Clinical characteristics, laboratory findings, radiographic signs and outcomes of 61,742 patients with confirmed COVID-19 infection: A systematic review and meta-analysis.

INTRODUCTION: In the current time where we face a COVID-19 pandemic, there is no vaccine or effective treatment at this time. Therefore, the prevention of COVID-19 and the rapid diagnosis of infected patients is crucial.
METHOD: We searched all relevant literature published up to February 28, 2020. We used Random-effect models to analyze the appropriateness of the pooled results. RESULT: Eighty studies were included in the meta-analysis, including 61,742 patients with confirmed COVID-19 infection. 62.5% (95% CI 54.5-79, p < 0.001) of patients had a history of recent travel endemic area or contact with them. The most common symptoms among COVID-19 infected patients were fever 87% (95% CI 73-93, p < 0.001), and cough 68% (95% CI 55.5-74, p < 0.001)), respectively. The laboratory analysis showed that thrombocytosis was present in 61% (95% CI 41-78, p < 0.001) CRP was elevated in 79% (95% CI 65-91, p < 0.001), and lymphopenia in 57.5% (95% CI 42-79, p < 0.001). The most common radiographic signs were bilateral involvement in 81% (95% CI 62.5-87, p < 0.001), consolidation in 73.5% (95% CI 50.5-91, p < 0.001), and ground-glass opacity 73.5% (95% CI 40-90, p < 0.001) of patients. Case fatality rate (CFR) in <15 years old was 0.6%, in >50 years old was 39.5%, and in all range group was 6%.
CONCLUSIONS: Fever and cough are the most common symptoms of COVID-19 infection in the literature published to date. Thombocytosis, lymphopenia, and increased CRP were common lab findings although most patients included in the overall analysis did not have laboratory values reported. Among Chinese patients with COVID-19, rates of hospitalization, critical condition, and hospitalization were high in this study, but these findings may be biased by reporting only confirmed cases.

Introduction

In December 2019, the new COVID-19 coronavirus was recognized as a cause of respiratory illness. The first reports of pneumonia were from people who worked or lived in the Huanan seafood wholesale market in Wuhan, China raising concerns about a zoonotic viral infection [1,2]. Phylogenetic analysis showed that the COVID-19 belong to the beta-coronavirus [1]. Epidemiological studies have shown that the virus is spread relatively easily and can be transmitted by aerosol, droplets, and through infected surfaces [3]. The COVID-19 has now spread to more than 50 countries from December 2019 to February 2020 [4]. Most symptoms are non-specific in patients with respiratory disease. According to the latest WHO report, out of 83,652 confirmed cases of COVID-19 worldwide, 2791 deaths occurred in China and 67 deaths is recorded in other countries [4].

Thus far, 6 coronaviruses that are able to infect humans have been identified, coronavirus infections are typically asymptomatic or associated with mild respiratory symptoms [1]. The first coronavirus to cause severe disease in humans was the Severe Acute Respiratory Syndrome virus (SARS), which was appeared in the Guangdong province of southern China in 2002, there were 8098 reported case and 774 deaths [5]. In Saudi Arabia in 2012, the Middle East respiratory syndrome coronavirus (MERS-CoV), which was transmitted from the camels to humans, caused 2458 infections with 848 deaths [6].

Clinical studies have shown that COVID-19 can rapidly cause pulmonary damage and severe respiratory symptoms [3]. There is no vaccine or targeted treatment currently available for COVID-19 infection. Treatment is largely supportive although multiple experimental antiviral medications are being evaluated [7,8]. Thus, prevention and rapid diagnosis of infected patients is crucial. To date, the published clinical studies are quite small and give variable findings. With this in mind, here we evaluate the clinical features and laboratory findings using a large sample size of COVID-19 infected patients in order to assist in its understanding, prevention and treatment.

Methods

Search strategy

This study was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement (PRISMA) guidelines [9]. We searched all studies published up to February 28, 2020 from the following databases: Embase, Scopus, PubMed, Web of Science and the Cochrane library. Search medical subject headings (MeSH) terms used were: “COVID-19”, “Coronavirus”, “severe acute respiratory syndrome coronavirus”, and all their synonyms like “Wuhan Coronavirus”, “SARS-CoV-2”, and “COVID-19”. Moreover, we searched for unpublished and grey literature with Google scholar, Center for Disease Controls (CDC) and WHO databases. We also examined references of included articles to find additional relevant studies. There was no language restriction and all included studies are written in English or Chinese languages, the latter were translated by https://translate.google.com/. Additional search strategy details are provided in Table S1 (supplementary material) [10].

Study selection

Duplicate studies were removed using EndNote X7 (Thomson Reuters, New York, NY, USA). Records were initially screened by title and abstract by independently two authors (AP, SG). The full-text of potentially eligible records was retrieved and examined. Any discrepancies were resolved by consensus.

Inclusion criteria

Studies had to fulfil the following pre-determined criteria to be eligible for inclusion in our meta-analysis. Studies were included if they reported the number of confirmed cases of patients with demographic data, [AND] [OR] clinical data, [AND] [OR] laboratory data, [AND] [OR] risk factor data. Confirmed patients were defined as any patient with positive nucleic acid testing (most of the studies with Real-Time PCR) or those meeting CDC and WHO criteria at the time of their publication.

Exclusion criteria

Studies were excluded if they did not report number of confirmed cases, were letters to the editor or individual case reports or reviews. News reports were also excluded.

Data extraction

All included publications were published in 2020 and all patients are from China. The following items were extracted from each article: first author, Center and study location in China, sample collection time period, patient follow-up time, reference standard for infection confirmation, number of confirmed cases, and all demographic, clinical, laboratory data, and risk factor data. Two of our authors (AP and SG) independently extracted data and differences were resolved by consensus.

Quality assessment

Quality assessments of studies were performed by two reviewers independently according to the Critical Appraisal Checklist recommended by the Joanna Briggs Institute [11], and disagreements were resolved by consensus. The checklist is composed of nine questions that reviewers addressed for each study. The ‘Yes’ answer for each question received one point. Thus, final scores for each study could range from zero to nine (Table S2 in Supplementary Material).

Analysis

Data cleaning and preparation was done in Microsoft Excel 2010 (Microsoft©, Redmond, WA, USA) and further analyses were carried out via Comprehensive Meta-Analysis Software Version 2.0 (Biostat, Englewood, NJ). Determination of heterogeneity among the studies was undertaken using the chi-squared test (Cochran's Q) to assess the appropriateness of pooling data. We used Random effect model (M − H heterogeneity) for pooled results [12]. P values reflect study heterogeneity with <0.05 being significant. We also used the Begg's and Egger's tests based on the symmetry assumption to detect publication bias.

Results

Characteristics of included studies

The process of study selection is displayed in Fig. 1 . A total of 36,115 reports were screened for the analysis of patients with COVID-19, 36,014 were excluded after title and abstract screening and the full text of 342 reports were reviewed in full text. We excluded studies that did not report sufficient data and finally 80 studies met the inclusion criteria (Fig. 1). Characteristics of the selected articles are summarized in Table 1 . Of the 80 studies that were included in the analysis, 79 studies were in English and the one of them was in the language of Chinese [13]. All studies were retrospective, published in 2020, and all patients were from China.

Fig. 1

Flow Diagram of Literature Search and Study Selection (PRISMA flow chart).

Table 1

Characterization of Included Studies with total 61, 742 COVID-19 Confirmed Patients. All Studies are Retrospective, from China, and Published in 2020.

First Author	Sampling Center	Sample collection time	Patient follow up (days)	N Confirmed Patients	Mean age in years (IQR)	N sex (male)	Reference standard
Nanshan Chen [14]	Wuhan Jinyintan Hospital	Jan 1 to Jan 20, 2020	5–24	99	55·5	67	RT-PCR
					(21–82)
Kaiyuan Sun [30]	Multicenter	Jan 20- Jan 29, 2020	42	288	49	62.3	CDC guideline
					(2–89)
Jie Li [31]	Dazhou Central Hospital	22 January- February 10, 2020	1–21	17	45.1	9	RT-PCR
					(32–65)
Dawei Wang [15]	Zhongnan Hospital of Wuhan	January 1-January 28, 2020	6–34	138	56	75	RT-PCR
					(42–68)
Chaolin Huang [16]	Jin Yintan Hospital (Wuhan)	Dec 31, 2019-UN	NA	41	49	30	RT-PCR
					(41–58)
Weijie Guan [17]	Multicenter	NA	NA	1099	47	640	RT-PCR
					(35–58)
Yang Yang [32]	NA	NA	51 days	4021	49	2211	NA
Lei Chen (Chinese) [13]	Tongji hospital in Wuhan	January 14–29, 2020	15 day	29	56	21	RT-PCR
					(26–79)
Adam Bernheim [3]	Multicenter	January 18-February 2, 2020	12 days	121	45	61	RT-PCR & CT scan
					(18–80)
Feng Pan [33]	Union Hospital	12 Jan-6 Fen 2020	NA	21	40	15	RT-PCR
					(25–63)
jin Zhang [18]	No.7 hospital of Wuhan	Jan 16th to Feb 3rd, 2020	NA	140	57	71	RT-PCR
					(25–87)
Yichun Cheng [19]	Tongji hospital in Wuhan	January 28-February 11, 2020	10 (7–13)	710	63	374	RT-PCR
					(51–71)
Ming-Yen [34]	Hong Kong-Shenzhen Hospital	NA	NA	21	56	13	RT-PCR
					(37–65)
Sijia Tian [35]	Beijing Emergency Medical Service	Jan 20 to Feb 10, 2020	Feb.10 20	262	47.5	127	RT-PCR
					(1–94)
Qun Li [20]	NA	NA	NA	425	15–89	240	WHO guideline
					(26–82)
De Chang [36]	3 hospitals in Beijing	January 16- January 29, 2020	Feb.42020	13	34	10	NA
					(34–48)
Xiao-Wei Xu [21]	Zhejiang province	10 January −26 January 2020	10 days	62	41	36	WHO guideline
					(32–52)
Fengxiang Song [22]	Center for Disease Control, Shanghai	January 20- January 27, 2020	NA	51	49	25	CT scan & nucleic acid test
					(16–76)
Michael Chung [37]	Multicenter	January 18–27, 2020	NA	21	51	13	CT scan, NA
					(29–77)
Zunyou Wu (CDC) [38]	Multicenter	through February 11, 2020	15 days	44,672	30–79	22,981	nucleic acid test result
Bicheng Zhang [39]	hospitalized death	January 11, 2020 to February 10	30 day	82	72.5	54	rt-pcr

NA = not known, RT-PCR= Real Time Polymerase Chain Reaction, CDC= Centers for Disease Control and Prevention, WHO= World Health Organization, CT scan = CT scan of chest, N = number, IQR = interquartile range.

Quality assessment of included studies were performed based on the Critical Appraisal Checklist and the final scores for quality of included studies are represented in Table S2 (in supplementary material). In brief, studies by Chen [14], Wang [15], Huang [16], Guan [17], Zhang [18], Cheng [19], Li [20], Wei Xu [21], and Song [22] had the highest quality of the studies available in the purpose of this study.

Demographics, baseline characteristics, and clinical characterization

Table 2 shows that 61, 742 confirmed patients with COVID-19 infection were included in the Meta-analysis, of which 55% (95% CI 50–57.5, p < 0.001) were male. The most of the patients had fever 87% (95% CI 73–93, p < 0.001) and cough 68% (95% CI 55.5–74, p < 0.001). A much smaller proportion of patients had sore throat 14% (95% CI 7.8–17, p 0.06), headache 14% (95% CI 8.3–18, p < 0.001), diarrhea 8% (95% CI 4.6–11.4, p < 0.001), rhinorrhea 7% (95% CI 3–12, p 0.43) or nausea and vomiting 6.5% (95% CI 2.7–13, p < 0.001). Most patients required hospitalization 81% (95% CI 68–94, p < 0.001), 25.6% (95% CI 6.7–48, p < 0.001) were deemed to be in critical condition and the mortality rate was 6% (95% CI 4–8.5, p < 0.001) between all infected patients. Table 3 shows that case fatality rate (CFR) in <15 years old age groups was 0.6% (95% CI 0–0.9, p 1), >50 years old was 39.5% (95% CI 28.5–52, p < 0.001) (Fig. 2 ), all range group was 6% (95% CI 4–8.5, p < 0.001) (Fig. 3 ).

Table 2

Demographics, baseline characteristics, and clinical outcomes of patients with confirmed COVID-19.

	Clinical presentation*	Confidence interval 95%	Heterogeneity test, I2 (%)**	Heterogeneity test, P Value**	Number of Studies
Age, years	48 (mean)	43–50	98	<0.001	23
Sex (Male)	55 (%)	50–57.5	88.4	<0.001	24
Fever	87 (%)	73–93	98	<0.001	18
Cough	68 (%)	55.5–74	86	<0.001	18
Fatigue	39 (%)	29–52.5	93	<0.001	14
Sputum production/Expectoration	31 (%)	19–39	92	<0.001	9
Myalgia	24 (%)	14–43	92	<0.001	9
Dyspnea	24 (%)	12.6–32	92	<0.001	11
Sore throat	14 (%)	7.8–17	52	0.06	9
Headache	14 (%)	8.3–18	77	<0.001	16
Diarrhea	8 (%)	4.6–11.4	70	<0.001	18
Rhinorrhea	7 (%)	3–12	0	0.43	6
Nausea and vomiting	6.5 (%)	2.7–13	84	<0.001	6
Outcome
Hospitalized	81 (%)	68–94	95	<0.001	7
Critical condition/ICU	25.6 (%)	6.7–48	99	<0.001	8
CFR (all age group)	6 (%)	4–8.5	89.6	<0.001	49

*Age is an exception, presented in mean age in years. ** Greater than 50% is considered high heterogeneity, less than 50% is considered low heterogeneity. A low p value (<0.05) is consistent with high heterogeneity. Case fatality rate (CFR).

Table 3

Meta-analysis on clinical presentation of case fatality rate (CFR) in different age groups of confirmed COVID-19 cases.

Age groups (year)	CFR (%)	Confidence Interval		patients		Heterogeneity test*
		Lower limit (%)	Upper limit (%)	Number Studies	Included patients	I-squared	P-value
All Range	6	4	8.5	49	54,252	89.6	<0.001
>50	39.5	28.5	52	14	1935	97	<0.001
<15	0.6	0	0.9	1	82	0	1

Case fatality rate (CFR), * Greater than 50% is considered high heterogeneity, less than 50% is considered low heterogeneity. A low p value.

Fig. 2

Forest plot of the meta-analysis on clinical presentation of case fatality rate (CFR) in different age groups of confirmed COVID-19 cases.

Fig. 3

Forest plot of the meta-analysis on clinical presentation of case fatality rate (CFR) in all age groups of confirmed COVID-19 cases.

Clinical characteristics, and Comorbid conditions of patients infected with COVID-19

The majority of patients, 62.5% (95% CI 54.5–79, p < 0.001), had a history of recent travel endemic area or contact with them. A significant minority of patients (39.5%, 95% CI 20–56, p < 0.001) had a history of chronic diseases and 26.5% (95% CI 9.6–49, p < 0.001) had exposure at the seafood market(s) (Table 4 ).

Table 4

Clinical Characteristics and Comorbid Conditions of patients with confirmed COVID-19.

Risk Factor	Patients with risk factor (%)	Confidence interval 95%	Heterogeneity test, I2 (%)*	Heterogeneity test, P Value*	Number of Studies reporting
History of recent travel endemic area or contact with them	62.5	54.5–79	96	<0.001	11
Chronic diseases	39.5	20–56	95	<0.001	6
Exposure to seafood market	26.5	9.6–49	95	<0.001	8
Sick contacts with respiratory illness	18	4.5–39.6	97	<0.001	7
Hypertension	18	8.5–24.6	97.5	<0.001	17
ARDS	17.5	4–26.7	95.7	<0.001	8
Diabetes	9	4–15	96	<0.001	11
Current smoker	8.2	3.7–15	69	0.01	8
Chronic liver disease	7	3.8–8.4	6	0.38	12
Digestive system disease	4.5	2.5–4.9	95	<0.001	8
Health care worker	16	2–4.6	79	0.008	12
Past smoker	4	1.1–7.5	80	0.02	6
Cardiovascular and cerebrovascular diseases	3.3	2.2–2.5	98	<0.001	14
Chronic respiratory disease	3.2	0.6–8	93	<0.001	7
Cancer	2.7	0.4–7.4	96.3	<0.001	9

ARDS = acute respiratory distress syndrome * Greater than 50% is considered high heterogeneity, less than 50% is considered low heterogeneity. A low p value (<0.05) is consistent with high heterogeneity.

Laboratory findings of patients infected with COVID-19

The laboratory analysis and features showed that the most infected patients had increased platelets 61% (95% CI 41–78, p < 0.001), and CRP 79% (95% CI 65–91, p < 0.001), while others showed decreased lymphocytes, 57.5% (95% CI 42–79, p < 0.001) (Table 5 ).

Table 5

Laboratory features for confirmed patients with COVID-19.

		Confidence interval 95%	normal range	Total Patient Number	Number of Studies
Leucocytes (WBCs) (mean)	6.2 ( × 10⁹ per L)	5.3–6.9	3.5–9.5	2961	17
Increaseda	18.3 (%)	6.4–25.6
Decreased	28 (%)	21–33
Neutrophils (mean)	4.6 ( × 10⁹ per L)	3.1–5.1	1.8–6.3	1212	12
Lymphocytes (mean)	0.94 ( × 10⁹ per L)	0.9–1.06	1.1–3.2	3161	18
Decreased	57.5 (%)	42–79
Platelets (mean)	196.5 ( × 10⁹ per L)	167–205	125–350	2900	15
Decreased	13 (%)	5–30
Increased	61 (%)	41–78
CRPa(mean)	32 (mg/L)	19.7–46.5	0–0.5	880	10
Increased	79 (%)	65–91
Hemoglobin (mean)	113 (g/L)	106–132	130–175	2862	12
ESR**(mean)	44 (mm/h)	46–57	0–15	320	4
Albumin (mean)	36.8 (g/L)	24.5–46	40–55	420	5
Decreased	81%	72–87
Interleukin-6 (mean)	8.1 (pg/mL)	6.8–8.6	0.0–7	509	6
Increased	56%	42–61
LDH*** (mean)	286	268–294	120–250	2383	12
Increased	69.3 (%)	58–83

CRP= C Reaction Protein, ESR = Erythrocyte sedimentation rate. WBCs= White blood cells.

Increased or Decreased refers to values above or below the normal range.

Chest X-ray and CT scan findings in patients infected with COVID-19

Analysis showed that the most abnormality which finding with Chest X-ray and CT are bilateral involvement of chest radiography 81% (95% CI 62.5–87, p < 0.001), consolidation 73.5% (95% CI 50.5–91, p < 0.001), and ground-glass opacity 73.5% (95% CI 40–90, p < 0.001) (Table 6 ).

Table 6

Chest X-ray and CT scan Findings in Patients with Confirmed COVID-19.

	Abnormality (%)	Confidence interval 95%	Heterogeneity test, I2 (%)a	Heterogeneity test, P Valuea	Number of Studies
Bilateral involvement of chest radiography	81	62.5–87	93	<0.001	18
Consolidation	73.5	50.5–91	89	<0.001	9
Ground-glass opacity	73.5	40–90	97	<0.001	16
Unilateral involvement of chest radiography	18.5	8.5–29.5	94	<0.001	9

Greater than 50% is considered high heterogeneity, less than 50% is considered low heterogeneity. A low p value (<.05) is consistent with high heterogeneity. CT scan = CT scan.

Discussion

COVID-19 belongs to the Coronaviridae family and is the newest serious zoonotic virus after the related viruses SARS and MERS [23,24]. Prior to 2002, coronaviruses were associated with mild respiratory illness, but with the emergence of SARS in 2002, MERS in 2012, and now in late 2019, COVID-19, establishes that coronaviruses can be associated with severe respiratory disease. Genetic variation and phylogenetic analysis of these viruses show that the COVID-19 virus has 84% homology to other beta-coronaviruses, 96% sequence similarity at the whole genome level to a bat coronavirus and 79.5% similarity to the SARS virus [8,25]. These results suggest that bats are important coronavirus reservoirs.

A study by Adam Bernheim et al. showed that among 121 COVID-19 patients, fever, cough and sputum production were the most common clinical symptoms [3]. Our study found utilizing data from 52,251 patients with COVID-19 infection, that in additional to these, fatigue and myalgia (muscle soreness) were also common.

The large data set here finds that 81% of patients required hospitalization, 25.6% were found to be in critical condition and the mortality rate was 6% between all infected patients. The mortality rate is lower than some studies (for example, 11% in Nanshan et al. [14]), but still higher than many viral infections. It should be recognized that these numbers are bias due to the data set including publications related to screening practices (e.g. only those with symptoms being screened) increased the % value. The true mortality rate from COVID-19 is almost certainly much lower than that found in this study. As more data emerges from screening asymptomatic or mildly symptomatic individuals in China and around the world, the true mortality rate will be better understood. Additionally, at the time of submission of this manuscript only ~50% of reported infected patients had recovered (gisanddata.maps.arcgis.com). Lymphopenia, age, multilobular infiltration, smoking history, hypertension, and bacterial co-infection have been reported as mortality risk factors. Underlying cardiovascular disease (40%) and bilateral pneumonia (81%) were common among those who have died. Recent travel endemic area or contact with them, exposure to persons with respiratory symptoms, and seafood market exposures were common amongst those contracting COVID-19. Among 2361 COVID-19 patients with laboratory data available, leukocytosis was found in 18.3% and leukopenia in 28% with lymphocytopenia in 57.5%. Among 2200 patients, thrombocytosis occurred in 61% and in a smaller sample (n = 290) CRP was increased in 79%.

A study by Yu Zhao et al. showed that ACE2 is a COVID-19 virus receptor and that it is normally expressed on pulmonary alveolar epithelial cells [26]. ACE2 activates the RAS cascade, which can lead to hypertension. The pathology in this pathway can also stimulate fibrogenesis, inflammation, cell hypertrophy, and cell proliferation [27,28]. ACE2 expression is increased in people with pulmonary ARDS and acute respiratory injury [29]. The data collected here shows that ARDS occurred in 17.5% of reported patients with COVID-19 infection.

Limitations

Several limitations of this study exist. Publication bias and study heterogeneity are unavoidable in this type of study, therefore it should be considered when interpreting the outcomes of the reports and our final data set. Further, this study likely overestimates disease severity due to lack of screening of asymptomatic or mildly symptomatic individuals and subsequent publication bias related to these factors. It is very likely that many infected persons have not been detected, thus falsely elevating the rates of hospitalization, critical condition, and mortality.

Conclusions

Fever and cough are the most common symptoms of COVID-19 infection in the literature published to date. Thombocytosis, lymphopenia, and increased CRP were common lab findings although most patients included in the overall analysis did not have laboratory values reported. The most common radiographic sign was bilateral involvement in and consolidation. Among Chinese patients with COVID-19, rates of hospitalization, critical condition, and hospitalization were high in this study, but these findings may be biased by reporting of only confirmed cases.

Declaration of competing interest

The authors have declared that no competing interests exist.