†These authors contributed equally.
The coronavirus disease 2019 (COVID-19) is a novel coronavirus infection that
has rapidly spread worldwide, causing a pandemic. The main objective of this
meta-analysis was to evaluate the prevalence of the most common symptoms and
complications of COVID-19. All relevant studies on the clinical complications of
COVID-19 have been identified by searching two web databases (i.e., PubMed and
Scopus). Afterward, the relevant data were extracted from the selected studies,
and then analyzed by the STATA (Version 14) random-effects model. The 30 studies
selected for our meta-analysis covered 6,389 infected patients. The prevalence
rates of the most common symptoms were as follows: fever: 84.30% (95% CI:
77.13-90.37; I
Since the emergence of a cluster of pneumonia cases in Wuhan, China in December 2019, the COVID-19 disease has attracted a lot of global attention. On 9 January 2020, the Chinese Center for Disease Control and Prevention stated that the cause of this newly-emerged disease was a novel virus from the coronavirideae family, which was later named the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), while the disease caused by this virus was then called coronavirus disease 2019 (COVID-19). Unfortunately, this epidemic was not just limited to China. On 11 March 2020, the World Health Organization (WHO) announced the COVID-19 outbreak as a pandemic (Kreutz et al., 2020; WHO, 2020b). According to WHO, as of writing this draft (8 August, 2020), about 20,000,000 individuals have been diagnosed with COVID-19, and unfortunately more than 700,000 of these patients have died (WHO, 2020a). Moreover, based on the latest reports, the most common symptoms of COVID-19 are fever, dry cough, fatigue, dyspnea, and diarrhea (Guan et al., 2020).
The main clinical complications of COVID-19 are related to the respiratory system, ranging from simple pneumonia in mild cases to acute respiratory distress syndrome (ARDS) and shock in severe patients. In this regard, the angiotensin-converting enzyme 2 (ACE2) was identified as the host cell receptor responsible for the entry of SARS-CoV-2 and the facilitation of infection. It should be noted that lung, heart, kidney, and intestine cells express ACE2.
As previously observed for SARS-CoV (Li et al., 2003), SARS-CoV2 (Hoffmann et al., 2020) can similarly use ACE2 as a receptor for viral cell entrance. In the Renin-Angiotensin-Aldosterone System (RAAS), ACE2 has a catalytic effect on the conversion of angiotensin II to angiotensin 1-7, which has a number of protective effects in the cardiovascular system, and can be considered as a vasodilator. In animal experiments, the increased activity and expression of ACE2 in different organs (e.g., the heart) were associated with the administration of ARB and ACE-I (Ferrario et al., 2005; Kuster et al., 2020). Human pathogenic coronaviruses (i.e., SARS-CoV2 and SARS-CoV) may bind to their target cells through ACE2, which is expressed by the epithelial cells of the kidney, the intestine, the blood vessels, and the lung (Fang et al., 2020; Y. Wan et al., 2020). In addition, severe lung inflammation caused by SARS-CoV-2 infection can lead to dysregulation of the renin-angiotensin system as well as further development of ARDS.
In addition to respiratory manifestations, cardiovascular and renal complications may also occur following COVID-19 infection. In practice, growing evidence confirms cardiovascular involvement in the COVID-19 disease, and its negative impact on prognosis (Shi et al., 2020a). There are also some studies confirming that acute and chronic renal injuries are expected due to the high expression of ACE2 in the renal tubular epithelium (South et al., 2020).
Since these complications are extremely life-threatening, their prevalence in the clinical outcomes of patients can act as determining factors in the morbidity and mortality rates of the disease. Therefore, this paper aimed to determine the prevalence of critical complications in patients with COVID-19 through a systematic review of a number of recent studies and analyzing the relevant data.
Study flow diagram. To find eligible cross-sectional and case-control studies, a 5 step screening process was conducted (based on PRISMA checklist).
In order to identify the eligible cross-sectional and case-control studies published on COVID-19 until May 1, 2020, we conducted a web search in the citations in PubMed. Accordingly, two authors have independently conducted the search process. In this regard, our search queries included ‘ARDS AND COVID’, ‘((Heart) OR Cardio-) AND COVID’, ‘((Kidney) OR Renal) AND COVID’, and ‘Complications AND COVID’. Eligible studies were then selected based on the titles of the identified papers. In addition, the reference lists of all the related reviews (i.e., narrative and systematic) were searched to identify more related articles (Table 1 and Fig. 1).
Study (ref) | Place | Patients (No.) | Age (Median) | Symptoms (P.) | Complications (P.) | Mortality (%) | ||||||||
All | Male | Female | Fever | Cough | Fatigue | Diarrhea | Dyspnea | Acute Cardiac Injury | AKI | ARDS | ||||
Zhou et al.,2020 | China | 191 | 119 | 72 | 56 | 94 | 79 | 23 | 5 | 29 | 17 | 15 | 54 | 28 |
Du et al.,2020 | China | 85 | 62 | 23 | 92 | 22 | 59 | 19 | 59 | 5 | 47 | |||
L. Wang et al.,2020 | China | 339 | 166 | 173 | 69 | 92 | 53 | 40 | 13 | 41 | 21 | 8 | 21 | 19 |
X. Li et al.,2020 | China | 25 | 10 | 15 | 73 | 92 | 92 | 100 | ||||||
N. Chen et al.,2020 | China | 99 | 67 | 32 | 83 | 82 | 11 | 2 | 31 | 3 | 17 | 11 | ||
D. Wang et al.,2020 | China | 138 | 75 | 63 | 99 | 59 | 70 | 10 | 31 | 7 | 4 | 20 | 4 | |
Guan et al.,2020 | China | 1099 | 637 | 459 | 47 | 44 | 68 | 38 | 4 | 19 | 0.5 | 3 | 1 | |
X. Yang et al.,2020 | China | 52 | 35 | 17 | 98 | 77 | 12 | 64 | 30 | 67 | 62 | |||
Guo et al.,2020 | China | 187 | 91 | 96 | 15 | 25 | 23 | |||||||
Y. Liu et al.,2020 | China | 12 | 8 | 4 | 83 | 92 | 33 | 17 | 50 | |||||
W. Liu et al.,2020 | China | 78 | 39 | 39 | 38 | 44 | 26 | |||||||
Z. Li et al.,2020 | China | 193 | 95 | 98 | 57 | 89 | 69 | 39 | 18 | 36 | 12 | 28 | 28 | 66 |
T. Chen et al.,2020 | China | 274 | 171 | 103 | 62 | 91 | 68 | 50 | 28 | 44 | 44 | 11 | 72 | 41 |
J. Zhang et al.,2020 | China | 82 | 54 | 28 | 72.5 | 78 | 65 | 46 | 12 | 63 | 100 | |||
Shi et al.,2020a | China | 416 | 205 | 211 | 64 | 80 | 35 | 13 | 4 | 28 | 2 | 23 | 14 | |
L. Wang et al.,2020 | China | 116 | 67 | 49 | 54 | 10 | ||||||||
Arentz et al.,2020 | USA | 21 | 11 | 52 | 48 | 76 | 2 | 52 | ||||||
T. Chen et al.,2020 | China | 203 | 108 | 95 | 54 | 89 | 60 | 8 | 2 | 13 | ||||
G. Zhang et al.,2020 | China | 221 | 108 | 113 | 55 | 91 | 61 | 71 | 11 | 29 | 8 | 5 | 22 | 5 |
Cao et al.,2020 | China | 102 | 53 | 49 | 54 | 81 | 49 | 55 | 11 | 34 | 15 | 20 | 20 | 17 |
Hu et al.,2020 | China | 323 | 166 | 157 | 61 | 84 | 51 | 4 | 7 | 5 | 4 | |||
Huang et al.,2020 | China | 41 | 30 | 11 | 49 | 98 | 76 | 44 | 3 | 55 | 12 | 0.7 | 29 | 15 |
X. C. Li et al.,2020 | China | 548 | 279 | 269 | 60 | 95 | 76 | 47 | 33 | 57 | 22 | 17 | 38 | 17 |
Lian et al.,2020 | China | 136 | 58 | 78 | 85 | 63 | 18 | 13 | 2 | 17 | 0 | |||
Lian et al.,2020 | China | 652 | 349 | 303 | 80 | 65 | 18 | 3 | 2 | 5 | 0 | |||
S. Wan et al.,2020 | China | 135 | 72 | 63 | 47 | 89 | 77 | 33 | 13 | 13 | 7 | 4 | 16 | 0.7 |
Deng et al.,2020 | China | 112 | 57 | 55 | 65 | 88 | 71 | 56 | 13 | 0 | ||||
Shao et al.,2020 | China | 136 | 90 | 46 | 38 | 52 | 49 | 20 | 75 | 0 | ||||
Tu et al.,2020 | China | 25 | 19 | 6 | 70 | 72 | 76 | 92 | 100 | |||||
Tu et al.,2020 | China | 149 | 60 | 89 | 51 | 5 | 5 | 5 | 0 | |||||
F. Yang et al.,2020 | China | 91 | 49 | 43 | 34 | 17 | 80 | 100 | ||||||
Yao et al.,2020 | China | 108 | 43 | 65 | 52 | 74 | 78 | 26 | 8 | 14 | 7 | 15 | 42 | 11 |
*Abbreviations: No = Number; P = prevalence. |
At the first stage, all the studies analyzing the clinical complications of COVID-19 were examined. To be included in the final analysis, the screened studies must report data on the prevalence of each of the clinical complications in COVID-19 patients, including ARDS, acute heart damage, arrhythmia, heart failure, and AKI.
For all the included studies, related data, i.e., the name of the first author, date of publication, location of publication, sample size, sample age, sample gender, prevalence of symptoms (including fever, cough, dyspnea, fatigue, and diarrhea), critical complications (including ARDS, acute cardiac injury, arrhythmia, heart failure, and AKI), and clinical outcomes (e.g., mortality rate) were extracted. To avoid including conflicting results, the authors extracted data from each study separately, and then the results were compared (Table 1).
The prevalence of cardiovascular and renal complications was considered as the
effect size in this study. Moreover, using the binomial distribution, its
variance was assessed (with 95% confidence interval). Average weight was then
applied to combine the prevalence rates from different studies. Afterward, an
inverse relationship was found between the study weight and its variance. The Q
statistic and the I
Forest Plot of the prevalence of acute cardiac injury in COVID-19 patients. Each square shows effect estimate of individual studies with their 95% CI. Size of squares is proportional to the weight of each study in the meta-analysis. In this plot, studies are shown in the order of publication date and first author’s names (based on a random effects model).
In order to perform the analysis, we used the PRISMA checklist (Liberati et al., 2009). Initially, 137 studies were identified through database searches, and 44 additional studies were also included through other sources (e.g., the reference list of review articles). Of these 181 studies, 61 studies were excluded due to duplication. After screening the abstracts, 55 other articles were excluded as well (based on exclusion criteria, listed in the Methods Section, e.g., non-English articles, review articles, and non-human studies). Therefore, the full-text of 85 remaining studies were evaluated, and 28 other studies were excluded due to various reasons (e.g., lack of sufficient data). After carefully reviewing the selected studies, 30 published articles carried out from February, 2020 to April, 2020 were included for further analyses (Fig. 1 and Table 1).
Pooling all the extracted data together, the total number of COVID-19 positive
patients was 6,389, including 1,402 patients with ARDS, 494 patients with acute
cardiac injury, and 418 patients with AKI. The selected studies have been
primarily conducted in China, except for one, which was carried out in the United
States. According to the average value calculated from the articles reporting the
median age of the subjects, the mean age of the patients was 57.64 years, while
45.29% of the patients were female (95% CI: 42.61% to 47.99%; I
Our meta-analysis revealed that the most common symptoms of COVID-19 were fever:
84.30% (95% CI: 77.13-90.37; I
Cardiac and renal complications were also assessed in these patients. In
addition, the most common respiratory complication was ARDS with 33.15% (95%
CI: 23.35-43.73; I
Forest Plot of the prevalence of acute kidney injury (AKI) in COVID-19 patients. Each square shows effect estimate of individual studies with their 95% CI. Size of squares is proportional to the weight of each study in the meta-analysis. In this plot, studies are shown in the order of publication date and first author’s names (based on a random effects model).
Number of study | Prevalence (%) | 95% CI | I |
P Value for heterogeneity | |
Mortality | 22 | 12.29 | (6.20-19.99) | 98.29 | 0.00 |
Mortality in China | 21 | 11.20 | (5.36-18.75) | 98.34 | 0.00 |
Mortality in USA | 1 | 52.38 | (29.78-74.29) | ||
Gender | |||||
Female | 29 | 45.29 | (42.61-47.99) | 74.56 | 0.00 |
Male | 29 | 54.72 | (52.04-57.39) | 74.33 | 0.00 |
Clinical Manifestations | |||||
Fever | 24 | 84.30 | (77.13-90.37) | 97.74 | 0.00 |
Cough | 25 | 63.01 | (57.63-68.23) | 93.73 | 0.00 |
Dyspnea | 23 | 37.16 | (27.31-47.57) | 98.32 | 0.00 |
Fatigue | 21 | 34.22 | (26.29-42.62) | 97.29 | 0.00 |
Diarrhea | 18 | 11.47 | (6.96-16.87) | 95.58 | 0.00 |
Complications | |||||
Respiratory | |||||
ARDS | 27 | 33.15 | (23.35-43.73) | 98.56 | 0.00 |
Cardiovascular | |||||
Acute Cardiac injury | 15 | 15.68 | (11.01-20.97) | 91.36 | 0.00 |
Arrhythmia | 5 | 16.64 | (9.34-25.50) | 92.29 | 0.00 |
Heart failure | 4 | 11.50 | (3.45-22.83) | 89.48 | 0.00 |
Renal | |||||
AKI | 21 | 9.87 | (6.18-14.25) | 95.64 | 0.00 |
Based on the primary analysis, the mortality rate was calculated as 21.70%
(95% CI: 12.39-32.70; I
According to the primary analysis, the prevalence of acute cardiac injury was
15.68% (95% CI: 11.1-20.97; I
Begg’s funnel plot for publication bias.
Fig. 4. shows the Begg’s funnel plot for the studies related to cardiac and renal injuries in COVID-19 patients. The interpretation of this plot showed no sign of publication bias in the included studies (P = 0.08). Accordingly, this result implies that reports have been published with both positive and negative results (Fig. 4).
Since late 2019, a novel coronavirus outbreak, i.e., coronavirus disease 2019 (COVID-19), has emerged and spread around the world (Ghebreyesus, 2020). The city of Wuhan in China was the first infected area and the starting point of this pandemic (J. Li and Xu, 2020). It is noteworthy that COVID-19 is highly transmittable and has great ability to infect a cluster of cases (Chan et al., 2020).
Earlier in the pandemic, many patients did not have outpatient testing and diagnosis; therefore, they presented late and had the diagnosis made in the hospital (McCullough and Arunthamakun, 2020).
To date, no definite treatment has been discovered for COVID-19. The current recommendations regarding this disease include a number o reduce the risk of infection (N. Chen et al., 2020; W. Liu et al., 2020).
It has been shown that home treatment for COVID-19 immediately following the onset of symptoms is likely to significantly reduce hospitalizations, critical complications, and death (McCullough et al., 2020). Unfortunately, in severe cases of COVID-19, the disease can cause acute respiratory distress syndrome (ARDS), which is one of the deadliest complications of COVID-19, and can consequently contribute to death. According to Tian et al. (Tian et al., 2020), the prevalence rate of severe and mild outcomes was 18% of severe cases, while the remaining 82% were common cases (i.e., mild cases (73.3%), non-pneumonia cases (4.2%), and asymptomatic cases (5.0%)).
Based on very recent evidence, SARS-CoV-2 reactivation is also possible. Many factors can affect this viral reactivation, including virologic factors and immunosuppressive therapies. Virologic factors include SARS-CoV-2 genotype and viral load. Viral load can also determine disease severity and treatment response (Zou et al., 2020). Immunosuppressive agents, as commonly used agents, can generally inhibit immune functions in different ways. For instance, steroids can suppress interleukin production and lead to impaired cell-mediated immunity (Löwenberg et al., 2007). Therefore, immunosuppressive agents can be considered as a potential predisposing factor for SARS-CoV-2 reactivation (Ye et al., 2020). However, as these findings are limited to a group of 3 patients, further studies are highly recommended on the mechanism of SARS-CoV-2.
Our meta-analysis was conducted on data extracted from 30 previously published
studies. These studies collected data from laboratory-confirmed COVID-19
patients, and they were mainly conducted in hospitals in China. According to
previous reports during SARS-CoV and MERS-CoV outbreaks, these coronaviruses have
been shown to affect men in higher numbers than women (Badawi and Ryoo, 2016; Channappanavar et al., 2017). Moreover, regarding COVID-19, J. Yang et al. (2020) revealed that men were at a greater risk compared to women. The results
of our study also confirm findings that indicate SARS-CoV-2 has a higher risk in
men (54.72% (95% CI: 52.04-57.39; I
We also extracted data on the most frequent symptoms of COVID-19. According to
our analysis, fever with 84.30% (95% CI: 77.13-90.37; I
As we looked at the included studies, we realized that the most reported
complication was ARDS, which had a33.15% prevalence rate (95% CI: 23.35-43.73;
I
As it has already been proved that respiratory tract infections are associated with a high risk of vascular diseases (e.g., artery and venous thrombosis), clotting changes and further thrombosis are also predictable in SARS-CoV-2. According to previous reports, 10% of patients with pneumonia may experience a myocardial infarction (MI) and ischemic stroke (less frequent) (Cangemi et al., 2014; Violi et al., 2020). In addition, several studies have reported that the elevation of troponin level in COVID-19 patients is associated with poor outcomes (Lippi et al., 2020). This elevated troponin level may have different meanings in COVID-19 patients (Zimmermann et al., 2015), including pulmonary embolism (PE), type 1 and 2 MI, myocarditis, non-specific myocardial injury, and impaired renal function (Thygesen et al., 2018). It seems that elevated natriuretic peptide level is a non-specific sign, and thrombosis must be considered in an appropriate clinical context (Bikdeli et al., 2020). On the other hand, deep vein thrombosis (DVT) has not been reported yet. In addition to pneumonia, sepsis can also lead to systemic coagulation abnormalities, e.g., clotting activation and anticoagulant inhibition (Violi et al., 2020). The pathogenesis of hypercoagulability in COVID-19 is not yet identified (Singhania et al., 2020).
Acute myocardial injuries in patients with COVID-19 include arrhythmias, heart
failure, cardiac arrest, acute coronary syndromes, cardiomyopathy, myocarditis,
cardiogenic shock, pericarditis, and pericardial effusion (NICE, 2020). In
our analysis, the prevalence rate of arrhythmia was estimated at 16.64% (95%
CI: 9.34-25.5; I
Markers that can be useful in the diagnosis of an acute myocardial injury
include high sensitivity troponin I (hs-cTnI) or T (hs-cTnT), and NT-proBNP. In
this regard, performing an electrocardiogram (ECG) can also be helpful as ECG
changes show myocardial ischemia. Moreover, the troponin level has diagnostic
value as inflammatory responses of the heart to severe illness can lead to
elevated troponin levels (NICE, 2020). In addition, the prevalence rate of
AKI was 8.40% (95% CI: 5.15-12.31; I
Acute cellular injury due to SARS-CoV-2 fibroblast, pericytes, or cardiomyocyte infection via ACE2-mediated entry, and thus viral replication is still a theory and has not been proven yet. Analyzing histological samples has shown the direct viral infection of the conductive heart cells and myocardium with SARS-CoV-1 (Clerkin et al., 2020; Zhao et al., 2001; Zhou et al., 1982). Previous experiences of acute myocarditis with the substitute viruses indicate that direct cell damage is associated with the combination of cardiotropic viral entry into the myocytes and delayed innate immune responses which may cause diffused or focal myocardial necrosis (Cooper Jr, 2009). A few days into this direct cellular damage, necrosis and edema may cause clinical symptoms and contractile dysfunction (Cooper Jr, 2009). If correct in COVID-19, these secondary damages can suddenly manifest as clinical symptoms after a few days of persistence (Hendren et al., 2020).
Cardiovascular comorbidities are often seen among hospitalized patients, and they are related to an increase in cardiovascular outcomes and mortality rates. Recent clinical studies on COVID-19 demonstrate that about 33% of the hospitalized COVID-19 patients have at least one comorbidity, e.g., diabetes (20%), hypertension (15%), or cardiovascular disease (15%). All these underlying diseases had an association with an increase in the mortality rate (7.3%, 6%, and 10.5%, respectively) (N. Chen et al., 2020; Madjid et al., 2020). Common cardiac outcomes reported in COVID-19 patients due to treatment side-effects or myocardial injury include arrhythmias, myocarditis, and acute coronary syndromes. Patients with cardiovascular injury, i.e., those with an increased troponin level, have a higher mortality rate and prevalence of ARDS (Monsuez, 2020; Shi et al., 2020b).
As noted earlier, the main focus of this study was to determine the most
frequent respiratory and non-respiratory complications in COVID-19 patients,
which can help in identifying the causes of the worst outcomes and death in these
patients. On March 3, 2020, the mortality rate of COVID-19 was reported as 3.4%
by World Health Organization (WHO) (WHO, 2020c), while according to our analysis,
the mortality rate was 12.29% (95% CI: 6.20-19.99; I
COVID-19 infection has a high morbidity rate, especially in elderly patients. These severely-ill patients require supportive medical care, which can place a huge burden on the healthcare system in different countries. There are several complications that can put these patients in critical conditions. The most frequent clinical outcome of COVID-19 involves respiratory complications, in particular ARDS. There are also a number of non-respiratory outcomes in COVID-19 patients. Accordingly, while these outcomes are less common compared to respiratory outcomes, they can have fatal effects on patients. Two of the most frequent non-respiratory complications in COVID-19 patients are acute cardiac injury and AKI. This information gives clinicians a better insight into what they are fighting. Since these data are being updated on a daily basis, further evaluation of these clinical outcomes is recommended.
K.V. and M.F. conceived of the presented idea, searched electronic databases and collected the data. K.V. wrote the manuscript. F.S. performed the computation and analytical methods. M.H. and M.R supervised the findings of this work. F.S., A.M., M.H. and A.P. critically revised the manuscript. All authors discussed the results and contributed to the final manuscript.
This study was carried out under the approval of ethics committee of Shahid Beheshti University of Medical Sciences (IR.SBMU.RETECH.REC.1399.083).
The authors are grateful to Shahid Beheshti University of Medical Sciences, Tehran, IR Iran for their collaborative efforts.
All data generated or analyzed during this study are included in published articles available in table 1.
The authors declare that they have no competing interests.