key: cord-286631-3fmg3scx authors: Pormohammad, Ali; Ghorbani, Saied; Khatami, Alireza; Farzi, Rana; Baradaran, Behzad; Turner, Diana L.; Turner, Raymond J.; Bahr, Nathan C.; Idrovo, Juan‐Pablo title: Comparison of confirmed COVID‐19 with SARS and MERS cases ‐ Clinical characteristics, laboratory findings, radiographic signs and outcomes: A systematic review and meta‐analysis date: 2020-06-05 journal: Rev Med Virol DOI: 10.1002/rmv.2112 sha: doc_id: 286631 cord_uid: 3fmg3scx INTRODUCTION: Within this large‐scale study, we compared clinical symptoms, laboratory findings, radiographic signs, and outcomes of COVID‐19, SARS, and MERS to find unique features. METHOD: We searched all relevant literature published up to February 28, 2020. Depending on the heterogeneity test, we used either random or fixed‐effect models to analyze the appropriateness of the pooled results. Study has been registered in the PROSPERO database (ID 176106). RESULT: Overall 114 articles included in this study; 52 251 COVID‐19 confirmed patients (20 studies), 10 037 SARS (51 studies), and 8139 MERS patients (43 studies) were included. The most common symptom was fever; COVID‐19 (85.6%, P < .001), SARS (96%, P < .001), and MERS (74%, P < .001), respectively. Analysis showed that 84% of Covid‐19 patients, 86% of SARS patients, and 74.7% of MERS patients had an abnormal chest X‐ray. The mortality rate in COVID‐19 (5.6%, P < .001) was lower than SARS (13%, P < .001) and MERS (35%, P < .001) between all confirmed patients. CONCLUSIONS: At the time of submission, the mortality rate in COVID‐19 confirmed cases is lower than in SARS‐ and MERS‐infected patients. Clinical outcomes and findings would be biased by reporting only confirmed cases, and this should be considered when interpreting the data. During the last two decades, coronaviruses have been recognized as one of the most critical human pathogenic viruses that affect global health and cause concern in the world health system. 1 Coronavirus is classified into four genera: alpha, beta, delta, and gamma. Major (about 4% mortality). 8 There is no vaccine or targeted treatment currently available for COVID-19 infection. Treatment is mostly supportive, although multiple experimental antiviral medications are being evaluated. 9, 10 Thus, prevention and rapid diagnosis of infected patients are crucial. The trigger for rapid screening and treatment of COVID-19 patients is based on clinical symptoms, laboratory, and radiographic findings that are similar to SARS and MERS infections. In this study, we attempted to distinguish the clinical symptoms, laboratory findings, radiographic signs, and outcomes of confirmed COVID-19, SARS, and MERS patients. All findings are compared to determine unique features among each of them. These data could be helpful in the early diagnosis and prevention of infection as well as providing more reliable epidemiological data on a large-scale for health care policies and future studies. This study was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Statement (PRISMA) guidelines, and it has been registered in the PROSPERO database (ID 176106). 11 We searched all studies published up to 28 February 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," "SARS Virus," "severe acute respiratory syndrome coronavirus 2", "Coronavirus Infections," "Middle East Respiratory Syndrome Coronavirus," and all their synonyms like "Wuhan Coronavirus," "SARS-CoV-2," and "COVID-19," "2019-nCoV" and MERS. Moreover, we searched for unpublished and grey literature with Google scholar, Centre 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 were written in English or Chinese languages; the latter was translated by https://translate.google.com/. Additional search strategy details are provided in Table S1 . Duplicate studies were removed using EndNote X7 (Thomson Reuters, New York, NY, USA). Records were initially screened by title and abstract by independently four authors (AP, SG, AK, and RF). The full-text of potentially eligible records was retrieved and examined, and any discrepancies were resolved by consensus. Studies had to fulfill the following predetermined criteria to be eligible for inclusion in our meta-analysis. All case-control, cross-sectional, cohort studies, case reports, and case series peer-reviewed 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. Studies were excluded if they did not report the number of confirmed cases. Letters to the editor, individual case reports, review articles, and news reports were also excluded. Duplicate information from the same patient was combined and counted as a single case when the data was reported twice. All COVID-19 included publications were published in 2020, and all patients were from China. The following items were extracted from each article: first author, center and study location in China, countries, sample collection time, patient follow-up time, the reference standard for infection confirmation, number of confirmed cases, study type, and all demographic, clinical, laboratory data, and risk factor data. Three of our authors (SG, AK, and RF) independently extracted data, and all extracted data were checked randomly by another author (AP); differences were resolved by consensus. Quality assessments of studies were performed by two reviewers independently according to the Critical Appraisal Checklist recommended by the Joanna Briggs Institute, 12 and disagreements were resolved by consensus. The checklist is composed of nine questions that reviewers addressed for each study. The "Yes" answer to each question received one point. Thus, the final scores for each study could range from zero to nine (Table S2 ). Data cleaning and preparation were 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. Depending on the heterogeneity test, we used either random or fixed-effect models for pooled results. In the case of high heterogeneity (I2 > 50%), a random effect model (M-H heterogeneity) was applied, while in low heterogeneity cases (I2 < 50%), a fixed-effect model was used. 13 Percentages and means ± SDs were calculated to describe the distributions of categorical and continuous variables, respectively. P values reflect study heterogeneity with <.05 being significant. We also used the funnel plot, Begg's, and Egger's tests based on the symmetry assumption to detect publication bias ( Figure S1 ). The process of study selection is displayed in Figure 1 Quality assessment of included studies was performed based on the Critical Appraisal Checklist, and the final quality scores of the included studies are represented in Table S2 . In brief, studies by Chen et al, 14 Wang et al, 17 Huang et al, 18 Guan et al, 19 Zhang et al, 24 Cheng et al, 25 Li et al, 28 Xu et al, 30 Figure S2 ). Cough was the second most common symptom presenting in COVID-19 63% (95% CI 55.5-70, P < .001), SARS 54.2% (95% CI 49-59, P < .001), and MERS 61% (95% CI 51-70, P < .001) of patients ( Figure S3 ). Age is an exception, presented in mean age in years. (95% CI 2.7-13, P < .001), or runny nose 6% (95% CI 1-14, P < .001). More detail information about demographics and clinical characterization of COVID-19 (Table S3) , SARS (Table S4) , and MERS patients (Table S5 ) demonstrated in the supplementary material. The greatest risk for COVID-19 patients 69.5% (95% CI 54.5-81, P < .001) up to 28 February 2020, is a history of recent travel to Wuhan, contact with people from Wuhan, or were Wuhan residents, and 24.3% (95% CI 9.6-49, P < .001) had exposure at the seafood market(s (Table S9) , SARS (Table S10) , and MERS patients (Table S11) is demonstrated in the supplementary material. Most COVID-19 confirmed patients required hospitalization 85.4% (95% CI 68-94, P < .001) and 20.6% (95% CI 6.7-48, P < .001) were deemed to be in critical condition. The mortality rate of COVID-19 confirmed cases was 5.6% (95% CI 2.5-12.5, P < .001), SARS 13% (95% 9-17, P < .001), and MERS 35% (95% CI 31-39, P < .001) (Figure 2 ). The laboratory findings showed that among a subset of patients 4.5% increased the percentage value. The actual 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 exact mortality rate will be better understood. Among COVID-19, SARS, and MERS patients, leukocytosis was found in 13.3%, 28%, and 30%, respectively, and leukopenia in 26%, 32%, and 41%, respectively. Most of the patients with coronavirus had abnormal chest radiological findings. On the other hand, runny nose and rhinorrhea are less common symptoms in coronavirus-infected patients, 127 and that it is typically expressed on pulmonary alveolar epithelial cells. 128 Another study reported that following COVID-19 infection deregulated cytokine/chemokine response and higher virus titer causes an inflammatory cytokine storm with lung immunopathological injury. 129 Inflammation related to the cytokine storm in the lungs may then spread throughout the body via the circulation system. COVID-19 patients have been reported to have increased plasma concentration of inflammation-related cytokines, including interleukin (IL)-2,6,7,10, tumor necrosis factor-α (TNF-α), and monocyte chemoattractant protein I (MCP-I) especially in moribund patients. 130 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. Furthermore, this study likely overestimates disease severity due to a lack of screening for asymptomatic or mildly symptomatic individuals and subsequent publication bias related to these factors. Likely, many infected persons have not been detected, thus falsely elevating the rates of hospitalization, critical condition, and mortality. The lower quality analysis and reporting in some of the included publications is another limitation of the study. To prevent language bias, we included reports in languages other than English. Additionally, we searched for a variety of sites and databases to prevent internet platform bias. Using Egger's regression test, we did not find significant publication bias. Journal bias is an issue facing those who carry out a meta-analysis, yet it does not usually affect the general conclusions. 132 However, we cannot reject the occurrence of other biases in this study, such as choice bias, since several journals are not indexed in Embase, Scopus, PubMed, Web of Science, and the Cochrane library and unpublished data from some regions of the world. Fever and cough are the most common symptoms of COVID-19-, SARS-, and MERS-infected patients. The mortality rate in COVID-19 confirmed cases was lower than SARS-and MERS-infected patients. Clinical outcomes and findings may be biased by reporting only confirmed cases, and it should be considered when interpreting the data. 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Empirical assessment of effect of publication bias on meta-analyses Supporting Information section at the end of this article. How to cite this article Comparison of confirmed COVID-19 with SARS and MERS cases -Clinical characteristics, laboratory findings, radiographic signs and outcomes: A systematic review and meta-analysis None. The authors have declared that no conflict of interests.