Clinical features, comorbidities, complications and treatment options in severe and non‐severe COVID‐19 patients: A systemic review and meta‐analysis

Abstract Objectives The aim of this analysis was to assess the prevalence of clinical features, comorbidities, complications and treatment options in the patients with COVID‐19 and compare incidence of these clinical data in severe and non‐severe patients. Design Systemic review and Meta‐analysis. Methods PubMed, Embase, Scopus and Web of Sciences databases were searched to identify relevant papers until 20 July 2020. All studies comparing clinical data of severe and non‐severe patients of COVID‐19 were included. Heterogeneity across included studies was determined using Cochrane's Q test and the I 2 statistic. Results were expressed as odds ratio with accompanying 95% confidence intervals. Results Twelve studies with 3,046 patients were included. The result showed the most prevalent clinical symptoms were fever 88.3%, cough 62.2%, fatigue 39.5% and dyspnoea 31.5%. Further meta‐analysis showed incidence of fever, cough, fatigue and dyspnoea was higher in severe patients. The most prevalent comorbidities were hypertension 22.6%, diabetes 11.5%, cardiovascular disease 10.3% and cancer 2.5%. We found that compared with non‐severe patients, the symptoms, existing comorbidities and complications are prevalent in severe COVID‐19 patients. Future well‐methodologically designed studies from other populations are strongly recommended.

As of 16 August 2020, globally a total of 21,294,845 cases of the COVID-19 have been reported, with death rate of around 3.6% (World Health Organization, n.d.).
Furthermore, the prevalence of hypertension, diabetes mellitus, cardiovascular disease and other comorbidities, as well as complications, also varied between the studies due to the various characteristics of the study populations (Guan, Ni et al., 2020;Huang et al., 2020;Wan et al., 2020;Wang, Hu et al., 2020;Zhang et al., 2020). Although there are many studies regarding the clinical characteristics and comorbidities, of but there are limited studies that compared clinical characteristics, comorbidities, treatment options and complications of severe and non-severe patients. The exponential growth of COVID-19 cases is overwhelming healthcare systems of developing countries with limited sources. Currently, there are no proven vaccines or effective treatment against the virus. Therefore, healthcare workers assessment ability to distinguish between mild and severe COVID-19 cases promptly could help save lives and boost healthcare system. The present systematic review and meta-analysis were undertaken to provide a systemic evaluation and detailed estimate to draw the whole clinical picture of COVID-19 in severe and non-severe cases. This assessment will help frontline healthcare workers for emergency preparedness and response to SARS-CoV-2 and its severe outcomes. The main objectives of our meta-analysis are as follows: • To acquire more accurate conclusions on the clinical features, comorbidities, complications and treatment options among patients with COVID-19.
• To compare clinical features comorbidities, complications and treatment options among severe and non-severe patients.

| Eligibility criteria
The inclusion criteria were as follows: (a) study population: studies with patients diagnosed with (b) comparative studies: studies that compare severe and non-severe cases of and (c) the studies reporting parameters of clinical features, comorbidities, complications and treatment. Non-English studies, letters, case studies, editorials, conference abstracts, vaccination trials studies and articles with abstracts only were excluded. Studies with only paediatric cases were also excluded.

| Information sources and Searching strategies
We conducted this systematic review and meta-analysis accord-

| Data extraction and outcomes
All duplicate studies were excluded by using by EndNote X 8.0 software. The two investigators who performed the literature search also independently extracted the data from included studies.
Disagreements were resolved with a third investigator. Microsoft Excel database was used to record all available information including variables: author, date, age, gender and number of participants in severe and non-severe groups. The prevalence of clinical symptoms such as fever, cough, fatigue, dyspnoea, sore throat, headache, chest pain, comorbidities, complications and treatment options used including antiviral drugs, antibiotics, glucocorticoids, oxygen support, continuous renal replacement therapy (CRRT), non-invasive ventilation (NIV) and invasive mechanical ventilation (IMV) was also recorded.
The primary outcome measure was to compare the prevalence of clinical feature, comorbidities, complications and treatment options in severe cases (ICU cases, patients with elevated TnT level, patients with cardiac injury, cases with SpO2 < 90% and patients with ARDS as the second choice if severe data were not provided) and non-severe (non-ICU cases, patients normal TnT level, patients without cardiac injury, cases with SpO2 ≥ 90% and patients without ARDS as the second choice if non-severe data were not provided).

| Risk of bias assessment
The potential risk of bias of the included studies was assessed using the MINORS, a methodological index for non-randomized studies.

| Statistical analysis of data
All statistical analyses were performed with OpenMeta Analyst version 10.10 (www.cebm.brown.edu/open_meta), a free open-source program and RevMan software version 5.3. Meta-analysis of proportions (and 95% CI) was calculated for the clinical symptoms, comorbidities, complications and treatment options. Binary random effect model was used as clinical data are varied across study population.
The prevalence of clinical symptoms, comorbidities, complications and treatments was illustrated with forest plots. With OR (Odds ratio) as the effect quantity, we used Mantel-Haenszel test with fixed or random effect for further meta-analysis of the clinical symptoms, comorbidities, complications and treatments with statistical differences in severe and non-severe patients. We evaluated heterogeneity across the studies by using the I 2 statistic and Cochran's Q test (Higgins et al., 2003). When I 2 < 50%, the fixed effect model was used, while random effect model was used when

| RE SULTS
A total of 476 papers were retrieved from the four databases, of which 162 studies were removed as duplicates. Remaining 314 studies were screened by title and abstract and, 293 studies were discarded according to exclusion criteria. After evaluating the 21 full texts, 9 studies were excluded due to presenting data that were irrelevant to our aim. Finally, 12 articles (Guan, Ni et al., 2020;Huang et al., 2020;Wan et al., 2020;Wang, Hu et al., 2020;Zhang et al., 2020), (Guo et al., 2020;Liu et al., 2020;Shi et al., 2020;Tian et al., 2020;Wang, Yang et al., 2020;Wu, Li et al., 2020) met the inclusion criteria but some of the required information of severe and non-severe cases was not reported in all of the articles. A flow chart of study selection is shown in (Figure 1).
All included studies were published in 2020 with different sample size that ranged from 41-1,099 patients. The risk of bias of eligible studies is presented in (Table 1). The 12 included studies scored between 18-21, with the mean overall score for all comparative studies being 19.6. According to the MINORS assessment, all studies were considered to have a low risk of bias for selection. The main characteristics of eligible studies are summarized in (Table 2).

| Demographic characteristics
The overall average age (±SE) of patients across 12 studies was 50 ± 2 years (range: 38-64 years). Men (53.8%, 95% CI: 50.3-57.3) were more likely to be infected than women counterparts. The proportion of severe patients in our study was 25.9% (95% CI: 20.8-31.0; Table 3). Chi-square test showed that there was significant difference in gender between severe and non-severe groups (p < .05).
We also compared the prevalence of clinical features between severe patients and non-severe patients. For clinical features, the heterogeneity test results, I 2 varied from 50%-86%. Thus, the fixed effect model was adopted for further analysis. The result showed that the incidence of fever, cough, sore throat and headache in severe patients were higher than non-severe group, but without sta-

| Treatment
With regard to the treatment options, antiviral drugs ( Figure 5).

| D ISCUSS I ON
The continued occurrence of SARS-CoV-2 infection globally is deeply concerning. Despite outbreak prevention and control measures, there is no substantial pandemic change, even after seven months from the onset of SARS-CoV-2 outbreak still cases of this infection are increasing at an alarming rate. The present meta-analysis showed that men were more likely to be infected with COVID-19 than female counterparts. Previous studies also showed male predominance in incidence of MERS-CoV and SARS-CoV infections (Badawi & Ryoo, 2016;Leong et al., 2006). The reason for this might be females are relatively resistant to virus infections as they have stronger innate and adaptive immune responses (Klein & Flanagan, 2016).
The most prevalent symptoms of COVID-19 were fever, cough, fatigue, dyspnoea and sore throat. The clinical picture of SARS-CoV-2 is similar to SARS-CoV and MERS-CoV. However, diarrhoea F I G U R E 2 Meta-analysis for the proportion of fever, cough, fatigue and dyspnoea in COVID-19 cases. Weights are calculated from binary random-effects model analysis. Values represent proportions of these clinical features in the COVID-19 patients and 95% CI. Heterogeneity analysis was carried out using Q test, the among studies variation (I 2 index). Forest plots depict the comparison of the incidences of clinical features in severe and non-severe patients which was prevalent in patients with MERS-CoV or SARS-CoV was rare in case of COVID-19 (Assiri et al., 2013;Fan et al., 2006). Recent study from Singapore and United States of America also revealed that in patients with SARS-CoV-2 infection the main symptoms were fever, cough, dyspnoea and sore throat and so on (Arentz et al., 2020;Young et al., 2020). The incidence of fever, cough, dyspnoea, fatigue, sore throat and headache in severe patients was more common than non-severe group. Previous studies also showed that compared with non-severe patients, these symptoms were more common in severe patients (Guan, Ni et al., 2020;Huang et al., 2020;Wan et al., 2020;Wang, Hu et al., 2020).
Results of our meta-analysis demonstrated that the most prevalent comorbidities were hypertension, diabetes and cardiovascular disease. In addition to these comorbidities, cancer, chronic kidney disease and chronic obstructive pulmonary disease (COPD) were also obvious in some patients. The comorbidities identified in our study are in line with previous studies Zumla et al., 2015). A meta-analysis of 637 MERS-CoV cases by Badawi et al suggested that hypertension, diabetes and cardiac disease were prevalent in most patients (Badawi & Ryoo, 2016  . Our finding implies that comorbidities should be taken into account when predicting the prognosis in patients with COVID-19. With regard to the complication, 22.2% of the patients presented with ARDS, while shock and acute kidney injury were less prevalent. Excessive inflammation reactions with a cytokine storm leading to ARDS were prominently seen in SARS and MERS cases (Kim et al., 2016;Lew et al., 2003). Cytokines including TNFα, IL-1β, IL-2, IL-6, IFNα, IFNβ, IFNγ and MCP-1 released by cytokine storm induce immune cells to produce free radicals which are major causes of ARDS (Tisoncik et al., 2012). Patients with COVID-19 pneumonia who had developed ARDS had significantly higher cytokines contributing to cytokine storm . patients (Guan, Ni et al., 2020;Huang et al., 2020;Wan et al., 2020;Wang, Hu et al., 2020).

F I G U R E 4
Meta-analysis for the proportion acute respiratory distress syndrome, acute kidney injury and shock in COVID-19 cases. Weights are calculated from binary random-effects model analysis. Values represent proportions of these complications in the COVID-19 patients and 95% CI. Heterogeneity analysis was carried out using Q test, the among studies variation (I 2 index). Forest plots depict the comparison of the incidences of these three complications in severe and non-severe patients Previous studies have demonstrated that in patients with SARS and MERS corticosteroids did not improve survival, but resulted in high complications and delayed viral clearance (Arabi et al., 2018;Lee et al., 2004). In our study, glucocorticoid was used in about 36% of patients and it was given to more severe cases. Corticosteroids might have been used to tackle a cytokine storm, to prevent acute lung injury and ARDS (Sanders et al., 2020;Tisoncik et al., 2012

| CON CLUS ION
Based on our results, frontline healthcare professionals such doctors and nurses should be aware that the severe patients might manifest more severe clinical symptoms than the general population. People with pre-existing comorbidities will need to be considered as a high-risk group for COVID-19. Our findings may contribute to a better understanding of patient at risk and can help to improve the assessment and management of severe patients. Furthermore, severity of COVID-19 has excreted immense pressure on healthcare system of developing countries and early identification of patients at risk for severe illness may reduce the burden on healthcare system.

F I G U R E 5
Meta-analysis for the proportion of antiviral therapy, antibiotic therapy, glucocorticoids and oxygen therapy in COVID-19 cases. Weights are calculated from binary random-effects model analysis. Values represent proportions of the 4 therapies in the COVID-19 patients and 95% CI. Heterogeneity analysis was carried out using Q test, the among studies variation (I 2 index). Forest plots depict the comparison of the incidences of the 4 therapies in severe and non-severe patients

ACK N OWLED G EM ENT
We would like to thank Dr. Guichuan Huang for his assistance in this research.

CO N FLI C T O F I NTE R E S T
All authors disclose no conflict of interest.

AUTH O R CO NTR I B UTI O N S
Mohan Giri, Anju Puri: Study design. Mohan Giri, Anju Puri, Ting Wang, Shuliang Guo: Data collection and analysis. In addition, all authors read and approved the final manuscript.

E TH I C A L A PPROVA L
The study does not require ethical approval because the meta-analysis is based on published research.

DATA AVA I L A B I L I T Y S TAT E M E N T
The data used to support the findings of this study are available from the corresponding author upon request.