16
GQQ [95] 2 2 2 2 2 0 2 1 13
Lo LI [96] 2 2 2 2 2 2 1 1 14
1. A clear purpose of the study; 2. Including continuous patients; 3. Expected collection of data; 4. The end point adapted to the research goal; 5. A fair assessment of the end point of the study; 6. A follow-up period commensurate with the objectives of the study; 7. Comprehensive laboratory indicators; 8. Sufficient numbers of patients.
Table3 Results of Laboratory findings of severe and non-severe patients infected with COVID-19.
Variables Classification Number of articles included Number of patients included Mean (95% CI) P-value
Blood routine WBC×109 /l Non-severe patients 34 4236 5.07 (4.90, 5.24) <0.01
Severe patients 1703 6.06 (5.67, 6.46)
All patients 5939 5.39 (5.22, 5.55)
Neutrophils×109 /l Non-severe patients 29 3005 3.71 (3.36, 4.06) <0.01
Severe patients 1368 4.94 (4.30, 5.58)
All patients 4373 4.22 (3.94, 4.51)
Lymphocytes×109 /l Non-severe patients 34 4228 1.15 (1.08, 1.22) <0.01
Severe patients 1703 0.80 (0.75, 0.84)
All patients 5931 0.98 (0.92, 1.04)
Eosinophil×109 /l Non-severe patients 5 1035 0.04 (0.03, 0.05) 0.003
Severe patients 451 0.02 (0.01, 0.02)
All patients 1486 0.03 (0.02, 0.04)
Monocyte×109 /l Non-severe patients 13 1397 0.41 (0.40, 0.42) 0.086
Severe patients 700 0.38 (0.35, 0.41)
All patients 2097 0.40 (0.39, 0.42)
PLT×109 /l Non-severe patients 20 3068 184.19 (178.04, 190.33) 0.340
Severe patients 1019 212.58 (154.63, 270.53)
All patients 4087 201.44 (175.75, 227.12)
Hbg/l Non-severe patients 14 2652 131.36 (128.33, 134.39) 0.164
Severe patients 711 126.73 (121.16, 132.30)
All patients 3363 128.98 (126.17, 131.78)
Inflammation-related factors ESR mm/60 min Non-severe patients 8 1390 28.16 (20.13, 36.2) 0.111
Severe patients 695 40.54 (27.61, 53.4)
All patients 2085 33.94 (28.10, 39.7)
CRP mg/l Non-severe patients 26 2939 19.83 (16.67, 23.0) <0.01
Severe patients 1415 60.91 (49.24, 72.5)
All patients 4354 36.99 (33.31, 40.67)
PCT ng/ml Non-severe patients 22 2325 0.07 (0.05, 0.09) <0.01
Severe patients 1222 0.14 (0.11, 0.17)
All patients 3547 0.10 (0.08, 0.12)
Blood biochemistry ALT U/l Non-severe patients 26 2769 24.85 (22.69, 27.0) <0.01
Severe patients 1027 33.78 (29.54, 38.0)
All patients 3796 28.31 (26.25, 30.3)
AST U/l Non-severe patients 26 2731 26.24 (25.29, 28.18) <0.01
Severe patients 1019 36.78 (33.69, 39.87)
All patients 3750 30.66 (29.19, 32.13)
LDH U/l Non-severe patients 19 2306 224.20 (205.33, 243.07) <0.01
Severe patients 811 344.48 (307.08, 381.88)
All patients 3117 271.82 (254.13, 289.52)
CK U/l Non-severe patients 17 2241 77.69 (69.68, 85.70) <0.01
Severe patients 849 111.92 (98.24, 125.61)
All patients 3090 90.92 (83.54, 98.29)
CK-MB U/l Non-severe patients 9 1329 8.76 (4.74, 12.79) 0.246
Severe patients 550 12.26 (7.93, 16.58)
All patients 1879 10.51 (8.33, 12.70)
Albumin g/l Non-severe patients 10 985 39.41 (37.95, 40.87) <0.01
Severe patients 431 34.29 (32.79, 35.80)
All patients 1416 36.75 (35.17, 38.32)
Creatinine μmol/l Non-severe patients 25 2696 66.97 (64.65, 69.28) <0.01
Severe patients 1000 72.94 (69.23, 76.66)
All patients 3696 69.61 (67.57, 71.65)
Urea mmol/l Non-severe patients 16 1929 4.36 (4.12, 4.59) <0.01
Severe patients 703 5.59 (5.39, 6.51)
All patients 2632 4.98 (4.72, 5.23)
Total bilirubin mmol/l Non-severe patients 14 2065 10.38 (9.78, 10.99) 0.017
Severe patients 564 11.86 (10.81, 12.91)
All patients 2629 10.92 (10.39, 11.45)
Blood coagulation function APTTs Non-severe patients 12 1422 33.49 (31.17, 35.82) 0.724
Severe patients 407 32.92 (30.78, 35.06)
All patients 1829 33.23 (31.59, 34.86)
PT s Non-severe patients 12 1388 12.45 (11.98, 12.91) 0.319
Severe patients 504 12.80 (12.29, 13.30)
All patients 1892 12.63 (12.31, 12.94)
D dimer mg/l Non-severe patients 23 2503 0.47 (0.40, 0.53) <0.01
Severe patients 1043 1.29 (0.03, 0.54)
All patients 3546 0.61 (0.54, 0.67)
Lymphocyte subsets CD4 T cells /μL Non-severe patients 5 475 561.81 (485.46, 638.15) <0.01
Severe patients 208 266.79 (204.51, 329.07)
All patients 683 407.03 (310.24, 503.83)
CD8 T cells /μL Non-severe patients 5 475 349.01 (292.61, 405.40) <0.01
Severe patients 208 174.61 (125.95, 223.27)
All patients 683 266.65 (198.49, 334.81)
Cytokines IL-1β pg/ml Non-severe patients 2 246 5.01 (4.96, 5.06) 0.055
Severe patients 393 5.11 (5.02, 5.20)
All patients 639 5.06 (4.98, 5.15)
IL-6 pg/ml Non-severe patients 5 312 13.22 (6.88, 19.57) 0.027
Severe patients 455 25.58 (16.69, 34.47)
All patients 767 19.66 (13.44, 25.89)
IL-10 pg/ml Non-severe patients 2 246 5.71 (5.51, 5.90) <0.01
Severe patients 393 8.87 (7.15, 10.59)
All patients 639 7.23 (6.18, 8.28)
SPSS25 was used. Comparison between severe and non-severe patients with t test or Mann-Whitney U test.
Fig. 2 Meta-analysis of lymphocytes.
Fig. 3 Meta-analysis of CRP.
Fig. 4 Meta-analysis of PCT.
Fig. 5 Meta-analysis of ALT.
Fig. 6 Meta-analysis of AST.
Fig. 7 Meta-analysis of LDH.
Fig. 8 Meta-analysis of D-dimer.
Fig. 9 Meta-analysis of CD4 T cells.
Fig. 10 Meta-analysis of IL-6.
3.2.1 Blood routine examination
Leukopenia was observed in 21.92% (363/1656) patients with lymphocytopenia in 29.02% (886/3053) patients. Elevated neutrophils were observed in 19.85% (81/408) patients. 14.73% (75/509) and 12.68% (78/615) patients were accompanied by a decrease in Hemoglobin and platelet count (PLT) respectively. Most importantly, there were several significant differences between severe patients and non-severe patients, including higher leukocyte (1.20-fold; 6.06 vs 5.07 × 109/l; P < 0.01) and neutrophil (1.33-fold; 4.94 vs 3.71 × 109/l; P < 0.01), lower lymphocyte (1.44-fold; 1.15 vs 0.80 × 109/l; P < 0.01), eosinophils (2.00-fold; 0.04 vs 0.02 × 109/l; P = 0.03), monocytes (1.08-fold; 0.38 vs 0.41 × 109/l; P = 0.041), PLT (1.15-fold; 212.58 vs 184.19 × 109/l; P = 0.987) and hemoglobin (1.53-fold; 131.36vs 126.73 × 109 g/l; P = 0.163).
3.2.2 Inflammatory biomarkers examination
Increased C-reactive protein (CRP) concentration appeared in 57.40% (1494/2603) patients, procalcitonin (PCT) increased in 12.20% (256/2099) patients, and 39.26% (117/298) patients had an increase in erythrocyte sedimentation rate (ESR). Moreover, higher levels of CRP (3.04-fold; 60.91 vs 19.83 mg/l; P < 0.01), PCT (2.00-fold; 0.14vs 0.07 ng/ml; P < 0.01) and ESR (1.44-fold; 40.54 vs 28.16 mm/60 min; P = 0.096) were observed in severe patients in comparison with non-severe patients.
3.2.3 Blood biochemical examination
3.2.3.1 Cardiac markers examination
Our statistics showed that the related indexes of myocardial injury increased in different numbers of patients with COVID-19. (respectively creatine kinase (CK) (7.74% (157/2029)); aspartate aminotransferase (AST) (14.87% (388/2609)); lactate dehydrogenase (LDH) (24.50% (468/1910)). Several significant differences were noted between severe and non-severe patients, especially higher values of AST (1.40-fold; 36.78 vs 26.24 U/l; P < 0.01), LDH (1.54-fold; 344.48 vs 224.20 U/l; P < 0.01), CK (1.44-fold; 111.92 vs 77.69 U/l; P < 0.01) and CK-MB (1.39-fold; 12.26 vs 8.76 U/l; P = 0.317).
3.2.3.2 Liver function
The increase of alanine aminotransferase (ALT) (12.27% (296/2412)) and AST (14.87% (388/2609)) with COVID-19 has been observed. Moreover, the decrease of albumin (143/221 (64.70%)) was more common while the increase of total bilirubin (TBIL) was relatively rare in the majority of patients (109/1558 (6.70%)). Comparing with non-severe patients, higher ALT (1.34-fold; 33.78 vs 24.85 U/l; P < 0.01), AST (1.40-fold; 36.78 vs 26.24 U/l; P < 0.01), TBIL (1.14-fold; 11.86 vs 10.38 U/l; P = 0.024) and lower albumin (1.15-fold; 39.41vs 34.29 g/l; P < 0.01) of severe patients has been worked out.
3.2.3.3 Renal function
The increase of creatinine (2.41% (40/1659)) and urea (13.50% (47/348)) were observed among the included patients with COVID-19. Besides, albumin reduction (64.70% (143/221)) was very common. More importantly, higher levels of creatinine (1.09-fold;72.94 vs 66.97 μmol; P < 0.01), urea (1.28-fold; 5.59 vs 4.36 mmol; P < 0.01) and lower concentrations of albumin (1.15-fold; 39.41 vs 34.29 g/l; P < 0.01) of severe patients were summed up in comparison with non-severe patients.
3.2.4 Blood coagulation function
Prothrombin time (PT) prolonged in 22.65% (53/234) patients and shortened in 10.68% (25/234) patients while activated partial thromboplastin time (APTT) prolonged in 21.79% (51/234) patients and shortened in 5.56% (13/234) patients. D dimer increased in 28.94% (534/1845) patients. Abnormal coagulation function is more obvious in severe patients, including shorter APTT (1.02-fold;33.49 vs 32.92 s; P = 0.804), increased D-dimer (2.74-fold; 1.29 vs 0.47 mg/l; P < 0.01) and longer PT (1.03-fold; 12.80 vs 12.45 s; P = 0.407).
3.2.5 Immunological examination
3.2.5.1 Antibody detection
The values of antibodies and complements in blood serum in Qin's [13] study showed that immunoglobulins (IgA, IgG and IgM) and complement proteins (C3 and C4) of COVID-19 patients are within the normal range. Compared with the non-severe group, the IgM of the severe group was only slightly decreased, and there was no significant difference in other immunoglobulins and complement, which was consistent with the results of Feng's study [14].
3.2.5.2 Lymphocyte subsets
The total number of B cells, T cells and NK cells significantly decreased in patients with COVID-19 (852.9 /uL), and more evident in the severe cases (1.37-fold; 743.6 vs 1020.1 /uL; P = 0.032) compared to the non-severe group [13]. Lower levels of CD4 T cells (2.10-fold; 561.81 vs 266.79 cell/μl; P < 0.01), CD8 T cells (2.00-fold; 349.01 vs 174.61cell/μl; P < 0.01) were summarized in severe patients comparing with non-severe patients from 5 articles [14], [15], [16], [17], [18]. In addition, lower CD3 T cells (1.70-fold; 1070.23 vs 628.20 cell/μl; P < 0.01) in severe patients was noted in Liu et al. [18].
3.2.5.3 Cytokine
Series of inflammatory cytokines were also increaseted in severe cases than the non-severe ones, including interleukin (IL)-1β (1.02-fold; 5.11 vs 5.01 pg/ml; P = 0.098), IL-6 (1.93-fold; 25.58 vs 13.22 pg/ml; P = 0.043), IL-10 (1.55-fold; 8.87 vs 5.71 pg/ml; P < 0.01). In addition, Qin et al. found higher levels of IL-2R (1.14-fold; 757.0 vs 663.5 U/ml; P < 0.01), IL-8 (1.34-fold; 18.4 vs 13.7 pg/ml; P < 0.01) and TNF-α (1.04-fold; 8.7 vs 8.4 pg/ml; P = 0.037) in severe patients in comparison with non-severe patients [13]. Studies also reported that GSCF, IP-10, MCP1and MIP1A in severe patients were higher [10].
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Analysis of laboratory indicators
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Based on the comprehensive collation of the laboratory data provided in the selected 35 articles, the average value and variation range of various indexes of total patients, non-severe patients and severe patients were obtained and shown in Table 3 . In addition, the Funnel plot of important laboratory indicators are shown in Fig. 2, Fig. 3, Fig. 4, Fig. 5, Fig. 6, Fig. 7, Fig. 8, Fig. 9, Fig. 10 .
Table 2 The quality assessment of included studies.
Author 1 2 3 4 5 6 7 8 Score
Qin C [13] 2 2 2 2 2 0 2 2 14
Chen X [74] 2 2 2 2 2 0 1 1 12
Wang R [75] 2. 2 2 2 2 2 2 2 16
Gao Y [68] 2 2 2 2 2 0 2 1 13
Zheng YL [17] 2 2 2 2 2 2 2 1 15
Ma J [15] 2 2 2 2 2 0 2 1 13
Wang DL [18] 2 2 2 2 2 2 2 2 16
Liu W [56] 2 2 2 2 2 0 2 1 13
Yang AP [76] 2 2 2 2 2 1 1 1 13
Li KH [77] 2 2 2 2 2 0 2 1 13
Zhang JJ [62] 2 2 2 2 2 0 2 2 14
Huang CL [10] 2 2 2 2 2 0 2 1 13
Wang DW [64] 2 2 2 2 2 1 2 2 15
Liu M [14] 2 2 2 2 2 0 2 1 13
Mo PZ [78] 2 2 2 2 2 1 2 2 15
Peng YD [79] 2 2 2 2 2 2 2 1 15
Feng Y [80] 2 2 2 2 2 0 2 2 14
Cai QX [81] 2 2 2 2 2 2 2 2 16
Li H [82] 2 2 2 2 2 2 1 2 15
Zheng F [83] 2 2 2 2 2 0 2 2 14
Wang KK [84] 2 2 2 2 2 2 2 1 15
Guan W [85] 2 2 2 2 2 2 2 2 16
Gong J [86] 2 2 2 2 2 2 2 2 16
Lei SQ [87] 2 2 2 2 2 2 2 1 15
Deng Q [88] 2 2 2 2 2 2 1 2 15
Mao L [89] 2 2 2 2 2 0 1 2 13
Du RH [90] 2 2 2 2 2 2 2 2 16
Xie HS [91] 2 2 2 2 2 0 2 1 13
Wu J [92] 2 2 2 2 2 0 2 2 14
Chen G [93] 2 2 2 2 2 0 1 1 12
Wan S [94] 2 2 2 2 2 1 2 2 15
Pan HQ [42] 2 2 2 2 2 1 2 2 15
Bo XU [16] 2 2 2 2 2 2 2 2 16
GQQ [95] 2 2 2 2 2 0 2 1 13
Lo LI [96] 2 2 2 2 2 2 1 1 14
1. A clear purpose of the study; 2. Including continuous patients; 3. Expected collection of data; 4. The end point adapted to the research goal; 5. A fair assessment of the end point of the study; 6. A follow-up period commensurate with the objectives of the study; 7. Comprehensive laboratory indicators; 8. Sufficient numbers of patients.
Table3 Results of Laboratory findings of severe and non-severe patients infected with COVID-19.
Variables Classification Number of articles included Number of patients included Mean (95% CI) P-value
Blood routine WBC×109 /l Non-severe patients 34 4236 5.07 (4.90, 5.24) <0.01
Severe patients 1703 6.06 (5.67, 6.46)
All patients 5939 5.39 (5.22, 5.55)
Neutrophils×109 /l Non-severe patients 29 3005 3.71 (3.36, 4.06) <0.01
Severe patients 1368 4.94 (4.30, 5.58)
All patients 4373 4.22 (3.94, 4.51)
Lymphocytes×109 /l Non-severe patients 34 4228 1.15 (1.08, 1.22) <0.01
Severe patients 1703 0.80 (0.75, 0.84)
All patients 5931 0.98 (0.92, 1.04)
Eosinophil×109 /l Non-severe patients 5 1035 0.04 (0.03, 0.05) 0.003
Severe patients 451 0.02 (0.01, 0.02)
All patients 1486 0.03 (0.02, 0.04)
Monocyte×109 /l Non-severe patients 13 1397 0.41 (0.40, 0.42) 0.086
Severe patients 700 0.38 (0.35, 0.41)
All patients 2097 0.40 (0.39, 0.42)
PLT×109 /l Non-severe patients 20 3068 184.19 (178.04, 190.33) 0.340
Severe patients 1019 212.58 (154.63, 270.53)
All patients 4087 201.44 (175.75, 227.12)
Hbg/l Non-severe patients 14 2652 131.36 (128.33, 134.39) 0.164
Severe patients 711 126.73 (121.16, 132.30)
All patients 3363 128.98 (126.17, 131.78)
Inflammation-related factors ESR mm/60 min Non-severe patients 8 1390 28.16 (20.13, 36.2) 0.111
Severe patients 695 40.54 (27.61, 53.4)
All patients 2085 33.94 (28.10, 39.7)
CRP mg/l Non-severe patients 26 2939 19.83 (16.67, 23.0) <0.01
Severe patients 1415 60.91 (49.24, 72.5)
All patients 4354 36.99 (33.31, 40.67)
PCT ng/ml Non-severe patients 22 2325 0.07 (0.05, 0.09) <0.01
Severe patients 1222 0.14 (0.11, 0.17)
All patients 3547 0.10 (0.08, 0.12)
Blood biochemistry ALT U/l Non-severe patients 26 2769 24.85 (22.69, 27.0) <0.01
Severe patients 1027 33.78 (29.54, 38.0)
All patients 3796 28.31 (26.25, 30.3)
AST U/l Non-severe patients 26 2731 26.24 (25.29, 28.18) <0.01
Severe patients 1019 36.78 (33.69, 39.87)
All patients 3750 30.66 (29.19, 32.13)
LDH U/l Non-severe patients 19 2306 224.20 (205.33, 243.07) <0.01
Severe patients 811 344.48 (307.08, 381.88)
All patients 3117 271.82 (254.13, 289.52)
CK U/l Non-severe patients 17 2241 77.69 (69.68, 85.70) <0.01
Severe patients 849 111.92 (98.24, 125.61)
All patients 3090 90.92 (83.54, 98.29)
CK-MB U/l Non-severe patients 9 1329 8.76 (4.74, 12.79) 0.246
Severe patients 550 12.26 (7.93, 16.58)
All patients 1879 10.51 (8.33, 12.70)
Albumin g/l Non-severe patients 10 985 39.41 (37.95, 40.87) <0.01
Severe patients 431 34.29 (32.79, 35.80)
All patients 1416 36.75 (35.17, 38.32)
Creatinine μmol/l Non-severe patients 25 2696 66.97 (64.65, 69.28) <0.01
Severe patients 1000 72.94 (69.23, 76.66)
All patients 3696 69.61 (67.57, 71.65)
Urea mmol/l Non-severe patients 16 1929 4.36 (4.12, 4.59) <0.01
Severe patients 703 5.59 (5.39, 6.51)
All patients 2632 4.98 (4.72, 5.23)
Total bilirubin mmol/l Non-severe patients 14 2065 10.38 (9.78, 10.99) 0.017
Severe patients 564 11.86 (10.81, 12.91)
All patients 2629 10.92 (10.39, 11.45)
Blood coagulation function APTTs Non-severe patients 12 1422 33.49 (31.17, 35.82) 0.724
Severe patients 407 32.92 (30.78, 35.06)
All patients 1829 33.23 (31.59, 34.86)
PT s Non-severe patients 12 1388 12.45 (11.98, 12.91) 0.319
Severe patients 504 12.80 (12.29, 13.30)
All patients 1892 12.63 (12.31, 12.94)
D dimer mg/l Non-severe patients 23 2503 0.47 (0.40, 0.53) <0.01
Severe patients 1043 1.29 (0.03, 0.54)
All patients 3546 0.61 (0.54, 0.67)
Lymphocyte subsets CD4 T cells /μL Non-severe patients 5 475 561.81 (485.46, 638.15) <0.01
Severe patients 208 266.79 (204.51, 329.07)
All patients 683 407.03 (310.24, 503.83)
CD8 T cells /μL Non-severe patients 5 475 349.01 (292.61, 405.40) <0.01
Severe patients 208 174.61 (125.95, 223.27)
All patients 683 266.65 (198.49, 334.81)
Cytokines IL-1β pg/ml Non-severe patients 2 246 5.01 (4.96, 5.06) 0.055
Severe patients 393 5.11 (5.02, 5.20)
All patients 639 5.06 (4.98, 5.15)
IL-6 pg/ml Non-severe patients 5 312 13.22 (6.88, 19.57) 0.027
Severe patients 455 25.58 (16.69, 34.47)
All patients 767 19.66 (13.44, 25.89)
IL-10 pg/ml Non-severe patients 2 246 5.71 (5.51, 5.90) <0.01
Severe patients 393 8.87 (7.15, 10.59)
All patients 639 7.23 (6.18, 8.28)
SPSS25 was used. Comparison between severe and non-severe patients with t test or Mann-Whitney U test.
Fig. 2 Meta-analysis of lymphocytes.
Fig. 3 Meta-analysis of CRP.
Fig. 4 Meta-analysis of PCT.
Fig. 5 Meta-analysis of ALT.
Fig. 6 Meta-analysis of AST.
Fig. 7 Meta-analysis of LDH.
Fig. 8 Meta-analysis of D-dimer.
Fig. 9 Meta-analysis of CD4 T cells.
Fig. 10 Meta-analysis of IL-6.
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Table 2 The quality assessment of included studies.
Author 1 2 3 4 5 6 7 8 Score
Qin C [13] 2 2 2 2 2 0 2 2 14
Chen X [74] 2 2 2 2 2 0 1 1 12
Wang R [75] 2. 2 2 2 2 2 2 2 16
Gao Y [68] 2 2 2 2 2 0 2 1 13
Zheng YL [17] 2 2 2 2 2 2 2 1 15
Ma J [15] 2 2 2 2 2 0 2 1 13
Wang DL [18] 2 2 2 2 2 2 2 2 16
Liu W [56] 2 2 2 2 2 0 2 1 13
Yang AP [76] 2 2 2 2 2 1 1 1 13
Li KH [77] 2 2 2 2 2 0 2 1 13
Zhang JJ [62] 2 2 2 2 2 0 2 2 14
Huang CL [10] 2 2 2 2 2 0 2 1 13
Wang DW [64] 2 2 2 2 2 1 2 2 15
Liu M [14] 2 2 2 2 2 0 2 1 13
Mo PZ [78] 2 2 2 2 2 1 2 2 15
Peng YD [79] 2 2 2 2 2 2 2 1 15
Feng Y [80] 2 2 2 2 2 0 2 2 14
Cai QX [81] 2 2 2 2 2 2 2 2 16
Li H [82] 2 2 2 2 2 2 1 2 15
Zheng F [83] 2 2 2 2 2 0 2 2 14
Wang KK [84] 2 2 2 2 2 2 2 1 15
Guan W [85] 2 2 2 2 2 2 2 2 16
Gong J [86] 2 2 2 2 2 2 2 2 16
Lei SQ [87] 2 2 2 2 2 2 2 1 15
Deng Q [88] 2 2 2 2 2 2 1 2 15
Mao L [89] 2 2 2 2 2 0 1 2 13
Du RH [90] 2 2 2 2 2 2 2 2 16
Xie HS [91] 2 2 2 2 2 0 2 1 13
Wu J [92] 2 2 2 2 2 0 2 2 14
Chen G [93] 2 2 2 2 2 0 1 1 12
Wan S [94] 2 2 2 2 2 1 2 2 15
Pan HQ [42] 2 2 2 2 2 1 2 2 15
Bo XU [16] 2 2 2 2 2 2 2 2 16
GQQ [95] 2 2 2 2 2 0 2 1 13
Lo LI [96] 2 2 2 2 2 2 1 1 14
1. A clear purpose of the study; 2. Including continuous patients; 3. Expected collection of data; 4. The end point adapted to the research goal; 5. A fair assessment of the end point of the study; 6. A follow-up period commensurate with the objectives of the study; 7. Comprehensive laboratory indicators; 8. Sufficient numbers of patients.
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Table 2
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The quality assessment of included studies.
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The quality assessment of included studies.
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Author 1 2 3 4 5 6 7 8 Score
Qin C [13] 2 2 2 2 2 0 2 2 14
Chen X [74] 2 2 2 2 2 0 1 1 12
Wang R [75] 2. 2 2 2 2 2 2 2 16
Gao Y [68] 2 2 2 2 2 0 2 1 13
Zheng YL [17] 2 2 2 2 2 2 2 1 15
Ma J [15] 2 2 2 2 2 0 2 1 13
Wang DL [18] 2 2 2 2 2 2 2 2 16
Liu W [56] 2 2 2 2 2 0 2 1 13
Yang AP [76] 2 2 2 2 2 1 1 1 13
Li KH [77] 2 2 2 2 2 0 2 1 13
Zhang JJ [62] 2 2 2 2 2 0 2 2 14
Huang CL [10] 2 2 2 2 2 0 2 1 13
Wang DW [64] 2 2 2 2 2 1 2 2 15
Liu M [14] 2 2 2 2 2 0 2 1 13
Mo PZ [78] 2 2 2 2 2 1 2 2 15
Peng YD [79] 2 2 2 2 2 2 2 1 15
Feng Y [80] 2 2 2 2 2 0 2 2 14
Cai QX [81] 2 2 2 2 2 2 2 2 16
Li H [82] 2 2 2 2 2 2 1 2 15
Zheng F [83] 2 2 2 2 2 0 2 2 14
Wang KK [84] 2 2 2 2 2 2 2 1 15
Guan W [85] 2 2 2 2 2 2 2 2 16
Gong J [86] 2 2 2 2 2 2 2 2 16
Lei SQ [87] 2 2 2 2 2 2 2 1 15
Deng Q [88] 2 2 2 2 2 2 1 2 15
Mao L [89] 2 2 2 2 2 0 1 2 13
Du RH [90] 2 2 2 2 2 2 2 2 16
Xie HS [91] 2 2 2 2 2 0 2 1 13
Wu J [92] 2 2 2 2 2 0 2 2 14
Chen G [93] 2 2 2 2 2 0 1 1 12
Wan S [94] 2 2 2 2 2 1 2 2 15
Pan HQ [42] 2 2 2 2 2 1 2 2 15
Bo XU [16] 2 2 2 2 2 2 2 2 16
GQQ [95] 2 2 2 2 2 0 2 1 13
Lo LI [96] 2 2 2 2 2 2 1 1 14
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td
|
2
|
|
td
|
2
|
|
td
|
2
|
|
td
|
0
|
|
td
|
1
|
|
td
|
1
|
|
td
|
12
|
|
tr
|
Wang R [75] 2. 2 2 2 2 2 2 2 16
|
|
td
|
Wang R [75]
|
|
td
|
2.
|
|
td
|
2
|
|
td
|
2
|
|
td
|
2
|
|
td
|
2
|
|
td
|
2
|
|
td
|
2
|
|
td
|
2
|
|
td
|
16
|
|
tr
|
Gao Y [68] 2 2 2 2 2 0 2 1 13
|
|
td
|
Gao Y [68]
|
|
td
|
2
|
|
td
|
2
|
|
td
|
2
|
|
td
|
2
|
|
td
|
2
|
|
td
|
0
|
|
td
|
2
|
|
td
|
1
|
|
td
|
13
|
|
tr
|
Zheng YL [17] 2 2 2 2 2 2 2 1 15
|
|
td
|
Zheng YL [17]
|
|
td
|
2
|
|
td
|
2
|
|
td
|
2
|
|
td
|
2
|
|
td
|
2
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|
td
|
2
|
|
td
|
2
|
|
td
|
1
|
|
td
|
15
|
|
tr
|
Ma J [15] 2 2 2 2 2 0 2 1 13
|
|
td
|
Ma J [15]
|
|
td
|
2
|
|
td
|
2
|
|
td
|
2
|
|
td
|
2
|
|
td
|
2
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td
|
0
|
|
td
|
2
|
|
td
|
1
|
|
td
|
13
|
|
tr
|
Wang DL [18] 2 2 2 2 2 2 2 2 16
|
|
td
|
Wang DL [18]
|
|
td
|
2
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|
td
|
2
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td
|
2
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|
td
|
2
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td
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2
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td
|
2
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td
|
2
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td
|
2
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td
|
16
|
|
tr
|
Liu W [56] 2 2 2 2 2 0 2 1 13
|
|
td
|
Liu W [56]
|
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td
|
2
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|
td
|
2
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td
|
2
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td
|
2
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td
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2
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td
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0
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td
|
2
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td
|
1
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td
|
13
|
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tr
|
Yang AP [76] 2 2 2 2 2 1 1 1 13
|
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td
|
Yang AP [76]
|
|
td
|
2
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|
td
|
2
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td
|
2
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|
td
|
2
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td
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2
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td
|
1
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td
|
1
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td
|
1
|
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td
|
13
|
|
tr
|
Li KH [77] 2 2 2 2 2 0 2 1 13
|
|
td
|
Li KH [77]
|
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td
|
2
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|
td
|
2
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td
|
2
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td
|
2
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td
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2
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td
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0
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td
|
2
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td
|
1
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td
|
13
|
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tr
|
Zhang JJ [62] 2 2 2 2 2 0 2 2 14
|
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td
|
Zhang JJ [62]
|
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td
|
2
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td
|
2
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td
|
2
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td
|
2
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td
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2
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td
|
0
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td
|
2
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td
|
2
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td
|
14
|
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tr
|
Huang CL [10] 2 2 2 2 2 0 2 1 13
|
|
td
|
Huang CL [10]
|
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td
|
2
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td
|
2
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td
|
2
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td
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2
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td
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2
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td
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0
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td
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2
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td
|
1
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td
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13
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tr
|
Wang DW [64] 2 2 2 2 2 1 2 2 15
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td
|
Wang DW [64]
|
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td
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2
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td
|
2
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td
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2
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td
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2
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td
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2
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td
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1
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td
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2
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td
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2
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td
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15
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tr
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Liu M [14] 2 2 2 2 2 0 2 1 13
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td
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Liu M [14]
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td
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2
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td
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2
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td
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2
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td
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2
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td
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2
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td
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0
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td
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2
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td
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1
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td
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13
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tr
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Mo PZ [78] 2 2 2 2 2 1 2 2 15
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td
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Mo PZ [78]
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td
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2
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td
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2
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td
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2
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td
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2
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2
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1
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td
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2
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td
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2
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td
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15
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tr
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Peng YD [79] 2 2 2 2 2 2 2 1 15
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td
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Peng YD [79]
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td
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2
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td
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2
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td
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2
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td
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2
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2
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2
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td
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2
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td
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1
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td
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15
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tr
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Feng Y [80] 2 2 2 2 2 0 2 2 14
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td
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Feng Y [80]
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td
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2
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td
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2
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td
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2
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td
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2
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2
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0
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td
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2
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2
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td
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14
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tr
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Cai QX [81] 2 2 2 2 2 2 2 2 16
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td
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Cai QX [81]
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td
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2
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td
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2
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td
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2
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2
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2
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2
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td
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2
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td
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16
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tr
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Li H [82] 2 2 2 2 2 2 1 2 15
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td
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Li H [82]
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td
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2
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td
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2
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td
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2
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td
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2
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2
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2
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td
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1
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td
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2
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td
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15
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tr
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Zheng F [83] 2 2 2 2 2 0 2 2 14
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td
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Zheng F [83]
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td
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2
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td
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2
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td
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2
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td
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2
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td
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2
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0
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td
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2
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td
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2
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td
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14
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tr
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Wang KK [84] 2 2 2 2 2 2 2 1 15
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td
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Wang KK [84]
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td
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2
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td
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2
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td
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2
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2
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2
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2
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td
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2
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td
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1
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td
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15
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tr
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Guan W [85] 2 2 2 2 2 2 2 2 16
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td
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Guan W [85]
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td
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2
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td
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2
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td
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2
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2
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2
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2
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td
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2
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td
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2
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td
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16
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tr
|
Gong J [86] 2 2 2 2 2 2 2 2 16
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td
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Gong J [86]
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td
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2
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td
|
2
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td
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2
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td
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2
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2
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2
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td
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2
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2
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td
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16
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tr
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Lei SQ [87] 2 2 2 2 2 2 2 1 15
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td
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Lei SQ [87]
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td
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2
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td
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2
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td
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2
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td
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2
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2
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2
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td
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2
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td
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1
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td
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15
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tr
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Deng Q [88] 2 2 2 2 2 2 1 2 15
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td
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Deng Q [88]
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td
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2
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td
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2
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td
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2
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td
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2
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td
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2
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td
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2
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td
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1
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td
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2
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td
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15
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tr
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Mao L [89] 2 2 2 2 2 0 1 2 13
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td
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Mao L [89]
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td
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2
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td
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2
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td
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2
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td
|
2
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td
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2
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td
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0
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td
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1
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td
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2
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td
|
13
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tr
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Du RH [90] 2 2 2 2 2 2 2 2 16
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td
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Du RH [90]
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td
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2
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td
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2
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td
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2
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td
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2
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td
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2
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2
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td
|
2
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td
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2
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td
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16
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tr
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Xie HS [91] 2 2 2 2 2 0 2 1 13
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td
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Xie HS [91]
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td
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2
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td
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2
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td
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2
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td
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2
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td
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2
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td
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0
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td
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2
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td
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1
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td
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13
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tr
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Wu J [92] 2 2 2 2 2 0 2 2 14
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td
|
Wu J [92]
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td
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2
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td
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2
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td
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2
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td
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2
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td
|
2
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td
|
0
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td
|
2
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td
|
2
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td
|
14
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tr
|
Chen G [93] 2 2 2 2 2 0 1 1 12
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td
|
Chen G [93]
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td
|
2
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td
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2
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td
|
2
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td
|
2
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td
|
2
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td
|
0
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td
|
1
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td
|
1
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td
|
12
|
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tr
|
Wan S [94] 2 2 2 2 2 1 2 2 15
|
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td
|
Wan S [94]
|
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td
|
2
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td
|
2
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td
|
2
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td
|
2
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td
|
2
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td
|
1
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td
|
2
|
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td
|
2
|
|
td
|
15
|
|
tr
|
Pan HQ [42] 2 2 2 2 2 1 2 2 15
|
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td
|
Pan HQ [42]
|
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td
|
2
|
|
td
|
2
|
|
td
|
2
|
|
td
|
2
|
|
td
|
2
|
|
td
|
1
|
|
td
|
2
|
|
td
|
2
|
|
td
|
15
|
|
tr
|
Bo XU [16] 2 2 2 2 2 2 2 2 16
|
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td
|
Bo XU [16]
|
|
td
|
2
|
|
td
|
2
|
|
td
|
2
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td
|
2
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td
|
2
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td
|
2
|
|
td
|
2
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|
td
|
2
|
|
td
|
16
|
|
tr
|
GQQ [95] 2 2 2 2 2 0 2 1 13
|
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td
|
GQQ [95]
|
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td
|
2
|
|
td
|
2
|
|
td
|
2
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td
|
2
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td
|
2
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td
|
0
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td
|
2
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td
|
1
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td
|
13
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tr
|
Lo LI [96] 2 2 2 2 2 2 1 1 14
|
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td
|
Lo LI [96]
|
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td
|
2
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|
td
|
2
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td
|
2
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td
|
2
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td
|
2
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td
|
2
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td
|
1
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td
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1
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td
|
14
|
|
table-wrap-foot
|
1. A clear purpose of the study; 2. Including continuous patients; 3. Expected collection of data; 4. The end point adapted to the research goal; 5. A fair assessment of the end point of the study; 6. A follow-up period commensurate with the objectives of the study; 7. Comprehensive laboratory indicators; 8. Sufficient numbers of patients.
|
|
footnote
|
1. A clear purpose of the study; 2. Including continuous patients; 3. Expected collection of data; 4. The end point adapted to the research goal; 5. A fair assessment of the end point of the study; 6. A follow-up period commensurate with the objectives of the study; 7. Comprehensive laboratory indicators; 8. Sufficient numbers of patients.
|
|
p
|
1. A clear purpose of the study; 2. Including continuous patients; 3. Expected collection of data; 4. The end point adapted to the research goal; 5. A fair assessment of the end point of the study; 6. A follow-up period commensurate with the objectives of the study; 7. Comprehensive laboratory indicators; 8. Sufficient numbers of patients.
|
|
table-wrap
|
Table3 Results of Laboratory findings of severe and non-severe patients infected with COVID-19.
Variables Classification Number of articles included Number of patients included Mean (95% CI) P-value
Blood routine WBC×109 /l Non-severe patients 34 4236 5.07 (4.90, 5.24) <0.01
Severe patients 1703 6.06 (5.67, 6.46)
All patients 5939 5.39 (5.22, 5.55)
Neutrophils×109 /l Non-severe patients 29 3005 3.71 (3.36, 4.06) <0.01
Severe patients 1368 4.94 (4.30, 5.58)
All patients 4373 4.22 (3.94, 4.51)
Lymphocytes×109 /l Non-severe patients 34 4228 1.15 (1.08, 1.22) <0.01
Severe patients 1703 0.80 (0.75, 0.84)
All patients 5931 0.98 (0.92, 1.04)
Eosinophil×109 /l Non-severe patients 5 1035 0.04 (0.03, 0.05) 0.003
Severe patients 451 0.02 (0.01, 0.02)
All patients 1486 0.03 (0.02, 0.04)
Monocyte×109 /l Non-severe patients 13 1397 0.41 (0.40, 0.42) 0.086
Severe patients 700 0.38 (0.35, 0.41)
All patients 2097 0.40 (0.39, 0.42)
PLT×109 /l Non-severe patients 20 3068 184.19 (178.04, 190.33) 0.340
Severe patients 1019 212.58 (154.63, 270.53)
All patients 4087 201.44 (175.75, 227.12)
Hbg/l Non-severe patients 14 2652 131.36 (128.33, 134.39) 0.164
Severe patients 711 126.73 (121.16, 132.30)
All patients 3363 128.98 (126.17, 131.78)
Inflammation-related factors ESR mm/60 min Non-severe patients 8 1390 28.16 (20.13, 36.2) 0.111
Severe patients 695 40.54 (27.61, 53.4)
All patients 2085 33.94 (28.10, 39.7)
CRP mg/l Non-severe patients 26 2939 19.83 (16.67, 23.0) <0.01
Severe patients 1415 60.91 (49.24, 72.5)
All patients 4354 36.99 (33.31, 40.67)
PCT ng/ml Non-severe patients 22 2325 0.07 (0.05, 0.09) <0.01
Severe patients 1222 0.14 (0.11, 0.17)
All patients 3547 0.10 (0.08, 0.12)
Blood biochemistry ALT U/l Non-severe patients 26 2769 24.85 (22.69, 27.0) <0.01
Severe patients 1027 33.78 (29.54, 38.0)
All patients 3796 28.31 (26.25, 30.3)
AST U/l Non-severe patients 26 2731 26.24 (25.29, 28.18) <0.01
Severe patients 1019 36.78 (33.69, 39.87)
All patients 3750 30.66 (29.19, 32.13)
LDH U/l Non-severe patients 19 2306 224.20 (205.33, 243.07) <0.01
Severe patients 811 344.48 (307.08, 381.88)
All patients 3117 271.82 (254.13, 289.52)
CK U/l Non-severe patients 17 2241 77.69 (69.68, 85.70) <0.01
Severe patients 849 111.92 (98.24, 125.61)
All patients 3090 90.92 (83.54, 98.29)
CK-MB U/l Non-severe patients 9 1329 8.76 (4.74, 12.79) 0.246
Severe patients 550 12.26 (7.93, 16.58)
All patients 1879 10.51 (8.33, 12.70)
Albumin g/l Non-severe patients 10 985 39.41 (37.95, 40.87) <0.01
Severe patients 431 34.29 (32.79, 35.80)
All patients 1416 36.75 (35.17, 38.32)
Creatinine μmol/l Non-severe patients 25 2696 66.97 (64.65, 69.28) <0.01
Severe patients 1000 72.94 (69.23, 76.66)
All patients 3696 69.61 (67.57, 71.65)
Urea mmol/l Non-severe patients 16 1929 4.36 (4.12, 4.59) <0.01
Severe patients 703 5.59 (5.39, 6.51)
All patients 2632 4.98 (4.72, 5.23)
Total bilirubin mmol/l Non-severe patients 14 2065 10.38 (9.78, 10.99) 0.017
Severe patients 564 11.86 (10.81, 12.91)
All patients 2629 10.92 (10.39, 11.45)
Blood coagulation function APTTs Non-severe patients 12 1422 33.49 (31.17, 35.82) 0.724
Severe patients 407 32.92 (30.78, 35.06)
All patients 1829 33.23 (31.59, 34.86)
PT s Non-severe patients 12 1388 12.45 (11.98, 12.91) 0.319
Severe patients 504 12.80 (12.29, 13.30)
All patients 1892 12.63 (12.31, 12.94)
D dimer mg/l Non-severe patients 23 2503 0.47 (0.40, 0.53) <0.01
Severe patients 1043 1.29 (0.03, 0.54)
All patients 3546 0.61 (0.54, 0.67)
Lymphocyte subsets CD4 T cells /μL Non-severe patients 5 475 561.81 (485.46, 638.15) <0.01
Severe patients 208 266.79 (204.51, 329.07)
All patients 683 407.03 (310.24, 503.83)
CD8 T cells /μL Non-severe patients 5 475 349.01 (292.61, 405.40) <0.01
Severe patients 208 174.61 (125.95, 223.27)
All patients 683 266.65 (198.49, 334.81)
Cytokines IL-1β pg/ml Non-severe patients 2 246 5.01 (4.96, 5.06) 0.055
Severe patients 393 5.11 (5.02, 5.20)
All patients 639 5.06 (4.98, 5.15)
IL-6 pg/ml Non-severe patients 5 312 13.22 (6.88, 19.57) 0.027
Severe patients 455 25.58 (16.69, 34.47)
All patients 767 19.66 (13.44, 25.89)
IL-10 pg/ml Non-severe patients 2 246 5.71 (5.51, 5.90) <0.01
Severe patients 393 8.87 (7.15, 10.59)
All patients 639 7.23 (6.18, 8.28)
SPSS25 was used. Comparison between severe and non-severe patients with t test or Mann-Whitney U test.
|
|
label
|
Table3
|
|
caption
|
Results of Laboratory findings of severe and non-severe patients infected with COVID-19.
|
|
p
|
Results of Laboratory findings of severe and non-severe patients infected with COVID-19.
|
|
table
|
Variables Classification Number of articles included Number of patients included Mean (95% CI) P-value
Blood routine WBC×109 /l Non-severe patients 34 4236 5.07 (4.90, 5.24) <0.01
Severe patients 1703 6.06 (5.67, 6.46)
All patients 5939 5.39 (5.22, 5.55)
Neutrophils×109 /l Non-severe patients 29 3005 3.71 (3.36, 4.06) <0.01
Severe patients 1368 4.94 (4.30, 5.58)
All patients 4373 4.22 (3.94, 4.51)
Lymphocytes×109 /l Non-severe patients 34 4228 1.15 (1.08, 1.22) <0.01
Severe patients 1703 0.80 (0.75, 0.84)
All patients 5931 0.98 (0.92, 1.04)
Eosinophil×109 /l Non-severe patients 5 1035 0.04 (0.03, 0.05) 0.003
Severe patients 451 0.02 (0.01, 0.02)
All patients 1486 0.03 (0.02, 0.04)
Monocyte×109 /l Non-severe patients 13 1397 0.41 (0.40, 0.42) 0.086
Severe patients 700 0.38 (0.35, 0.41)
All patients 2097 0.40 (0.39, 0.42)
PLT×109 /l Non-severe patients 20 3068 184.19 (178.04, 190.33) 0.340
Severe patients 1019 212.58 (154.63, 270.53)
All patients 4087 201.44 (175.75, 227.12)
Hbg/l Non-severe patients 14 2652 131.36 (128.33, 134.39) 0.164
Severe patients 711 126.73 (121.16, 132.30)
All patients 3363 128.98 (126.17, 131.78)
Inflammation-related factors ESR mm/60 min Non-severe patients 8 1390 28.16 (20.13, 36.2) 0.111
Severe patients 695 40.54 (27.61, 53.4)
All patients 2085 33.94 (28.10, 39.7)
CRP mg/l Non-severe patients 26 2939 19.83 (16.67, 23.0) <0.01
Severe patients 1415 60.91 (49.24, 72.5)
All patients 4354 36.99 (33.31, 40.67)
PCT ng/ml Non-severe patients 22 2325 0.07 (0.05, 0.09) <0.01
Severe patients 1222 0.14 (0.11, 0.17)
All patients 3547 0.10 (0.08, 0.12)
Blood biochemistry ALT U/l Non-severe patients 26 2769 24.85 (22.69, 27.0) <0.01
Severe patients 1027 33.78 (29.54, 38.0)
All patients 3796 28.31 (26.25, 30.3)
AST U/l Non-severe patients 26 2731 26.24 (25.29, 28.18) <0.01
Severe patients 1019 36.78 (33.69, 39.87)
All patients 3750 30.66 (29.19, 32.13)
LDH U/l Non-severe patients 19 2306 224.20 (205.33, 243.07) <0.01
Severe patients 811 344.48 (307.08, 381.88)
All patients 3117 271.82 (254.13, 289.52)
CK U/l Non-severe patients 17 2241 77.69 (69.68, 85.70) <0.01
Severe patients 849 111.92 (98.24, 125.61)
All patients 3090 90.92 (83.54, 98.29)
CK-MB U/l Non-severe patients 9 1329 8.76 (4.74, 12.79) 0.246
Severe patients 550 12.26 (7.93, 16.58)
All patients 1879 10.51 (8.33, 12.70)
Albumin g/l Non-severe patients 10 985 39.41 (37.95, 40.87) <0.01
Severe patients 431 34.29 (32.79, 35.80)
All patients 1416 36.75 (35.17, 38.32)
Creatinine μmol/l Non-severe patients 25 2696 66.97 (64.65, 69.28) <0.01
Severe patients 1000 72.94 (69.23, 76.66)
All patients 3696 69.61 (67.57, 71.65)
Urea mmol/l Non-severe patients 16 1929 4.36 (4.12, 4.59) <0.01
Severe patients 703 5.59 (5.39, 6.51)
All patients 2632 4.98 (4.72, 5.23)
Total bilirubin mmol/l Non-severe patients 14 2065 10.38 (9.78, 10.99) 0.017
Severe patients 564 11.86 (10.81, 12.91)
All patients 2629 10.92 (10.39, 11.45)
Blood coagulation function APTTs Non-severe patients 12 1422 33.49 (31.17, 35.82) 0.724
Severe patients 407 32.92 (30.78, 35.06)
All patients 1829 33.23 (31.59, 34.86)
PT s Non-severe patients 12 1388 12.45 (11.98, 12.91) 0.319
Severe patients 504 12.80 (12.29, 13.30)
All patients 1892 12.63 (12.31, 12.94)
D dimer mg/l Non-severe patients 23 2503 0.47 (0.40, 0.53) <0.01
Severe patients 1043 1.29 (0.03, 0.54)
All patients 3546 0.61 (0.54, 0.67)
Lymphocyte subsets CD4 T cells /μL Non-severe patients 5 475 561.81 (485.46, 638.15) <0.01
Severe patients 208 266.79 (204.51, 329.07)
All patients 683 407.03 (310.24, 503.83)
CD8 T cells /μL Non-severe patients 5 475 349.01 (292.61, 405.40) <0.01
Severe patients 208 174.61 (125.95, 223.27)
All patients 683 266.65 (198.49, 334.81)
Cytokines IL-1β pg/ml Non-severe patients 2 246 5.01 (4.96, 5.06) 0.055
Severe patients 393 5.11 (5.02, 5.20)
All patients 639 5.06 (4.98, 5.15)
IL-6 pg/ml Non-severe patients 5 312 13.22 (6.88, 19.57) 0.027
Severe patients 455 25.58 (16.69, 34.47)
All patients 767 19.66 (13.44, 25.89)
IL-10 pg/ml Non-severe patients 2 246 5.71 (5.51, 5.90) <0.01
Severe patients 393 8.87 (7.15, 10.59)
All patients 639 7.23 (6.18, 8.28)
|
|
tr
|
Variables Classification Number of articles included Number of patients included Mean (95% CI) P-value
|
|
th
|
Variables
|
|
th
|
Classification
|
|
th
|
Number of articles included
|
|
th
|
Number of patients included
|
|
th
|
Mean (95% CI)
|
|
th
|
P-value
|
|
tr
|
Blood routine WBC×109 /l Non-severe patients 34 4236 5.07 (4.90, 5.24) <0.01
|
|
td
|
Blood routine
|
|
td
|
WBC×109 /l
|
|
td
|
Non-severe patients
|
|
td
|
34
|
|
td
|
4236
|
|
td
|
5.07 (4.90, 5.24)
|
|
td
|
<0.01
|
|
tr
|
Severe patients 1703 6.06 (5.67, 6.46)
|
|
td
|
Severe patients
|
|
td
|
1703
|
|
td
|
6.06 (5.67, 6.46)
|
|
tr
|
All patients 5939 5.39 (5.22, 5.55)
|
|
td
|
All patients
|
|
td
|
5939
|
|
td
|
5.39 (5.22, 5.55)
|
|
tr
|
Neutrophils×109 /l Non-severe patients 29 3005 3.71 (3.36, 4.06) <0.01
|
|
td
|
Neutrophils×109 /l
|
|
td
|
Non-severe patients
|
|
td
|
29
|
|
td
|
3005
|
|
td
|
3.71 (3.36, 4.06)
|
|
td
|
<0.01
|
|
tr
|
Severe patients 1368 4.94 (4.30, 5.58)
|
|
td
|
Severe patients
|
|
td
|
1368
|
|
td
|
4.94 (4.30, 5.58)
|
|
tr
|
All patients 4373 4.22 (3.94, 4.51)
|
|
td
|
All patients
|
|
td
|
4373
|
|
td
|
4.22 (3.94, 4.51)
|
|
tr
|
Lymphocytes×109 /l Non-severe patients 34 4228 1.15 (1.08, 1.22) <0.01
|
|
td
|
Lymphocytes×109 /l
|
|
td
|
Non-severe patients
|
|
td
|
34
|
|
td
|
4228
|
|
td
|
1.15 (1.08, 1.22)
|
|
td
|
<0.01
|
|
tr
|
Severe patients 1703 0.80 (0.75, 0.84)
|
|
td
|
Severe patients
|
|
td
|
1703
|
|
td
|
0.80 (0.75, 0.84)
|
|
tr
|
All patients 5931 0.98 (0.92, 1.04)
|
|
td
|
All patients
|
|
td
|
5931
|
|
td
|
0.98 (0.92, 1.04)
|
|
tr
|
Eosinophil×109 /l Non-severe patients 5 1035 0.04 (0.03, 0.05) 0.003
|
|
td
|
Eosinophil×109 /l
|
|
td
|
Non-severe patients
|
|
td
|
5
|
|
td
|
1035
|
|
td
|
0.04 (0.03, 0.05)
|
|
td
|
0.003
|
|
tr
|
Severe patients 451 0.02 (0.01, 0.02)
|
|
td
|
Severe patients
|
|
td
|
451
|
|
td
|
0.02 (0.01, 0.02)
|
|
tr
|
All patients 1486 0.03 (0.02, 0.04)
|
|
td
|
All patients
|
|
td
|
1486
|
|
td
|
0.03 (0.02, 0.04)
|
|
tr
|
Monocyte×109 /l Non-severe patients 13 1397 0.41 (0.40, 0.42) 0.086
|
|
td
|
Monocyte×109 /l
|
|
td
|
Non-severe patients
|
|
td
|
13
|
|
td
|
1397
|
|
td
|
0.41 (0.40, 0.42)
|
|
td
|
0.086
|
|
tr
|
Severe patients 700 0.38 (0.35, 0.41)
|
|
td
|
Severe patients
|
|
td
|
700
|
|
td
|
0.38 (0.35, 0.41)
|
|
tr
|
All patients 2097 0.40 (0.39, 0.42)
|
|
td
|
All patients
|
|
td
|
2097
|
|
td
|
0.40 (0.39, 0.42)
|
|
tr
|
PLT×109 /l Non-severe patients 20 3068 184.19 (178.04, 190.33) 0.340
|
|
td
|
PLT×109 /l
|
|
td
|
Non-severe patients
|
|
td
|
20
|
|
td
|
3068
|
|
td
|
184.19 (178.04, 190.33)
|
|
td
|
0.340
|
|
tr
|
Severe patients 1019 212.58 (154.63, 270.53)
|
|
td
|
Severe patients
|
|
td
|
1019
|
|
td
|
212.58 (154.63, 270.53)
|
|
tr
|
All patients 4087 201.44 (175.75, 227.12)
|
|
td
|
All patients
|
|
td
|
4087
|
|
td
|
201.44 (175.75, 227.12)
|
|
tr
|
Hbg/l Non-severe patients 14 2652 131.36 (128.33, 134.39) 0.164
|
|
td
|
Hbg/l
|
|
td
|
Non-severe patients
|
|
td
|
14
|
|
td
|
2652
|
|
td
|
131.36 (128.33, 134.39)
|
|
td
|
0.164
|
|
tr
|
Severe patients 711 126.73 (121.16, 132.30)
|
|
td
|
Severe patients
|
|
td
|
711
|
|
td
|
126.73 (121.16, 132.30)
|
|
tr
|
All patients 3363 128.98 (126.17, 131.78)
|
|
td
|
All patients
|
|
td
|
3363
|
|
td
|
128.98 (126.17, 131.78)
|
|
tr
|
Inflammation-related factors ESR mm/60 min Non-severe patients 8 1390 28.16 (20.13, 36.2) 0.111
|
|
td
|
Inflammation-related factors
|
|
td
|
ESR mm/60 min
|
|
td
|
Non-severe patients
|
|
td
|
8
|
|
td
|
1390
|
|
td
|
28.16 (20.13, 36.2)
|
|
td
|
0.111
|
|
tr
|
Severe patients 695 40.54 (27.61, 53.4)
|
|
td
|
Severe patients
|
|
td
|
695
|
|
td
|
40.54 (27.61, 53.4)
|
|
tr
|
All patients 2085 33.94 (28.10, 39.7)
|
|
td
|
All patients
|
|
td
|
2085
|
|
td
|
33.94 (28.10, 39.7)
|
|
tr
|
CRP mg/l Non-severe patients 26 2939 19.83 (16.67, 23.0) <0.01
|
|
td
|
CRP mg/l
|
|
td
|
Non-severe patients
|
|
td
|
26
|
|
td
|
2939
|
|
td
|
19.83 (16.67, 23.0)
|
|
td
|
<0.01
|
|
tr
|
Severe patients 1415 60.91 (49.24, 72.5)
|
|
td
|
Severe patients
|
|
td
|
1415
|
|
td
|
60.91 (49.24, 72.5)
|
|
tr
|
All patients 4354 36.99 (33.31, 40.67)
|
|
td
|
All patients
|
|
td
|
4354
|
|
td
|
36.99 (33.31, 40.67)
|
|
tr
|
PCT ng/ml Non-severe patients 22 2325 0.07 (0.05, 0.09) <0.01
|
|
td
|
PCT ng/ml
|
|
td
|
Non-severe patients
|
|
td
|
22
|
|
td
|
2325
|
|
td
|
0.07 (0.05, 0.09)
|
|
td
|
<0.01
|
|
tr
|
Severe patients 1222 0.14 (0.11, 0.17)
|
|
td
|
Severe patients
|
|
td
|
1222
|
|
td
|
0.14 (0.11, 0.17)
|
|
tr
|
All patients 3547 0.10 (0.08, 0.12)
|
|
td
|
All patients
|
|
td
|
3547
|
|
td
|
0.10 (0.08, 0.12)
|
|
tr
|
Blood biochemistry ALT U/l Non-severe patients 26 2769 24.85 (22.69, 27.0) <0.01
|
|
td
|
Blood biochemistry
|
|
td
|
ALT U/l
|
|
td
|
Non-severe patients
|
|
td
|
26
|
|
td
|
2769
|
|
td
|
24.85 (22.69, 27.0)
|
|
td
|
<0.01
|
|
tr
|
Severe patients 1027 33.78 (29.54, 38.0)
|
|
td
|
Severe patients
|
|
td
|
1027
|
|
td
|
33.78 (29.54, 38.0)
|
|
tr
|
All patients 3796 28.31 (26.25, 30.3)
|
|
td
|
All patients
|
|
td
|
3796
|
|
td
|
28.31 (26.25, 30.3)
|
|
tr
|
AST U/l Non-severe patients 26 2731 26.24 (25.29, 28.18) <0.01
|
|
td
|
AST U/l
|
|
td
|
Non-severe patients
|
|
td
|
26
|
|
td
|
2731
|
|
td
|
26.24 (25.29, 28.18)
|
|
td
|
<0.01
|
|
tr
|
Severe patients 1019 36.78 (33.69, 39.87)
|
|
td
|
Severe patients
|
|
td
|
1019
|
|
td
|
36.78 (33.69, 39.87)
|
|
tr
|
All patients 3750 30.66 (29.19, 32.13)
|
|
td
|
All patients
|
|
td
|
3750
|
|
td
|
30.66 (29.19, 32.13)
|
|
tr
|
LDH U/l Non-severe patients 19 2306 224.20 (205.33, 243.07) <0.01
|
|
td
|
LDH U/l
|
|
td
|
Non-severe patients
|
|
td
|
19
|
|
td
|
2306
|
|
td
|
224.20 (205.33, 243.07)
|
|
td
|
<0.01
|
|
tr
|
Severe patients 811 344.48 (307.08, 381.88)
|
|
td
|
Severe patients
|
|
td
|
811
|
|
td
|
344.48 (307.08, 381.88)
|
|
tr
|
All patients 3117 271.82 (254.13, 289.52)
|
|
td
|
All patients
|
|
td
|
3117
|
|
td
|
271.82 (254.13, 289.52)
|
|
tr
|
CK U/l Non-severe patients 17 2241 77.69 (69.68, 85.70) <0.01
|
|
td
|
CK U/l
|
|
td
|
Non-severe patients
|
|
td
|
17
|
|
td
|
2241
|
|
td
|
77.69 (69.68, 85.70)
|
|
td
|
<0.01
|
|
tr
|
Severe patients 849 111.92 (98.24, 125.61)
|
|
td
|
Severe patients
|
|
td
|
849
|
|
td
|
111.92 (98.24, 125.61)
|
|
tr
|
All patients 3090 90.92 (83.54, 98.29)
|
|
td
|
All patients
|
|
td
|
3090
|
|
td
|
90.92 (83.54, 98.29)
|
|
tr
|
CK-MB U/l Non-severe patients 9 1329 8.76 (4.74, 12.79) 0.246
|
|
td
|
CK-MB U/l
|
|
td
|
Non-severe patients
|
|
td
|
9
|
|
td
|
1329
|
|
td
|
8.76 (4.74, 12.79)
|
|
td
|
0.246
|
|
tr
|
Severe patients 550 12.26 (7.93, 16.58)
|
|
td
|
Severe patients
|
|
td
|
550
|
|
td
|
12.26 (7.93, 16.58)
|
|
tr
|
All patients 1879 10.51 (8.33, 12.70)
|
|
td
|
All patients
|
|
td
|
1879
|
|
td
|
10.51 (8.33, 12.70)
|
|
tr
|
Albumin g/l Non-severe patients 10 985 39.41 (37.95, 40.87) <0.01
|
|
td
|
Albumin g/l
|
|
td
|
Non-severe patients
|
|
td
|
10
|
|
td
|
985
|
|
td
|
39.41 (37.95, 40.87)
|
|
td
|
<0.01
|
|
tr
|
Severe patients 431 34.29 (32.79, 35.80)
|
|
td
|
Severe patients
|
|
td
|
431
|
|
td
|
34.29 (32.79, 35.80)
|
|
tr
|
All patients 1416 36.75 (35.17, 38.32)
|
|
td
|
All patients
|
|
td
|
1416
|
|
td
|
36.75 (35.17, 38.32)
|
|
tr
|
Creatinine μmol/l Non-severe patients 25 2696 66.97 (64.65, 69.28) <0.01
|
|
td
|
Creatinine μmol/l
|
|
td
|
Non-severe patients
|
|
td
|
25
|
|
td
|
2696
|
|
td
|
66.97 (64.65, 69.28)
|
|
td
|
<0.01
|
|
tr
|
Severe patients 1000 72.94 (69.23, 76.66)
|
|
td
|
Severe patients
|
|
td
|
1000
|
|
td
|
72.94 (69.23, 76.66)
|
|
tr
|
All patients 3696 69.61 (67.57, 71.65)
|
|
td
|
All patients
|
|
td
|
3696
|
|
td
|
69.61 (67.57, 71.65)
|
|
tr
|
Urea mmol/l Non-severe patients 16 1929 4.36 (4.12, 4.59) <0.01
|
|
td
|
Urea mmol/l
|
|
td
|
Non-severe patients
|
|
td
|
16
|
|
td
|
1929
|
|
td
|
4.36 (4.12, 4.59)
|
|
td
|
<0.01
|
|
tr
|
Severe patients 703 5.59 (5.39, 6.51)
|
|
td
|
Severe patients
|
|
td
|
703
|
|
td
|
5.59 (5.39, 6.51)
|
|
tr
|
All patients 2632 4.98 (4.72, 5.23)
|
|
td
|
All patients
|
|
td
|
2632
|
|
td
|
4.98 (4.72, 5.23)
|
|
tr
|
Total bilirubin mmol/l Non-severe patients 14 2065 10.38 (9.78, 10.99) 0.017
|
|
td
|
Total bilirubin mmol/l
|
|
td
|
Non-severe patients
|
|
td
|
14
|
|
td
|
2065
|
|
td
|
10.38 (9.78, 10.99)
|
|
td
|
0.017
|
|
tr
|
Severe patients 564 11.86 (10.81, 12.91)
|
|
td
|
Severe patients
|
|
td
|
564
|
|
td
|
11.86 (10.81, 12.91)
|
|
tr
|
All patients 2629 10.92 (10.39, 11.45)
|
|
td
|
All patients
|
|
td
|
2629
|
|
td
|
10.92 (10.39, 11.45)
|
|
tr
|
Blood coagulation function APTTs Non-severe patients 12 1422 33.49 (31.17, 35.82) 0.724
|
|
td
|
Blood coagulation function
|
|
td
|
APTTs
|
|
td
|
Non-severe patients
|
|
td
|
12
|
|
td
|
1422
|
|
td
|
33.49 (31.17, 35.82)
|
|
td
|
0.724
|
|
tr
|
Severe patients 407 32.92 (30.78, 35.06)
|
|
td
|
Severe patients
|
|
td
|
407
|
|
td
|
32.92 (30.78, 35.06)
|
|
tr
|
All patients 1829 33.23 (31.59, 34.86)
|
|
td
|
All patients
|
|
td
|
1829
|
|
td
|
33.23 (31.59, 34.86)
|
|
tr
|
PT s Non-severe patients 12 1388 12.45 (11.98, 12.91) 0.319
|
|
td
|
PT s
|
|
td
|
Non-severe patients
|
|
td
|
12
|
|
td
|
1388
|
|
td
|
12.45 (11.98, 12.91)
|
|
td
|
0.319
|
|
tr
|
Severe patients 504 12.80 (12.29, 13.30)
|
|
td
|
Severe patients
|
|
td
|
504
|
|
td
|
12.80 (12.29, 13.30)
|
|
tr
|
All patients 1892 12.63 (12.31, 12.94)
|
|
td
|
All patients
|
|
td
|
1892
|
|
td
|
12.63 (12.31, 12.94)
|
|
tr
|
D dimer mg/l Non-severe patients 23 2503 0.47 (0.40, 0.53) <0.01
|
|
td
|
D dimer mg/l
|
|
td
|
Non-severe patients
|
|
td
|
23
|
|
td
|
2503
|
|
td
|
0.47 (0.40, 0.53)
|
|
td
|
<0.01
|
|
tr
|
Severe patients 1043 1.29 (0.03, 0.54)
|
|
td
|
Severe patients
|
|
td
|
1043
|
|
td
|
1.29 (0.03, 0.54)
|
|
tr
|
All patients 3546 0.61 (0.54, 0.67)
|
|
td
|
All patients
|
|
td
|
3546
|
|
td
|
0.61 (0.54, 0.67)
|
|
tr
|
Lymphocyte subsets CD4 T cells /μL Non-severe patients 5 475 561.81 (485.46, 638.15) <0.01
|
|
td
|
Lymphocyte subsets
|
|
td
|
CD4 T cells /μL
|
|
td
|
Non-severe patients
|
|
td
|
5
|
|
td
|
475
|
|
td
|
561.81 (485.46, 638.15)
|
|
td
|
<0.01
|
|
tr
|
Severe patients 208 266.79 (204.51, 329.07)
|
|
td
|
Severe patients
|
|
td
|
208
|
|
td
|
266.79 (204.51, 329.07)
|
|
tr
|
All patients 683 407.03 (310.24, 503.83)
|
|
td
|
All patients
|
|
td
|
683
|
|
td
|
407.03 (310.24, 503.83)
|
|
tr
|
CD8 T cells /μL Non-severe patients 5 475 349.01 (292.61, 405.40) <0.01
|
|
td
|
CD8 T cells /μL
|
|
td
|
Non-severe patients
|
|
td
|
5
|
|
td
|
475
|
|
td
|
349.01 (292.61, 405.40)
|
|
td
|
<0.01
|
|
tr
|
Severe patients 208 174.61 (125.95, 223.27)
|
|
td
|
Severe patients
|
|
td
|
208
|
|
td
|
174.61 (125.95, 223.27)
|
|
tr
|
All patients 683 266.65 (198.49, 334.81)
|
|
td
|
All patients
|
|
td
|
683
|
|
td
|
266.65 (198.49, 334.81)
|
|
tr
|
Cytokines IL-1β pg/ml Non-severe patients 2 246 5.01 (4.96, 5.06) 0.055
|
|
td
|
Cytokines
|
|
td
|
IL-1β pg/ml
|
|
td
|
Non-severe patients
|
|
td
|
2
|
|
td
|
246
|
|
td
|
5.01 (4.96, 5.06)
|
|
td
|
0.055
|
|
tr
|
Severe patients 393 5.11 (5.02, 5.20)
|
|
td
|
Severe patients
|
|
td
|
393
|
|
td
|
5.11 (5.02, 5.20)
|
|
tr
|
All patients 639 5.06 (4.98, 5.15)
|
|
td
|
All patients
|
|
td
|
639
|
|
td
|
5.06 (4.98, 5.15)
|
|
tr
|
IL-6 pg/ml Non-severe patients 5 312 13.22 (6.88, 19.57) 0.027
|
|
td
|
IL-6 pg/ml
|
|
td
|
Non-severe patients
|
|
td
|
5
|
|
td
|
312
|
|
td
|
13.22 (6.88, 19.57)
|
|
td
|
0.027
|
|
tr
|
Severe patients 455 25.58 (16.69, 34.47)
|
|
td
|
Severe patients
|
|
td
|
455
|
|
td
|
25.58 (16.69, 34.47)
|
|
tr
|
All patients 767 19.66 (13.44, 25.89)
|
|
td
|
All patients
|
|
td
|
767
|
|
td
|
19.66 (13.44, 25.89)
|
|
tr
|
IL-10 pg/ml Non-severe patients 2 246 5.71 (5.51, 5.90) <0.01
|
|
td
|
IL-10 pg/ml
|
|
td
|
Non-severe patients
|
|
td
|
2
|
|
td
|
246
|
|
td
|
5.71 (5.51, 5.90)
|
|
td
|
<0.01
|
|
tr
|
Severe patients 393 8.87 (7.15, 10.59)
|
|
td
|
Severe patients
|
|
td
|
393
|
|
td
|
8.87 (7.15, 10.59)
|
|
tr
|
All patients 639 7.23 (6.18, 8.28)
|
|
td
|
All patients
|
|
td
|
639
|
|
td
|
7.23 (6.18, 8.28)
|
|
table-wrap-foot
|
SPSS25 was used. Comparison between severe and non-severe patients with t test or Mann-Whitney U test.
|
|
footnote
|
SPSS25 was used. Comparison between severe and non-severe patients with t test or Mann-Whitney U test.
|
|
p
|
SPSS25 was used. Comparison between severe and non-severe patients with t test or Mann-Whitney U test.
|
|
figure
|
Fig. 2 Meta-analysis of lymphocytes.
|
|
label
|
Fig. 2
|
|
caption
|
Meta-analysis of lymphocytes.
|
|
p
|
Meta-analysis of lymphocytes.
|
|
figure
|
Fig. 3 Meta-analysis of CRP.
|
|
label
|
Fig. 3
|
|
caption
|
Meta-analysis of CRP.
|
|
p
|
Meta-analysis of CRP.
|
|
figure
|
Fig. 4 Meta-analysis of PCT.
|
|
label
|
Fig. 4
|
|
caption
|
Meta-analysis of PCT.
|
|
p
|
Meta-analysis of PCT.
|
|
figure
|
Fig. 5 Meta-analysis of ALT.
|
|
label
|
Fig. 5
|
|
caption
|
Meta-analysis of ALT.
|
|
p
|
Meta-analysis of ALT.
|
|
figure
|
Fig. 6 Meta-analysis of AST.
|
|
label
|
Fig. 6
|
|
caption
|
Meta-analysis of AST.
|
|
p
|
Meta-analysis of AST.
|
|
figure
|
Fig. 7 Meta-analysis of LDH.
|
|
label
|
Fig. 7
|
|
caption
|
Meta-analysis of LDH.
|
|
p
|
Meta-analysis of LDH.
|
|
figure
|
Fig. 8 Meta-analysis of D-dimer.
|
|
label
|
Fig. 8
|
|
caption
|
Meta-analysis of D-dimer.
|
|
p
|
Meta-analysis of D-dimer.
|
|
figure
|
Fig. 9 Meta-analysis of CD4 T cells.
|
|
label
|
Fig. 9
|
|
caption
|
Meta-analysis of CD4 T cells.
|
|
p
|
Meta-analysis of CD4 T cells.
|
|
figure
|
Fig. 10 Meta-analysis of IL-6.
|
|
label
|
Fig. 10
|
|
caption
|
Meta-analysis of IL-6.
|
|
p
|
Meta-analysis of IL-6.
|
|
sec
|
3.2.1 Blood routine examination
Leukopenia was observed in 21.92% (363/1656) patients with lymphocytopenia in 29.02% (886/3053) patients. Elevated neutrophils were observed in 19.85% (81/408) patients. 14.73% (75/509) and 12.68% (78/615) patients were accompanied by a decrease in Hemoglobin and platelet count (PLT) respectively. Most importantly, there were several significant differences between severe patients and non-severe patients, including higher leukocyte (1.20-fold; 6.06 vs 5.07 × 109/l; P < 0.01) and neutrophil (1.33-fold; 4.94 vs 3.71 × 109/l; P < 0.01), lower lymphocyte (1.44-fold; 1.15 vs 0.80 × 109/l; P < 0.01), eosinophils (2.00-fold; 0.04 vs 0.02 × 109/l; P = 0.03), monocytes (1.08-fold; 0.38 vs 0.41 × 109/l; P = 0.041), PLT (1.15-fold; 212.58 vs 184.19 × 109/l; P = 0.987) and hemoglobin (1.53-fold; 131.36vs 126.73 × 109 g/l; P = 0.163).
|
|
label
|
3.2.1
|
|
title
|
Blood routine examination
|
|
p
|
Leukopenia was observed in 21.92% (363/1656) patients with lymphocytopenia in 29.02% (886/3053) patients. Elevated neutrophils were observed in 19.85% (81/408) patients. 14.73% (75/509) and 12.68% (78/615) patients were accompanied by a decrease in Hemoglobin and platelet count (PLT) respectively. Most importantly, there were several significant differences between severe patients and non-severe patients, including higher leukocyte (1.20-fold; 6.06 vs 5.07 × 109/l; P < 0.01) and neutrophil (1.33-fold; 4.94 vs 3.71 × 109/l; P < 0.01), lower lymphocyte (1.44-fold; 1.15 vs 0.80 × 109/l; P < 0.01), eosinophils (2.00-fold; 0.04 vs 0.02 × 109/l; P = 0.03), monocytes (1.08-fold; 0.38 vs 0.41 × 109/l; P = 0.041), PLT (1.15-fold; 212.58 vs 184.19 × 109/l; P = 0.987) and hemoglobin (1.53-fold; 131.36vs 126.73 × 109 g/l; P = 0.163).
|
|
sec
|
3.2.2 Inflammatory biomarkers examination
Increased C-reactive protein (CRP) concentration appeared in 57.40% (1494/2603) patients, procalcitonin (PCT) increased in 12.20% (256/2099) patients, and 39.26% (117/298) patients had an increase in erythrocyte sedimentation rate (ESR). Moreover, higher levels of CRP (3.04-fold; 60.91 vs 19.83 mg/l; P < 0.01), PCT (2.00-fold; 0.14vs 0.07 ng/ml; P < 0.01) and ESR (1.44-fold; 40.54 vs 28.16 mm/60 min; P = 0.096) were observed in severe patients in comparison with non-severe patients.
|
|
label
|
3.2.2
|
|
title
|
Inflammatory biomarkers examination
|
|
p
|
Increased C-reactive protein (CRP) concentration appeared in 57.40% (1494/2603) patients, procalcitonin (PCT) increased in 12.20% (256/2099) patients, and 39.26% (117/298) patients had an increase in erythrocyte sedimentation rate (ESR). Moreover, higher levels of CRP (3.04-fold; 60.91 vs 19.83 mg/l; P < 0.01), PCT (2.00-fold; 0.14vs 0.07 ng/ml; P < 0.01) and ESR (1.44-fold; 40.54 vs 28.16 mm/60 min; P = 0.096) were observed in severe patients in comparison with non-severe patients.
|
|
sec
|
3.2.3 Blood biochemical examination
3.2.3.1 Cardiac markers examination
Our statistics showed that the related indexes of myocardial injury increased in different numbers of patients with COVID-19. (respectively creatine kinase (CK) (7.74% (157/2029)); aspartate aminotransferase (AST) (14.87% (388/2609)); lactate dehydrogenase (LDH) (24.50% (468/1910)). Several significant differences were noted between severe and non-severe patients, especially higher values of AST (1.40-fold; 36.78 vs 26.24 U/l; P < 0.01), LDH (1.54-fold; 344.48 vs 224.20 U/l; P < 0.01), CK (1.44-fold; 111.92 vs 77.69 U/l; P < 0.01) and CK-MB (1.39-fold; 12.26 vs 8.76 U/l; P = 0.317).
3.2.3.2 Liver function
The increase of alanine aminotransferase (ALT) (12.27% (296/2412)) and AST (14.87% (388/2609)) with COVID-19 has been observed. Moreover, the decrease of albumin (143/221 (64.70%)) was more common while the increase of total bilirubin (TBIL) was relatively rare in the majority of patients (109/1558 (6.70%)). Comparing with non-severe patients, higher ALT (1.34-fold; 33.78 vs 24.85 U/l; P < 0.01), AST (1.40-fold; 36.78 vs 26.24 U/l; P < 0.01), TBIL (1.14-fold; 11.86 vs 10.38 U/l; P = 0.024) and lower albumin (1.15-fold; 39.41vs 34.29 g/l; P < 0.01) of severe patients has been worked out.
3.2.3.3 Renal function
The increase of creatinine (2.41% (40/1659)) and urea (13.50% (47/348)) were observed among the included patients with COVID-19. Besides, albumin reduction (64.70% (143/221)) was very common. More importantly, higher levels of creatinine (1.09-fold;72.94 vs 66.97 μmol; P < 0.01), urea (1.28-fold; 5.59 vs 4.36 mmol; P < 0.01) and lower concentrations of albumin (1.15-fold; 39.41 vs 34.29 g/l; P < 0.01) of severe patients were summed up in comparison with non-severe patients.
|
|
label
|
3.2.3
|
|
title
|
Blood biochemical examination
|
|
sec
|
3.2.3.1 Cardiac markers examination
Our statistics showed that the related indexes of myocardial injury increased in different numbers of patients with COVID-19. (respectively creatine kinase (CK) (7.74% (157/2029)); aspartate aminotransferase (AST) (14.87% (388/2609)); lactate dehydrogenase (LDH) (24.50% (468/1910)). Several significant differences were noted between severe and non-severe patients, especially higher values of AST (1.40-fold; 36.78 vs 26.24 U/l; P < 0.01), LDH (1.54-fold; 344.48 vs 224.20 U/l; P < 0.01), CK (1.44-fold; 111.92 vs 77.69 U/l; P < 0.01) and CK-MB (1.39-fold; 12.26 vs 8.76 U/l; P = 0.317).
|
|
label
|
3.2.3.1
|
|
title
|
Cardiac markers examination
|
|
p
|
Our statistics showed that the related indexes of myocardial injury increased in different numbers of patients with COVID-19. (respectively creatine kinase (CK) (7.74% (157/2029)); aspartate aminotransferase (AST) (14.87% (388/2609)); lactate dehydrogenase (LDH) (24.50% (468/1910)). Several significant differences were noted between severe and non-severe patients, especially higher values of AST (1.40-fold; 36.78 vs 26.24 U/l; P < 0.01), LDH (1.54-fold; 344.48 vs 224.20 U/l; P < 0.01), CK (1.44-fold; 111.92 vs 77.69 U/l; P < 0.01) and CK-MB (1.39-fold; 12.26 vs 8.76 U/l; P = 0.317).
|
|
sec
|
3.2.3.2 Liver function
The increase of alanine aminotransferase (ALT) (12.27% (296/2412)) and AST (14.87% (388/2609)) with COVID-19 has been observed. Moreover, the decrease of albumin (143/221 (64.70%)) was more common while the increase of total bilirubin (TBIL) was relatively rare in the majority of patients (109/1558 (6.70%)). Comparing with non-severe patients, higher ALT (1.34-fold; 33.78 vs 24.85 U/l; P < 0.01), AST (1.40-fold; 36.78 vs 26.24 U/l; P < 0.01), TBIL (1.14-fold; 11.86 vs 10.38 U/l; P = 0.024) and lower albumin (1.15-fold; 39.41vs 34.29 g/l; P < 0.01) of severe patients has been worked out.
|
|
label
|
3.2.3.2
|
|
title
|
Liver function
|
|
p
|
The increase of alanine aminotransferase (ALT) (12.27% (296/2412)) and AST (14.87% (388/2609)) with COVID-19 has been observed. Moreover, the decrease of albumin (143/221 (64.70%)) was more common while the increase of total bilirubin (TBIL) was relatively rare in the majority of patients (109/1558 (6.70%)). Comparing with non-severe patients, higher ALT (1.34-fold; 33.78 vs 24.85 U/l; P < 0.01), AST (1.40-fold; 36.78 vs 26.24 U/l; P < 0.01), TBIL (1.14-fold; 11.86 vs 10.38 U/l; P = 0.024) and lower albumin (1.15-fold; 39.41vs 34.29 g/l; P < 0.01) of severe patients has been worked out.
|
|
sec
|
3.2.3.3 Renal function
The increase of creatinine (2.41% (40/1659)) and urea (13.50% (47/348)) were observed among the included patients with COVID-19. Besides, albumin reduction (64.70% (143/221)) was very common. More importantly, higher levels of creatinine (1.09-fold;72.94 vs 66.97 μmol; P < 0.01), urea (1.28-fold; 5.59 vs 4.36 mmol; P < 0.01) and lower concentrations of albumin (1.15-fold; 39.41 vs 34.29 g/l; P < 0.01) of severe patients were summed up in comparison with non-severe patients.
|
|
label
|
3.2.3.3
|
|
title
|
Renal function
|
|
p
|
The increase of creatinine (2.41% (40/1659)) and urea (13.50% (47/348)) were observed among the included patients with COVID-19. Besides, albumin reduction (64.70% (143/221)) was very common. More importantly, higher levels of creatinine (1.09-fold;72.94 vs 66.97 μmol; P < 0.01), urea (1.28-fold; 5.59 vs 4.36 mmol; P < 0.01) and lower concentrations of albumin (1.15-fold; 39.41 vs 34.29 g/l; P < 0.01) of severe patients were summed up in comparison with non-severe patients.
|
|
sec
|
3.2.4 Blood coagulation function
Prothrombin time (PT) prolonged in 22.65% (53/234) patients and shortened in 10.68% (25/234) patients while activated partial thromboplastin time (APTT) prolonged in 21.79% (51/234) patients and shortened in 5.56% (13/234) patients. D dimer increased in 28.94% (534/1845) patients. Abnormal coagulation function is more obvious in severe patients, including shorter APTT (1.02-fold;33.49 vs 32.92 s; P = 0.804), increased D-dimer (2.74-fold; 1.29 vs 0.47 mg/l; P < 0.01) and longer PT (1.03-fold; 12.80 vs 12.45 s; P = 0.407).
|
|
label
|
3.2.4
|
|
title
|
Blood coagulation function
|
|
p
|
Prothrombin time (PT) prolonged in 22.65% (53/234) patients and shortened in 10.68% (25/234) patients while activated partial thromboplastin time (APTT) prolonged in 21.79% (51/234) patients and shortened in 5.56% (13/234) patients. D dimer increased in 28.94% (534/1845) patients. Abnormal coagulation function is more obvious in severe patients, including shorter APTT (1.02-fold;33.49 vs 32.92 s; P = 0.804), increased D-dimer (2.74-fold; 1.29 vs 0.47 mg/l; P < 0.01) and longer PT (1.03-fold; 12.80 vs 12.45 s; P = 0.407).
|
|
sec
|
3.2.5 Immunological examination
3.2.5.1 Antibody detection
The values of antibodies and complements in blood serum in Qin's [13] study showed that immunoglobulins (IgA, IgG and IgM) and complement proteins (C3 and C4) of COVID-19 patients are within the normal range. Compared with the non-severe group, the IgM of the severe group was only slightly decreased, and there was no significant difference in other immunoglobulins and complement, which was consistent with the results of Feng's study [14].
3.2.5.2 Lymphocyte subsets
The total number of B cells, T cells and NK cells significantly decreased in patients with COVID-19 (852.9 /uL), and more evident in the severe cases (1.37-fold; 743.6 vs 1020.1 /uL; P = 0.032) compared to the non-severe group [13]. Lower levels of CD4 T cells (2.10-fold; 561.81 vs 266.79 cell/μl; P < 0.01), CD8 T cells (2.00-fold; 349.01 vs 174.61cell/μl; P < 0.01) were summarized in severe patients comparing with non-severe patients from 5 articles [14], [15], [16], [17], [18]. In addition, lower CD3 T cells (1.70-fold; 1070.23 vs 628.20 cell/μl; P < 0.01) in severe patients was noted in Liu et al. [18].
3.2.5.3 Cytokine
Series of inflammatory cytokines were also increaseted in severe cases than the non-severe ones, including interleukin (IL)-1β (1.02-fold; 5.11 vs 5.01 pg/ml; P = 0.098), IL-6 (1.93-fold; 25.58 vs 13.22 pg/ml; P = 0.043), IL-10 (1.55-fold; 8.87 vs 5.71 pg/ml; P < 0.01). In addition, Qin et al. found higher levels of IL-2R (1.14-fold; 757.0 vs 663.5 U/ml; P < 0.01), IL-8 (1.34-fold; 18.4 vs 13.7 pg/ml; P < 0.01) and TNF-α (1.04-fold; 8.7 vs 8.4 pg/ml; P = 0.037) in severe patients in comparison with non-severe patients [13]. Studies also reported that GSCF, IP-10, MCP1and MIP1A in severe patients were higher [10].
|
|
label
|
3.2.5
|
|
title
|
Immunological examination
|
|
sec
|
3.2.5.1 Antibody detection
The values of antibodies and complements in blood serum in Qin's [13] study showed that immunoglobulins (IgA, IgG and IgM) and complement proteins (C3 and C4) of COVID-19 patients are within the normal range. Compared with the non-severe group, the IgM of the severe group was only slightly decreased, and there was no significant difference in other immunoglobulins and complement, which was consistent with the results of Feng's study [14].
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3.2.5.1
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Antibody detection
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The values of antibodies and complements in blood serum in Qin's [13] study showed that immunoglobulins (IgA, IgG and IgM) and complement proteins (C3 and C4) of COVID-19 patients are within the normal range. Compared with the non-severe group, the IgM of the severe group was only slightly decreased, and there was no significant difference in other immunoglobulins and complement, which was consistent with the results of Feng's study [14].
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3.2.5.2 Lymphocyte subsets
The total number of B cells, T cells and NK cells significantly decreased in patients with COVID-19 (852.9 /uL), and more evident in the severe cases (1.37-fold; 743.6 vs 1020.1 /uL; P = 0.032) compared to the non-severe group [13]. Lower levels of CD4 T cells (2.10-fold; 561.81 vs 266.79 cell/μl; P < 0.01), CD8 T cells (2.00-fold; 349.01 vs 174.61cell/μl; P < 0.01) were summarized in severe patients comparing with non-severe patients from 5 articles [14], [15], [16], [17], [18]. In addition, lower CD3 T cells (1.70-fold; 1070.23 vs 628.20 cell/μl; P < 0.01) in severe patients was noted in Liu et al. [18].
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3.2.5.2
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Lymphocyte subsets
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The total number of B cells, T cells and NK cells significantly decreased in patients with COVID-19 (852.9 /uL), and more evident in the severe cases (1.37-fold; 743.6 vs 1020.1 /uL; P = 0.032) compared to the non-severe group [13]. Lower levels of CD4 T cells (2.10-fold; 561.81 vs 266.79 cell/μl; P < 0.01), CD8 T cells (2.00-fold; 349.01 vs 174.61cell/μl; P < 0.01) were summarized in severe patients comparing with non-severe patients from 5 articles [14], [15], [16], [17], [18]. In addition, lower CD3 T cells (1.70-fold; 1070.23 vs 628.20 cell/μl; P < 0.01) in severe patients was noted in Liu et al. [18].
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3.2.5.3 Cytokine
Series of inflammatory cytokines were also increaseted in severe cases than the non-severe ones, including interleukin (IL)-1β (1.02-fold; 5.11 vs 5.01 pg/ml; P = 0.098), IL-6 (1.93-fold; 25.58 vs 13.22 pg/ml; P = 0.043), IL-10 (1.55-fold; 8.87 vs 5.71 pg/ml; P < 0.01). In addition, Qin et al. found higher levels of IL-2R (1.14-fold; 757.0 vs 663.5 U/ml; P < 0.01), IL-8 (1.34-fold; 18.4 vs 13.7 pg/ml; P < 0.01) and TNF-α (1.04-fold; 8.7 vs 8.4 pg/ml; P = 0.037) in severe patients in comparison with non-severe patients [13]. Studies also reported that GSCF, IP-10, MCP1and MIP1A in severe patients were higher [10].
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3.2.5.3
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Cytokine
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Series of inflammatory cytokines were also increaseted in severe cases than the non-severe ones, including interleukin (IL)-1β (1.02-fold; 5.11 vs 5.01 pg/ml; P = 0.098), IL-6 (1.93-fold; 25.58 vs 13.22 pg/ml; P = 0.043), IL-10 (1.55-fold; 8.87 vs 5.71 pg/ml; P < 0.01). In addition, Qin et al. found higher levels of IL-2R (1.14-fold; 757.0 vs 663.5 U/ml; P < 0.01), IL-8 (1.34-fold; 18.4 vs 13.7 pg/ml; P < 0.01) and TNF-α (1.04-fold; 8.7 vs 8.4 pg/ml; P = 0.037) in severe patients in comparison with non-severe patients [13]. Studies also reported that GSCF, IP-10, MCP1and MIP1A in severe patients were higher [10].
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4 Quality assessment
Judging by the evaluation score, all of the included articles were classified as high quality and there was no considerable publication bias ( Table 2 ). The quality assessment graph and the reporting bias of important laboratory indicators are exhibited in Supplementary materials.
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4
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Quality assessment
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Judging by the evaluation score, all of the included articles were classified as high quality and there was no considerable publication bias ( Table 2 ). The quality assessment graph and the reporting bias of important laboratory indicators are exhibited in Supplementary materials.
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5 Reports on laboratory indicators of COVID-19 patients worldwide except China
As the pandemic spreads to other countries and viral gene mutations, the characteristics of laboratory indicators of COVID-19 patients worldwide need to be grasped. Since only China has made a clear classification of the severity of patients with COVID-19, the definition of severe patients in countries expect China is simply summarized as staying in ICU. We decided to analyze the laboratory data of patients in representative articles about foreign countries to clarify the differences between foreign patients and Chinese patients with COVID-19.
In 21 critically ill patients with COVID-19 in Washington State, 67% and 38% of these patients had lymphopenia and abnormalities of liver function tests at admission respectively [19]. Higher concentrations of IL-6 and D-dimer at admission were independently associated with in-hospital mortality, which has been confirmed in 1150 patients in New York [20]. On a cohort of 300 COVID-19 patients from Italy, patients demonstrated lymphopenia in many cases [21]. In another group of Italian cases, the frequency of granulocyte morphological anomalies has been highlighted, especially in patients with severe ARDS at admission [22]. The values of leukocyte, IL-6, LDH, CK and D-dimer continued to increase in 50 COVID-19 patients with ARDS during hospitalization in a German report [23]. About Singapore, lymphopenia was present in 39% patients (7/16) and an elevated CRP in 38% patients (6/16), while kidney function remained normal [24]. Lower blood counts of leukocytes, platelets, neutrophils, lymphocytes, eosinophils, and basophils (all P < 0.001) in COVID-19 patients were significant predictors of SARS-CoV-2 positive test [25]. In addition, compared with less severe diseases, CRP is higher and the lymphocyte count is lower has been found in a study in Norway [26].
Overall, we found that foreign patients have similarities to the changes in laboratory indicators of Chinese patients, typically including a decrease in leukocytes and lymphocytes and an increase in inflammation-related factors. The abnormality of blood coagulation, liver and kidney function and immune function also appeared in foreign patients, especially in severe and critically ill patients. However, due to differences in viral gene variation and detection time, the diversity in the laboratory indicators of patients around the world is inevitable, and more data required to confirm.
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5
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Reports on laboratory indicators of COVID-19 patients worldwide except China
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As the pandemic spreads to other countries and viral gene mutations, the characteristics of laboratory indicators of COVID-19 patients worldwide need to be grasped. Since only China has made a clear classification of the severity of patients with COVID-19, the definition of severe patients in countries expect China is simply summarized as staying in ICU. We decided to analyze the laboratory data of patients in representative articles about foreign countries to clarify the differences between foreign patients and Chinese patients with COVID-19.
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p
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In 21 critically ill patients with COVID-19 in Washington State, 67% and 38% of these patients had lymphopenia and abnormalities of liver function tests at admission respectively [19]. Higher concentrations of IL-6 and D-dimer at admission were independently associated with in-hospital mortality, which has been confirmed in 1150 patients in New York [20]. On a cohort of 300 COVID-19 patients from Italy, patients demonstrated lymphopenia in many cases [21]. In another group of Italian cases, the frequency of granulocyte morphological anomalies has been highlighted, especially in patients with severe ARDS at admission [22]. The values of leukocyte, IL-6, LDH, CK and D-dimer continued to increase in 50 COVID-19 patients with ARDS during hospitalization in a German report [23]. About Singapore, lymphopenia was present in 39% patients (7/16) and an elevated CRP in 38% patients (6/16), while kidney function remained normal [24]. Lower blood counts of leukocytes, platelets, neutrophils, lymphocytes, eosinophils, and basophils (all P < 0.001) in COVID-19 patients were significant predictors of SARS-CoV-2 positive test [25]. In addition, compared with less severe diseases, CRP is higher and the lymphocyte count is lower has been found in a study in Norway [26].
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Overall, we found that foreign patients have similarities to the changes in laboratory indicators of Chinese patients, typically including a decrease in leukocytes and lymphocytes and an increase in inflammation-related factors. The abnormality of blood coagulation, liver and kidney function and immune function also appeared in foreign patients, especially in severe and critically ill patients. However, due to differences in viral gene variation and detection time, the diversity in the laboratory indicators of patients around the world is inevitable, and more data required to confirm.
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6 Discussion
Facing the huge threat of COVID--19 to human health, laboratory evaluation and early prediction of patients' condition should be paid to more attention. At present, the characteristics of laboratory examination results of hospitalized patients were reported, but the discrepancies were observed between these reports due to the different proportions of severe patients in each study.
Among 5912 patients who underwent laboratory examinations on admission, lymphopenia was typical, which might be risk factors for disease progression of COVID-19 [27]. The PLT-to-lymphocyte ratio (PLR) and the neutrophil/lymphocyte ratio (NLR) may provide new indexes for the monitoring the changes of patients with COVID-19 [28], [29], [30]. The NLR was > 5 in severe patients critically ill patients, proving that severe patients are more likely to develop leukocytosis and lymphocytopenia [29]. Neutrophils and eosinophils may be used to predict the recovery probability [31], [32]. The decrease of hemoglobin and PLT were significantly associated with the severity of the disease [33], [34]. In addition, the combined parameter NLR&RDW-SD can help clinically to predict the severity of COVID-19 patients [35]. In conclusion, blood routine examination is of great value in the diagnosis and prognosis of COVID-19.
High infection-related biomarkers (i.e. PCT, ESR, and CRP) have been observed in our study. CRP is a good predictor of adverse consequences and related to inflammation of tissues and organs [36], [37], [38]. A simple death risk index (ACP) consisting of age and CRP was developed by Lu et al [39], by which the short-term mortality associated with COVID-19 can be predicted. Higher serum hypersensitive C-reactive protein (hs-CRP) is an important marker of poor prognosis in COVID-19 patients and can be used to predict the risk of death in severe patients, which reflects the persistent state of inflammation [40]. Increased PCT, SAA and ESR were identified as powerful factors to predict disease progression of patients with COVID-19 [41], [42], [43], [44]. In addition, the combined detection of IL-6, ESR and CRP improve the efficiency of predicting the development of patients' condition [45]. On account of the common co-infection in children, the increase of PCT is more obvious than that in adults, so it should be used as an important index for the detection of children [46]. Thus, infection-related biomarkers are risk factors for disease progression.
In term of biochemical indicators, patients with organ dysfunction, (including ARDS, acute renal injury, heart injury, liver dysfunction, pneumothorax, etc.) are prone to exhibit abnormal results of blood biochemical examination [47]. Increased serum N-terminal proB-type natriuretic peptide (NT-proBNP), cardiac troponin-I (cTnI), myoglobin and creatinine were related factors of critical COVID-19 with heart damage [48], [49]. Cardiac injury defined by the increase of hs-cTnI and D-dimer on admission and patients with high BNP is associated with a higher risk of mortality [50], [51], [52]. LDH, AST/ALT ratio, TBIL could be identified as powerful predictive factors for early recognition of liver injury and were positively correlated with death risk of COVID-19 patients [53], [54], [55]. Albumin, serum urea nitrogen and creatine were risk factor s for assessing kidney damage and disease progression [55], [56]. Many patients have abnormal urine analysis on admission, including proteinuria or hematuria, which indicates that urine analysis can better reveal the potential kidney damage of COVID-19 patients to reflect and predict the severity of the disease [57], [58]. In short, the cardiac biomarkers, liver and kidney function examination for severe and critically ill patients can evaluate the degree of extrapulmonary damage caused by complications.
Furthermore, the level of lactic acid, plasma angiotensin II, amylase and lipase can also be used as indicators to estimate the course of the disease [49], [59]. Plasma angiotensin II level linearly correlated with virus titer and the degree of lung injury was increased in one study [49]. Other than the high expression of angiotensin-converting enzyme 2 (ACE2) in the pancreatic tissue of COVID-19 patients, the increase of serum amylase and lipase were found [60]. In addition, the detection of electrolyte and blood glucose indexes is of great significance for patients with underlying diseases of electrolyte balance disorder and glucose metabolism disorder.
The changes in blood coagulation, especially disseminated intravascular coagulation (DIC), which is common in critical diseases, should also been paid enough attention [61], [62], [63], [64]. Severe patients may exhibit blood coagulation disorders, including increased D-dimer, prolonged PT and shortened APTT, which is consistent with reports [62], [63]. D-dimer is associated with the severity of COVID-19 [65]. Fibrinogen can be significantly increased in the early stages of severe patients, but notably decreased in the later stages, this may be the reason why serious people are more likely to suffer from cerebrovascular disease [66], [67]. Bleeding and coagulation dysfunction and even DIC combined with COVID-19 is a process of dynamic change. Monitoring the blood coagulation function of patients is beneficial to the early diagnosis, prevention and treatment of the disease. In addition, The combined detection of IL-6 and D-dimer had important clinical value for early prediction of the severity of COVID-19 patients due to its high sensitivity and specificity [68].
Our analysis showed that lower levels of CD4 and CD8 and higher levels of inflammatory cytokines (IL-1β, IL-6, IL-10) in severe patients, which made important impacts in predicting the state of the illness changes from mild to severe. The decrease of CD4 and CD8 in peripheral blood and the increase of IL-6 are the high-risk factors of cytokine release syndrome-like (CRSL) [59], [69], [70]. CD3 + T cells, IP-10, MCP-3 and IL-1ra were also closely related to the severity and progression of COVID-19 [54], [71]. Diao et al. [69] found that the number of T cells was negatively correlated with the concentration of serum IL-6, IL-10 and TNF-α. In addition, the immune response phenotype based on late IgG response can be used as a simple complementary tool to distinguish between severe and non-severe COVID-19 patients and to further predict their clinical outcomes [72]. Overall, close monitoring of the T lymphocyte subsets and cytokines might provide valuable information on the patient’s condition change during the treatment process [73].
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label
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6
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title
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Discussion
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p
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Facing the huge threat of COVID--19 to human health, laboratory evaluation and early prediction of patients' condition should be paid to more attention. At present, the characteristics of laboratory examination results of hospitalized patients were reported, but the discrepancies were observed between these reports due to the different proportions of severe patients in each study.
|
|
p
|
Among 5912 patients who underwent laboratory examinations on admission, lymphopenia was typical, which might be risk factors for disease progression of COVID-19 [27]. The PLT-to-lymphocyte ratio (PLR) and the neutrophil/lymphocyte ratio (NLR) may provide new indexes for the monitoring the changes of patients with COVID-19 [28], [29], [30]. The NLR was > 5 in severe patients critically ill patients, proving that severe patients are more likely to develop leukocytosis and lymphocytopenia [29]. Neutrophils and eosinophils may be used to predict the recovery probability [31], [32]. The decrease of hemoglobin and PLT were significantly associated with the severity of the disease [33], [34]. In addition, the combined parameter NLR&RDW-SD can help clinically to predict the severity of COVID-19 patients [35]. In conclusion, blood routine examination is of great value in the diagnosis and prognosis of COVID-19.
|
|
p
|
High infection-related biomarkers (i.e. PCT, ESR, and CRP) have been observed in our study. CRP is a good predictor of adverse consequences and related to inflammation of tissues and organs [36], [37], [38]. A simple death risk index (ACP) consisting of age and CRP was developed by Lu et al [39], by which the short-term mortality associated with COVID-19 can be predicted. Higher serum hypersensitive C-reactive protein (hs-CRP) is an important marker of poor prognosis in COVID-19 patients and can be used to predict the risk of death in severe patients, which reflects the persistent state of inflammation [40]. Increased PCT, SAA and ESR were identified as powerful factors to predict disease progression of patients with COVID-19 [41], [42], [43], [44]. In addition, the combined detection of IL-6, ESR and CRP improve the efficiency of predicting the development of patients' condition [45]. On account of the common co-infection in children, the increase of PCT is more obvious than that in adults, so it should be used as an important index for the detection of children [46]. Thus, infection-related biomarkers are risk factors for disease progression.
|
|
p
|
In term of biochemical indicators, patients with organ dysfunction, (including ARDS, acute renal injury, heart injury, liver dysfunction, pneumothorax, etc.) are prone to exhibit abnormal results of blood biochemical examination [47]. Increased serum N-terminal proB-type natriuretic peptide (NT-proBNP), cardiac troponin-I (cTnI), myoglobin and creatinine were related factors of critical COVID-19 with heart damage [48], [49]. Cardiac injury defined by the increase of hs-cTnI and D-dimer on admission and patients with high BNP is associated with a higher risk of mortality [50], [51], [52]. LDH, AST/ALT ratio, TBIL could be identified as powerful predictive factors for early recognition of liver injury and were positively correlated with death risk of COVID-19 patients [53], [54], [55]. Albumin, serum urea nitrogen and creatine were risk factor s for assessing kidney damage and disease progression [55], [56]. Many patients have abnormal urine analysis on admission, including proteinuria or hematuria, which indicates that urine analysis can better reveal the potential kidney damage of COVID-19 patients to reflect and predict the severity of the disease [57], [58]. In short, the cardiac biomarkers, liver and kidney function examination for severe and critically ill patients can evaluate the degree of extrapulmonary damage caused by complications.
|
|
p
|
Furthermore, the level of lactic acid, plasma angiotensin II, amylase and lipase can also be used as indicators to estimate the course of the disease [49], [59]. Plasma angiotensin II level linearly correlated with virus titer and the degree of lung injury was increased in one study [49]. Other than the high expression of angiotensin-converting enzyme 2 (ACE2) in the pancreatic tissue of COVID-19 patients, the increase of serum amylase and lipase were found [60]. In addition, the detection of electrolyte and blood glucose indexes is of great significance for patients with underlying diseases of electrolyte balance disorder and glucose metabolism disorder.
|
|
p
|
The changes in blood coagulation, especially disseminated intravascular coagulation (DIC), which is common in critical diseases, should also been paid enough attention [61], [62], [63], [64]. Severe patients may exhibit blood coagulation disorders, including increased D-dimer, prolonged PT and shortened APTT, which is consistent with reports [62], [63]. D-dimer is associated with the severity of COVID-19 [65]. Fibrinogen can be significantly increased in the early stages of severe patients, but notably decreased in the later stages, this may be the reason why serious people are more likely to suffer from cerebrovascular disease [66], [67]. Bleeding and coagulation dysfunction and even DIC combined with COVID-19 is a process of dynamic change. Monitoring the blood coagulation function of patients is beneficial to the early diagnosis, prevention and treatment of the disease. In addition, The combined detection of IL-6 and D-dimer had important clinical value for early prediction of the severity of COVID-19 patients due to its high sensitivity and specificity [68].
|
|
p
|
Our analysis showed that lower levels of CD4 and CD8 and higher levels of inflammatory cytokines (IL-1β, IL-6, IL-10) in severe patients, which made important impacts in predicting the state of the illness changes from mild to severe. The decrease of CD4 and CD8 in peripheral blood and the increase of IL-6 are the high-risk factors of cytokine release syndrome-like (CRSL) [59], [69], [70]. CD3 + T cells, IP-10, MCP-3 and IL-1ra were also closely related to the severity and progression of COVID-19 [54], [71]. Diao et al. [69] found that the number of T cells was negatively correlated with the concentration of serum IL-6, IL-10 and TNF-α. In addition, the immune response phenotype based on late IgG response can be used as a simple complementary tool to distinguish between severe and non-severe COVID-19 patients and to further predict their clinical outcomes [72]. Overall, close monitoring of the T lymphocyte subsets and cytokines might provide valuable information on the patient’s condition change during the treatment process [73].
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7 Limitations
Although our analysis showed the characteristics of laboratory findings of COVID-19 patients, relatively few patients were included in the analysis. In addition, the recruited participants in our study were hospitalized before April 27, 2020 and more laboratory tests of COVID-19 patients should be investigated.
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Limitations
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Although our analysis showed the characteristics of laboratory findings of COVID-19 patients, relatively few patients were included in the analysis. In addition, the recruited participants in our study were hospitalized before April 27, 2020 and more laboratory tests of COVID-19 patients should be investigated.
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8 Conclusion
Some certain laboratory inspections could predict the progress of the COVID-19 changes, especially, lymphocytes, CRP, PCT, ALT, AST, LDH, D-dimer, CD4 T cells and IL6, which provide valuable signals for preventing the deterioration of the disease.
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Conclusion
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Some certain laboratory inspections could predict the progress of the COVID-19 changes, especially, lymphocytes, CRP, PCT, ALT, AST, LDH, D-dimer, CD4 T cells and IL6, which provide valuable signals for preventing the deterioration of the disease.
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CRediT authorship contribution statement
Jinfeng Bao: Conceptualization, Methodology, Software, Investigation, Writing - original draft. Chenxi Li: Validation, Formal analysis, Visualization, Software. Kai Zhang: Validation, Formal analysis, Visualization. Haiquan Kang: Resources, Writing - review & editing, Supervision, Data curation. Wensen Chen: Resources, Writing - review & editing, Supervision, Data curation. Bing Gu: .
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CRediT authorship contribution statement
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Jinfeng Bao: Conceptualization, Methodology, Software, Investigation, Writing - original draft. Chenxi Li: Validation, Formal analysis, Visualization, Software. Kai Zhang: Validation, Formal analysis, Visualization. Haiquan Kang: Resources, Writing - review & editing, Supervision, Data curation. Wensen Chen: Resources, Writing - review & editing, Supervision, Data curation. Bing Gu: .
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Appendix A Supplementary data
The following are the Supplementary data to this article:Supplementary data 1
Supplementary data 2
Supplementary data 3
Supplementary data 4
Supplementary data 5
Supplementary data 6
Supplementary data 7
Supplementary data 8
Supplementary data 9
Acknowledgement
This research was supported by Jiangsu Privincial Medical Talent (ZDRCA2016053), Six talent peaks project of Jiangsu Province (WSN-135), Advanced health talent of six-one project of Jiangsu Province (LGY2016042). Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.cca.2020.06.009.
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Appendix A Supplementary data
The following are the Supplementary data to this article:Supplementary data 1
Supplementary data 2
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Supplementary data 4
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Acknowledgement
This research was supported by Jiangsu Privincial Medical Talent (ZDRCA2016053), Six talent peaks project of Jiangsu Province (WSN-135), Advanced health talent of six-one project of Jiangsu Province (LGY2016042).
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Acknowledgement
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This research was supported by Jiangsu Privincial Medical Talent (ZDRCA2016053), Six talent peaks project of Jiangsu Province (WSN-135), Advanced health talent of six-one project of Jiangsu Province (LGY2016042).
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Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.cca.2020.06.009.
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Supplementary data to this article can be found online at https://doi.org/10.1016/j.cca.2020.06.009.
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