BioMed Research International

BioMed Research International / 2020 / Article

Review Article | Open Access

Volume 2020 |Article ID 3149020 | https://doi.org/10.1155/2020/3149020

Hamidreza Hasani, Shayan Mardi, Sareh Shakerian, Nooshin Taherzadeh-Ghahfarokhi, Parham Mardi, "The Novel Coronavirus Disease (COVID-19): A PRISMA Systematic Review and Meta-Analysis of Clinical and Paraclinical Characteristics", BioMed Research International, vol. 2020, Article ID 3149020, 16 pages, 2020. https://doi.org/10.1155/2020/3149020

The Novel Coronavirus Disease (COVID-19): A PRISMA Systematic Review and Meta-Analysis of Clinical and Paraclinical Characteristics

Academic Editor: Valeria Cavalcanti Rolla
Received07 Apr 2020
Revised18 Jun 2020
Accepted18 Jul 2020
Published17 Aug 2020

Abstract

An outbreak of pneumonia, caused by a novel coronavirus (SARS-CoV-2), was identified in China in December 2019. This virus expanded worldwide, causing global concern. Although clinical, laboratory, and imaging features of COVID-19 are characterized in some observational studies, we undertook a systematic review and meta-analysis to assess the frequency of these features. We did a systematic review and meta-analysis using three databases to identify clinical, laboratory, and computerized tomography (CT) scanning features of rRT-PCR confirmed cases of COVID-19. Data for 3420 patients from 30 observational studies were included. Overall, the results showed that fever (84.2%, 95% CI 82.6-85.7), cough (62%, 95% CI 60-64), and fatigue (39.4%, 95% CI 37.2-41.6%) are the most prevalent symptoms in COVID-19 patients. Increased CRP level, decreased lymphocyte count, and increased D-dimer level were the most common laboratory findings. Among COVID-19 patients, 92% had a positive CT finding, most prevalently ground-glass opacification (GGO) (60%, 95% CI 58-62) and peripheral distribution opacification (64%, 95% CI 60-69). These results demonstrate the clinical, paraclinical, and imaging features of COVID-19.

1. Background

In December 2019, the first case of unknown origin pneumonia was identified in Wuhan, the capital city of Hubei Province. By January 7, 2020, Chinese scientists had isolated a novel virus belongs to coronaviruses family and classified it as a type of RNA virus [1].

Although the outbreak has been started from a primary zoonotic virus transmission in a large seafood market (Huanan Seafood Market), person-to-person transmission of the virus started a pandemic involving 197 countries [2, 3].

The clinical outcomes of SARS-CoV-2 infection are various, including asymptomatic infection, mild upper respiratory tract illness, severe viral pneumonia, and even death. Patients are presented with various clinical manifestations such as fever, dyspnea, and cough [4].

Initially, some studies have observed particular imaging patterns on chest radiography and computed tomography in COVID-19 patients [5]. As our knowledge increased, recent studies claimed that the sensitivity of CT scan is higher compared to rRT-PCR in the diagnosis of COVID-19 [6].

Laboratory findings are essential in order to evaluate patients’ complications and triaging them [7]. Complete blood count as an easy and affordable test detects disorders such as leukopenia, anemia, and thrombocytopenia that are contributed to patients’ prognosis [8]. In response to inflammation induced by COVID-19, acute-phase reactants may increase or decrease [9]. These factors may contribute to the patients’ outcomes. This meta-analysis is aimed at measuring the most common clinical, laboratory, and imaging findings among COVID-19 patients.

2. Methods

2.1. Protocol

In this study, we used a protocol based on the transparent reporting of systematic reviews and meta-analysis (PRISMA) (Figures 13).

2.2. Eligibility Criteria

In this study, all included patients were confirmed using real-time reverse transcriptase-polymerase chain reaction (rRT-PCR). All searched articles were cross-sectional studies, reporting descriptive data, and no language restrictions were conducted. All articles published before drafting the manuscript have been included. Review articles, opinion articles, and letters not presenting original data were excluded from the analysis.

2.3. Information Sources and Search Strategy

Three systematic searches were performed using Medline/PubMed, Scopus, and Web of Science. Systematic search was conducted prior to March 25, 2020, and three independent researchers evaluated all papers. The search was conducted based on the following keywords, and all studies were divided in three groups: (1) For clinical characteristics group: (“clinical manifestation” AND COVID-19) or (“clinical manifestation” AND 2019-nCoV) or (“clinical manifestation” AND COVID) or (“clinical manifestation” AND Corona) or (“clinical characteristics” AND COVID-19) or (“clinical characteristics” AND 2019-nCoV) or (“clinical characteristics” AND COVID) or (“clinical characteristics” AND corona). (2) For laboratory findings group: (liver AND COVID-19) or (liver AND 2019-nCoV) or (liver AND COVID) or (liver AND Corona) or (“blood gas” AND COVID-19) or (“blood gas” AND 2019-nCoV) or (“blood gas” AND COVID) or (“blood gas” AND corona). (3) For imaging studies group: (COVID-19 AND radiography) or (2019-nCoV AND radiography) or (Corona AND radiography) or (COVID AND radiography) or (COVID-19 AND radiographic) or (2019-nCoV AND radiographic) or (Corona AND radiographic) or (COVID AND radiographic) or (COVID-19 AND CT) or (2019-nCoV AND CT) or (Corona AND CT) or (COVID AND CT) or (COVID-19 AND “computed tomography”) or (2019-nCoV AND “computed tomography”) or (Corona AND “computed tomography”) or (COVID AND “computed tomography”) or (CBC AND corona) or (CBC AND COVID) or (CBC AND COVID-19) or (CBC AND 2019-nCoV).

2.4. Study Selection

In the initial search, we assessed the title and abstract, followed by a full-text evaluation based on previously described inclusion and exclusion criteria. When two articles reported one patient’s characteristics, we merged all reported data and assumed as a single individual. Descriptive studies reporting clinical symptoms, laboratory, and radiological findings were used to perform a meta-analysis. The characteristics of the included studies are shown in Table 1. The modified appraisal tool for cross-sectional studies (AXIS) was used to determine the methodological quality of the research designs of the included studies (Table 2). AXIS is used to assess research papers systematically and to judge the reliability of the study being presented in the paper. It also helps in assessing the worth and relevance of the study. Studies with total scores of ten or less were excluded.


RowAuthorJournalTypeDateCountrySample sizeReference

Imaging
1Fang et al.RadiologyCross-sectionalFeb 19China51[6]
2Zhao et al.American Journal of RoentgenologyCross-sectionalFeb 18China101[38]
3Shi et al.The LancetCross-sectionalFeb 24China81[40]
4Pan et al.European RadiologyCross-sectionalFeb 13China63[41]
5Xu et al.European Journal of Nuclear Medicine and Molecular ImagingCross-sectionalFeb 28China90[42]
6Zhang et al.European Respiratory JournalCross-sectionalMar 25China17[43]
7Chen et al.The LancetCross-sectionalJan 30China99[44]
8HuangThe LancetCross-sectionalFeb 21China41[18]
9Wu et al.Clinical Infectious DiseasesCross-sectionalFeb 29China80[19]
10Guan et al.The New England Journal of MedicineCross-sectionalFeb 28China1099[45]
11Wang et al.Clinical Infectious DiseasesCross-sectionalFeb 29China138[46]
12Yang et al.Journal of InfectionCross-sectionalFeb 26China149[47]
13Xu et al.Journal of InfectionCross-sectionalFeb 25China50[48]
14Wu et al.Investigative RadiologyCross-sectionalFeb 29China80[49]
15Li et al.Investigative RadiologyCross-sectionalFeb 29China83[50]
16Xia et al.Pediatric PulmonologyCross-sectionalMar 05China20[51]
17Zhang et al.AllergyCross-sectionalFeb 19China140[52]
18Zhou et al.American Journal of RoentgenologyCross-sectionalFeb 16China62[53]
19Wang et al.Journal of Zhejiang UniversityCross-sectionalFeb 24China52[54]
20Yoon et al.Korean J RadiolCross-sectionalApr 21China9[55]
Laboratory
21Qian et al.An International Journal of MedicineCross-sectionalFeb 21China91[56]
22Liu et al.medRxivCross-sectionalFeb 21China109[57]
23Chen et al.medRxivCross-sectionalFeb 14China21[58]
24Young et al.JamaCross-sectionalFeb 24China18[59]
25Fan et al.The LancetCross-sectionalMar 05China148[30]
26Wu et al.Clinical Infectious DiseasesCross-sectionalFeb 29China80[19]
27Guan et al.New England Journal of MedicineCross-sectionalFeb 28China1099[45]
Clinical characteristics
28Chen et al.The LancetCross-sectionalFeb 21China99[44]
29Deng and PengJournal Clinical MedicineCross-sectionalFeb 14China41[60]
30HuangClinical Gastroenterology and HepatologyCross-sectionalFeb 21China41[18]
31Guan et al.The New England Journal of MedicineCross-sectionalFeb 19China1099[45]
32Huang et al.Travel Medicine and Infectious DiseaseCross-sectionalFeb 24China34[61]
33Kui et al.Chinese Medical JournalCross-sectionalFeb 07China137[62]
34Tian et al.Journal of InfectionCross-sectionalFeb 27China262[63]
35Wang et al.JamaCross-sectionalFeb 21China138[46]
36Wu et al.Clinical Infectious DiseasesCross-sectionalFeb 29China80[19]
37Xu et al.European Journal of Nuclear Medicine and Molecular ImagingCross-sectionalFeb 28China90[42]
38Xiao-Wei et al.BMJ: British Medical JournalCross-sectionalFeb 19China62[64]
39Xu et al.Journal of InfectionCross-sectionalFeb 25China50[48]
40Yang et al.Journal of InfectionCross-sectionalFeb 26China149[47]
41Zhang et al.AllergyCross-sectionalFeb 19China140[52]


RowAuthorDateAXIS scoreReference
IntroductionMethodsResultsDiscussionOthersTotal

1Fang et al.Feb 191642215[6]
2Zhao et al.Feb 181832216[38]
3Shi et al.Feb 241932217[40]
4Pan et al.Feb 131722214[41]
5Xu et al.Feb 281642215[42]
6Zhang et al.Mar 251722214[43]
7Chen et al.Jan 301852218[44]
8HuangFeb 211852218[18]
9Wu et al.Feb 291622112[19]
10Guan et al.Feb 281952219[45]
11Wang et al.Feb 291952219[46]
12Yang et al.Feb 261532213[47]
13Xu et al.Feb 251642215[48]
14Wu et al.Feb 291932217[49]
15Li et al.Feb 291852218[50]
16Xia et al.Mar 051522212[51]
17Zhang et al.Feb 191621212[52]
18Zhou et al.Feb 161542214[53]
19Wang et al.Feb 241732215[54]
20Yoon et al.Apr 211432212[55]
21Qian et al.Feb 211742216[56]
22Liu et al.1642215[57]
23Chen et al.1522212[58]
24Young et al.Feb 241842217[59]
25Fan et al.Mar 051832216[30]
29Deng and PengFeb 141552215[60]
32Huang et al.Feb 241532213[61]
33Kui et al.Feb 071532213[62]
34Tian et al.Feb 271532213[63]
38Xiao-Wei et al.Feb 191722214[64]

2.5. Data Collection Process and Data Items

Three independent researchers filled data extraction forms containing study type, journal, publication date, sample size, age, gender, clinical characteristics, laboratory, and radiological findings. Conflicts were resolved by another researcher.

2.6. Assessment of Methodological Quality and Risk of Bias

Publication bias was assessed with a funnel plot for the standard error and considering that the interpretation of the plot is subjective (Figures 46). Also, bias was quantified by using the Egger regression test.

Sensitivity analysis and adjusting for risk bias were performed by the attractive test (trim and fill method). We initially identified and trimmed the asymmetric (missing) studies, followed by estimating the unbiased summary effect. Sensitivity analysis was also performed using the “Remove-One” analysis by running the analysis with each of the studies removed. The result of the impact of each study on the pooled estimate is shown in the forest plot (Figure 7).

The percentage of total variation across studies (heterogeneity) was measured by the inconsistency index tool ( squared). The squared index measured for each of the clinical characteristics, imaging studies, and laboratory findings groups. squared index value in the ranges of <25%, 25–50%, 50–75%, and >75% was interpreted as low, moderate, high, and very high heterogeneity, respectively [10].

We conducted a random-effects analysis because it was assumed that some of the included studies did not share a common effect size (heterogeneity). Findings in each group are summarized as forest plots in Figures 810.

2.7. Statistical Approach

Effect size pooled estimate for imaging and clinical data was measured based on event rate, logit event rate, and standard error.

Considering that the computational index of laboratory data was median in order to meta-analyze them in CMA v.2. software, we use the following formula:

Estimating the mean and variance from the median: where is the median, is the smallest value (minimum), is the largest value (maximum), and is the size of the sample [11].

The meta-analysis was performed using STATA, the software OpenMeta[Analyst], and Comprehensive Meta-Analysis Software (CMA) ve.2. Pooled estimate and their 95% confidence intervals (95% CIs) were used to summarize the weighted effect size for each study grouping variable.

3. Results

3.1. Study Selection and Characteristics

Two hundred seven articles were included based on a search strategy, which are previously described (Table 1). The full text of 65 articles was evaluated after the title and abstract assessment. Twenty-four articles were excluded due to inadequate data. Finally, the meta-analysis was performed on 30 articles (three different subjects). The article’s data summary is reported in Table 2. Also, demographic characteristics and comorbidities of patients participated in the included studies are demonstrated in Table 3.


RowAuthorDateSample sizeMean age (y. old)Age rangeSex (male)DiabetesHypertensionCardiovascular diseaseCOPDMalignanciesDigestive system disease

1Fang et al.Feb 19514539-5529
2Zhao et al.Feb 1810144/4417-755615/84/9
3Shi et al.Feb 248149/539-61421215101159
4Pan et al.Feb 136344/931-6233
5Xu et al.Feb 28905018-86396193122
6Zhang et al.Mar 251748/623-74811/751111/7
7Chen et al.Jan 309955/521-8267401111
8HuangFeb 21414941-5830201515222
9Wu et al.Feb 29804618-653931/251/251/253/75
10Guan et al.Feb 2810964935-586377/4153/91/10/92/1
11Wang et al.Feb 291385642-687510/131/214/52/97/22/9
12Yang et al.Feb 2614945/130-688118/790/671/345/37
13Xu et al.Feb 2550433-8529
14Wu et al.Feb 29804430-52425514
15Li et al.Feb 298345/525-64447/861/26
16Xia et al.Mar 052010-713
17Zhang et al.Feb 191355725-8771123012/12/810/7
18Zhou et al.Feb 166252/830-7739661
19Wang et al.Feb 2452
20Yoon et al.Apr 21954
21Qian et al.Feb 21915036-57378/7916/483/3
22Liu et al.1095543-665911336/43/7
23Chen et al.21561714/323/8
24Young et al.Feb 24184731-739
25Fan et al.Mar 051485036-6473
26Xu et al.Feb 19624132-5236282211
27Deng and PengFeb 144155/525-8942/353/819/219/2
28Huang et al.Feb 243456/2426-881411/823/517/62/98/82/9
29Kui et al.Feb 071375720-83619/510/27/31/51/5
30Tian et al.Feb 2726247/51-94127

In this study, we evaluate 30 articles. All papers were from China, and 3420 individual’s data were evaluated. All studies were cross-sectional, and 27 variables were included.

3.2. Heterogeneity

Evaluating the heterogeneity of the studies indicated that in the clinical characteristics and imaging studies groups, the combined effect of the squared index is high (68.43, 68.53). While the laboratory findings groups combined effect of the squared index is considered low (6.12). squared index for each of the outcomes is shown in Tables 47.


Clinical groupEffect size and 95% confidence intervalTest of null (2-tail)Heterogeneity
OutcomeNumber of studiesPoint estimateLower limitUpper limit value value squared

Cough140.6010.5350.6642.9750.00387.304
Fever140.8430.7860.8878.6500.00087.006
Headache120.0910.0700.118-15.9330.00057.760
Diarrhea110.0640.0430.095-12.2580.00071.281
Fatigue110.3940.2910.508-1.8270.06894.073
Dyspnea80.1710.0910.298-4.2900.00092.769
Expectoration50.2390.1640.334-4.8400.00077.659
Hemoptysis50.0230.0090.057-7.6520.00070.741
Shortness of breath50.2410.1510.361-3.8980.00087.758
Sore throat50.1300.0850.193-7.8660.00078.329
Muscle ache40.1140.0520.231-4.7490.00083.406
Nausea20.1090.0380.275-3.6330.00081.783


Group LAB testEffect size and 95% confidence intervalTest of null (2-tail)Heterogeneity
OutcomeNumber of studiesPoint estimateStandard errorVarianceLower limitUpper limit value value squared

CRP (mg/l)710.782.214.896.4415.114.870.000030.63
Lymphocytes (×109/l)71.000.130.020.731.267.480.000045.87
WBC (×109/l)74.870.260.074.365.3818.760.00000.00
ALT (U/l)523.432.848.0717.8629.008.250.00000.00
AST (U/l)525.842.476.0821.0130.6810.480.00001.71
Cr (μmol/l)569.903.8714.9762.3277.4818.060.00000.00
D-dimer (μg/l)5567.89112.1112568.89348.15787.625.070.00000.00
Hemoglobin (g/l)5131.713.3711.38125.10138.3239.050.00000.00
LDH (U/l)5258.5626.39696.51206.84310.299.800.000011.06
Neutrophils (×109/l)53.200.310.092.593.8010.380.00000.00
Platelets (×109/l)5166.3910.92119.17145.00187.7915.240.00000.00
Procalcitonin (ng/ml)50.170.080.010.010.322.150.031268.47
Creatine kinase (U/l)4110.1819.92396.7271.14149.225.530.00000.00
Total bilirubin (mmol/l)48.361.081.166.2510.487.760.00000.00
Albumin (g/l)338.611.462.1435.7541.4826.410.000044.52
BUN (mmol/l)34.830.550.303.755.918.770.00000.00
ESR240.7924.93621.75-8.0889.671.640.101891.15
Fibrinogen (g/l)23.210.220.052.783.6414.710.00000.00


Laboratory findings group (one study removed)Effect size and 95% confidence intervalTest of null (2-tail)Heterogeneity
OutcomeNumber of studiesPoint estimateLower limitUpper limit value value squared

CRP (mg/l)69.136.0012.275.710.001.73
Lymphocytes (×109/l)60.990.781.209.200.0037.93
WBC (×109/l)64.884.315.4516.750.000.00
ALT (U/l)523.4317.8629.008.250.000.00
AST (U/l)525.8421.0130.6810.480.001.71
Cr (μmol/l)569.9062.3277.4818.060.000.00
D-dimer (μg/l)5567.89348.15787.625.070.000.00
Hemoglobin (g/l)5131.71125.10138.3239.050.000.00
LDH (U/l)5258.56206.84310.299.800.0011.06
Neutrophils (×109/l)53.202.593.8010.380.000.00
Platelets (×109/l)5166.39145.00187.7915.240.000.00
Procalcitonin (ng/ml)50.170.010.322.150.0368.47
Creatine kinase (U/l)4110.1871.14149.225.530.000.00
Total bilirubin (mmol/l)48.366.2510.487.760.000.00
Albumin (g/l)338.6135.7541.4826.410.0044.52
BUN (mmol/l)34.833.755.918.770.000.00
Fibrinogen (g/l)23.212.783.6414.710.000.00


CT groupNumber of studiesEffect size and 95% confidence intervalTest of null (2-tail)Heterogeneity
OutcomePoint estimateLower limitUpper limit value value squared

Total (CT+)200.9230.8770.9539.2900.00081.398
(1) Ground-glass opacification (GGO)140.6980.6030.7793.8900.00090.700
(4) Bilateral involvement110.7940.6690.8814.0810.00093.032
(5) Consolidation110.3780.2640.508-1.8450.06590.564
(3) Peripheral distribution60.6680.5000.8021.9550.05188.951
(10) Unilateral involvement50.1560.0820.278-4.5100.00071.765
() Mixed opacity40.3280.1210.635-1.1060.26997.487
(11) Air bronchogram40.4260.2250.656-0.6180.53787.500
(6) Pleural effusion40.0660.0360.120-7.9500.00014.830
(7) Adjacent pleura thickening40.4090.2110.641-0.7640.44592.842
(12) Fibrous stripes30.2290.0560.596-1.4860.13793.828
(2) Lower lung predominant30.6240.4670.7581.5580.11964.196
(8) Interlobular septal thickening30.5350.3590.7030.3790.70585.123
1 lobe affected20.2070.0870.417-2.6080.00983.897
2 lobes affected20.0610.0320.113-7.9270.0000.000
3 lobes affected20.1050.0470.220-4.7960.00057.131
4 lobes affected20.0990.0600.157-8.1350.0000.000
5 lobes affected20.3940.3120.483-2.3310.02018.313
(9) Cavitation20.0120.0020.082-4.3500.0000.000
Crazy-paving pattern20.2220.0670.530-1.7860.07492.080
Linear opacities20.6300.5550.6993.3720.0010.000

3.3. Publication Bias and Sensitivity Analysis

The Funnel plot for clinical characteristics group studies is almost symmetric confirmed by the Egger regression test (, value = 0.20). By using the random-effects model, the summary estimate and 95% confidence interval for the combined studies is 0.25 (0.22, 0.29). These findings indicated no publication bias in the clinical characteristics group (Figure 4).

The funnel plots in the imaging studies group and findings studies seem asymmetric and skewed (Figures 5 and 6). Also, the Egger regression test has indicated an intercept of 1.55, and value of 0.01 for the laboratory findings group and an intercept of 0.94 and value of 0.04 for the imaging studies group.

In the laboratory findings group, using the random-effects model, the point estimate and 95% confidence interval for the combined studies is 3.01 (2.22, 3.80). Using trim and fill (four trimmed studies), the imputed summary estimate is 3.30 (2.30, 3.38) (Figure 5).

In the imaging studies group, the summary estimate and 95% confidence interval for the combined studies is 0.51 (0.48, 0.54). Using trim and fill, the imputed (four trimmed studies) summary estimate is 0.50 (0.47, 0.53) (Figure 6).

In conclusion, the finding of trim and fill analysis has indicated only minimal changes, which do not seem to be a threat to the validity of the effect size estimates.

Sensitivity analysis by using the “Remove-One” analysis did not show any change in the combined effect of the clinical characteristics and imaging studies groups after removing any of the studies. However, in the laboratory findings group, removing only one of the studies (Fan et al.) changed the combined effect significantly (Figure 7). The combined effect before and after removing Fan et al. study is presented in Tables 5 and 6.

3.4. Clinical Characteristics Group

According to clinical manifestations, fever (84.3%, 95% CI 78.6-88.7), cough (60.1%, 95% CI 53.5-66.4), and fatigue (39.4%, 95% CI 29.1-50.8) are the most prevalent clinical symptoms among patients (Table 4) (Figure 8).

3.5. Laboratory Findings Group

Laboratory studies show increased level in following tests: CRP (10.78 mg/l, 95% CI 6.44-15.11) with the normal range of 0-3.0 mg/l, D-dimer (567.89 ng/ml, 95% CI 348.15-787.62) with the normal range of 0-500 ng/ml, LDH (258.56 U/l, 95% CI 206.84-310.29) with the normal range of 135-250 U/l, and procalcitonin (0.17 ng/ml, 95% CI 0.01-0.32) which is normally less than 0.05 ng/ml in a healthy individual.

Also, the level of some laboratory factors is lower than normal, such as lymphocyte (, 95% CI 0.73-1.26) and albumin (38.61 g/l, 95% CI 35.75-41.48) concerning the normal range of and 40-55 g/l, respectively (Tables 5 and 6) (Figure 9).

3.6. Imaging Studies Group

Among all patients infected by SARS-CoV-2 (confirmed by RT-PCR), 85% had abnormalities in CT scans. In most of them, the bilateral pneumonia was dominant (79.4%, 95% CI 66.9-88.1). Ground-glass opacification (GGO) (69.8%, 95% CI 60.3-77.9), peripheral distribution (66.8%, 95% CI 50.0-80.2), and consolidation (37.8%, 95% CI 26.4-50.8) in those with CT scan results are presented (Table 7) (Figure 10).

4. Discussion

From December 2019, more than 500,000 cases of new unknown origin pneumonia have been confirmed all over the world [12]. It was primarily known as 2019-nCov; then, WHO decided to name this novel coronavirus “SARS-CoV-2” [13]. COVID-19 is a severe condition that is compromising the health condition of people in all countries worldwide [14]. Identifying the various characteristics of this infection is vital for controlling the outbreak in different countries [15]. Clinical, laboratory, and imaging findings are essential to evaluate the different aspects of infection [16]. Different outcomes of COVID-19 (from an asymptomatic infection to death) and contagiousness of this virus, even in its incubation period [17], emphasize why discovering different characteristics are crucial in controlling this pandemic.

In this systematic review and meta-analysis, we describe the most common clinical data on COVID-19 confirmed cases that were published during the first months of the outbreak. We analyzed 2422, rRT-PCR confirmed patients, for different clinical manifestations. Our findings are robust due to the pooled results after combining all the studies’ data.

As expected from initial studies in China, COVID-19 patients presented predominantly with cough and fever, as well as headache, diarrhea, and fatigue, among other clinical features [18]. This was consistently found in many of the included studies [19, 20]. Fever frequency is similar in other β-CoV-associated infections such as SARS and MERS, but studies showed that the cough frequency is higher in SARS and COVID-19 than MERS (<50%) [21, 22]. In SARS and MERS, diarrhea is reported in about a quarter of patients, but our data shows that only 6 percent of COVID-19 patients present with diarrhea (Table 4). Our data also suggest that about 11 percent of patients are presented with headache as a symptom. Unlike SARS, which is well characterized in the two-stage clinical course of the disease [23], COVID-19 still needs further definition to identify the disease process.

Studies on epidemiological features of COVID-19 showed that about 80 percent of patients are asymptomatic or are presented with mild manifestations [24, 25], but almost all of the patients included in our study had moderate-to-severe characteristics. It seems that fever and cough are the most common clinical features among moderate-to-severe patients (Table 4).

Studies show different laboratory abnormalities in COVID-19 patients, such as hypoalbuminemia or elevated inflammatory markers [26]. However, our data suggest that C-reactive protein is the most elevated factor among infected cases (Tables 5 and 6). D-dimer, LDH, and procalcitonin are also elevated in patients, which confirmed that measuring inflammatory markers are essential to investigate new cases [27]. Also, seven studies showed lymphopenia and albuminuria as other common laboratory findings. Data from new studies suggest lymphocytopenia or an increase in WBC as prognostic factors in COVID-19 patients. Studies on the SARS outbreak in 2003 indicate that lymphopenia, leukopenia, and thrombocytopenia, elevated levels of LDH, alanine transaminase (ALT), AST, and creatine-kinase are the most affected laboratory findings [28].

Nevertheless, not significantly seen in COVID-19, the novel corona virus can affect the liver and other organs [29]. AST and ALT are normal in most cases, but impaired liver function tests are associated with poor prognosis and higher mortality rates [30, 31]. Coagulation function tests (such as INR) are affected in the prognosis of this infection [32]. Lymphopenia in COVID-19 patients suggests that this virus might act on lymphocytes (mainly T cells), but some studies suggest that B cells are also affected [26, 33].

CT scan is one of the most useful methods to diagnose the respiratory tract diseases diagnosis. CT scan high sensitivity and availability makes it one of the most common tests for lung disease screening [34, 35]. In COVID-19 patients, different results could present in the early stages of infection [36, 37]; even some studies demonstrate that CT scan sensitivity is higher than rRT-PCR [6]. Our data shows that 92% of rRT-PCR confirmed cases had abnormal CT scan results, which suggest CT scan as a reliable method. As seen in Table 7, CT scan meta-analysis outcomes are performed in random-effects analyses. Our meta-analysis on 15 studies showed that ground-glass opacification (GGO) and peripheral distribution are seen on 69.8% and 66.8% of patients, respectively. 79.4% of the patients had bilateral involvements, which is contributed to poor prognosis. CT scan is useful in monitoring the treatment, and it is crucial in classifying patients and identifying who should be treated with aggressive treatments [38]. Other findings such as consolidation or reverse halo or atoll sign are reported in some studies [39], which were not included in our analysis.

5. Limitations

This review has several limitations. Few studies are available on COVID-19, and most of them are from China. Many countries such as Italy, the United States, and Iran reported several new COVID-19 patients, but data about clinical characteristics or laboratory findings are limited. By publishing more studies worldwide, researchers are going to get a more comprehensive understanding of COVID-19. Patients’ detailed information, especially in clinical outcomes, was unavailable in most studies at the time of analysis. In this study, we used random-effects model for analysis in all three groups. In comparison to the fixed model, random model findings have wider confidence intervals and less accurate results, although heterogeneity in included studies could not be considered in the fixed model.

6. Conclusion

COVID-19 presents in the majority of cases with fever and cough. Laboratory findings such as elevated inflammatory markers can assist the diagnosis. Other laboratory indices, such as AST, ALT, or INR, are also affected in these patients. 92% of the RT-PCR confirmed that patients have abnormalities in CT scan most frequently bilateral involvement. Additional research with higher sample sizes is needed in order to describe the patients’ characteristics more precisely.

Abbreviations

COVID-19:Corona virus disease of 2019
rRT-PCR:Real-time reverse transcription-polymerase chain reaction
CT scan:Computed tomography scan
CI:Confidence intervals
CRP:C-reactive protein
RNA:Ribonucleic acid
SARS-CoV-2:Severe acute respiratory syndrome corona virus #2
PRISMA:Protocol based on the transparent reporting of systematic reviews and meta-analysis
CBC:Complete blood count
CMA:Comprehensive meta-analysis
GGO:Ground-glass opacification
β-CoV:Beta coronavirus
MERS:Middle East respiratory syndrome
SARS:Severe acute respiratory syndrome
LDH:Lactate dehydrogenase
WBC:White blood cell
ALT:Alanine transaminase
AST:Aspartate aminotransferase
INR:International normalized ratio.

Conflicts of Interest

The authors declare that they have no competing interest regarding the publication of this paper.

Authors’ Contributions

HH managed the group and participated in preparing the manuscript. SS participated in meta-analysis. SM and NT participated in the systematic search and substantially drafted the manuscript. PM revised the manuscript critically. All authors read and approved the final version of the manuscript.

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