key: cord-0805720-xsnt8u4o authors: Zhang, Yun‐Jing; Sun, Xi‐Feng; Xie, Bing; Feng, Wen‐Juan; Han, Shi‐Liang title: Exploration of severe Covid‐19 associated risk factor in China: Meta‐analysis of current evidence date: 2021-10-05 journal: Int J Clin Pract DOI: 10.1111/ijcp.14900 sha: 2481512a219258834147647c3a2fcb0c3848dbf8 doc_id: 805720 cord_uid: xsnt8u4o AIM: This meta‐analysis aimed to explore potential risk factors for severe Covid‐19. METHODS: We systemically and comprehensively retrieved the eligible study evaluating clinical differences between severe vs non‐severe Covid‐19. Main effect sizes were demographic characteristics, comorbidities, signs and symptoms, laboratory findings as well as radiological features of chest CT. RESULTS: A total of 2566 Covid‐19 people (771 in the severe group and 1795 in the non‐severe group) from 14 studies were eligible for this meta‐analysis. It was demonstrated that older age and males were more likely to have severe Covid‐19. Patients with underlying comorbidities, such as hypertension, diabetes, heart disease and COPD were significantly more susceptible to severe Covid‐19. Patients with dyspnoea were more likely to be severely ill. Depressed total lymphocytes were observed in this article. Meanwhile, although reticulation (30.8%), intrathoracic lymph node enlargement (20.5%) and pleural effusions (30.8%) were relatively infrequent, meta‐analysis revealed that patients with these presentations in chest CT were associated with increased risk of severe Covid‐19. CONCLUSIONS: There are significant differences in clinical characteristics between the severe and non‐severe Covid‐19 patients. Many factors are related to the severity of the disease, which can help clinicians to differentiate severe patients from non‐severe patients. The present review was conducted strictly according to "Handbook for systematic Reviews of Interventions Version 5.1.0." 13 First, we searched for related literature with keyword of "2019-nCoV," "Covid-19" and "SARS-CoV-2" on PubMed and web of science. No restriction was used so that all the possible studies would be systemically checked. Then, Additional articles were retrieved by screening the reference lists of the included studies. The literature search was last updated (10 April 2020) to ensure a comprehensive investigation. This is a meta-analysis that collected data from published papers. Thus, ethics approval was not necessary. All search items were evaluated for eligibility by two reviewers (YJ Zhang and SL Han). Consensus was reached by negotiation. 13 To be included in the final review, the following criteria should be met: 1. Type of studies: Randomised or non-randomised controlled trials, prospective or retrospective cross-sectional studies; Infection by the National Health Commission (Trial Version 5). 10 Case reports, letters to editor and correspondence that did not reported explicit data were excluded. Articles unrelated to the aim of our topic and published repeatedly were also excluded. When two or more articles reported on overlapping patients, only the article with the largest sample size was included. Firstly, two investigators (YJ Zhang and SL Han) independently extracted data using a prior structured study recording form. We extracted the following study design characteristics: first author name, year of publication, patient source, study design, sample volume, baseline demographic characteristics of patients, comorbidities, signs and symptoms, laboratory findings and outcomes about abnormal chest computer tomography (CT). Then double-check procedure was performed to ensure the accuracy of the data extracted. At last, a manager (XF Sun) inputted the extracted data into a spreadsheet. Meta-analysis was performed on the crude data extracted from the text. We calculated the weighted mean difference (WMD) for continuous outcomes and the odds ratio (OR) for the dichotomous Studies were considered for this review if they compared the clinical characteristics across severe Covid-19 vs nonsevere Covid-19 and were published before April 10, 2020. Publication searches were carried out using the electronic databases Pubmed and Web of science. 14 eligible studies were included in this article. Older males presenting with dyspnoea as well as lymphopenia should be more cautious as more likely to have severe Covid-19. Patients with hypertension, diabetes, heart disease and COPD were associated with an increased risk of severe Covid-19. Patients with reticulation, intrathoracic lymph node enlargement and pleural effusions in chest CT were more likely to be severely ill. data, along with the 95% confidence intervals (CIs). In the absence of reported standard errors, we calculated the standard error of mean difference according to the methods described in Cochrane Handbook. 13 Prior to analysing the data, heterogeneity was assessed by the Cochran Q test along with visual inspection of the forest plot. Then, it was quantified by the I 2 test. A fixed-effect model was used when the effects were assumed to be homogenous (P > .05 or I 2 < 50%). 13 However, given that the clinical settings differed across studies, we assumed the presence of heterogeneity and used random-effects model in all subsequent analyses, for the outcome of which were more conservative as they considered differences both within and among studies in calculating the error terms used in the analysis. 13 Funnel plots were employed for detection of publication bias, in which the effect sizes (eg, OR or WMD) are plotted on the horizontal axis and its variance (eg, the standard error of the log effect) on the vertical axis. Bias was revealed if the plots were asymmetrical about the pooled value. All statistical analyses were done with Review Manager 5.3.5 (Cochrane Collaboration, Oxford, UK). Results were regarded as statistically significant if P < .05. The search strategy yielded 6721 citations (3516 from Pubmed and 3205 from ISI web of science). All the documents were selected strictly according to the criteria described above. Subsequent scrutiny of the titles and abstracts excluded 6677 of these articles on the grounds that they were irrelevant or duplicative for the aim of this meta-analysis. 21 articles were further excluded as irrelevant to our purposes. The full publications were obtained for the remaining 23 articles. Based on the inclusion criteria, 9 articles were further excluded, leaving 14 studies eligible for this article. [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] Specially, two articles were excluded either for overlapping patients with included studies, 3 or for they defined the degree of severity of Covid-19 with the different criteria. 3, 28 One article reporting results regarding 55 cases of 2019-nCoV were also excluded, since it did not report the origin of the patients. 29 For three articles reporting outcomes in Wuhan Tongji hoptital, 17,30,31 three articles reporting outcomes in Jiangsu Province 27,32,33 and three articles reporting outcomes in Zhejiang province, [33] [34] [35] only studies with the largest simple volume were included in this article. 17, 27, 33 No additional articles were retrieved from the citation list of included studies. The details of study selection flow were explicitly described in Figure 1 Finally, 14 retrospective cross-sectional studies on different populations, from which conclusions could be drawn from the origin of patients, were suitable for this meta-analysis [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] : four multi-centre studies [19] [20] [21] 27 and ten single-centre studies. [14] [15] [16] [17] [18] [22] [23] [24] [25] [26] All the eligible studies were from China and published in 2020. As a result of the instinctive design of this meta-analysis, no randomised or nonrandomised controlled trials were eligible for this meta-analysis. A total of 2566 individuals were identified (771 in the severe group and 1795 in the non-severe group). 14 Twelve studies reported 409 males out of 747 patients in the severe group (54.8%) and 843 males in 1724 out of the Non-severe group (48.9%). [14] [15] [16] [17] [19] [20] [21] [22] [23] [24] [25] 27 The pooled OR was 1.30 (95% CI: 1.07 to 1.57), demonstrating that males were associated with significantly increased susceptibility of severe Covid-19 (P = .007). Consistently, of 14 studies reporting the age of patients, [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] it was revealed that older individuals were more susceptible to severe Covid-19 (WMD: 11.12, 95% CI: 6.70 to 15.55, P < .00001). The outcomes were explicitly expressed in Table 2 . In 7 eligible trials, 15 Of 7 studies reporting data on blood tests, lymphocyte count was revealed in all 7 studies. 15 and monocyte count. 15, 17, 27 The outcomes were explicitly revealed in Table 2 . Among 5 studies reporting data on chest CT, 15 Publication bias statistics were determined by a funnel plot. The plot demonstrated asymmetry of the pooled effects where publication bias may exist (Figure 2 ). Up to now, this is the first meta-analysis to explore the clinical, lab- Through the statistical analysis, it was demonstrated that patients in the severe Covid-19 group were older and had a greater number of comorbid conditions (eg, hypertension, diabetes and heart disease) than the non-severe group. Study found that about one-fifth of patients with COVID-19 developed heart disease, which increased the mortality rate. 37 Severe and sudden inflammation of the heart muscle can cause arrhythmias and impair the heart's ability to efficiently pump blood. Therefore, we believe that patients with a history of heart disease and with high blood pressure are at a higher risk of severe Covid-19 and death than the normal individuals. Compromised respiratory status on admission (eg, COPD) was Abbreviations: COPD, chronic obstructive pulmonary disease; OR, odds ratio; WMD, weighted mean difference; 95% Cis, 95% confidence intervals. also associated with severe illness. These suggest that age and comorbidity may be risk factors for poor outcomes. Meanwhile, severe 2019-nCoV infection is more likely to affect males. These data were consistent with the recent report. 38 What's more, our outcome did not support that smoking was associated with the severity of Covid-19 illness. Consistently, Lippi et al conducted a meta-analysis of current evidence and concluded that active smoking does not appear to be significantly associated with an enhanced risk of Covid-19 progression to severe disease, which further confirmed our results. 39 Common symptoms of Covid-19 at onset of illness were fever, dry cough, expectoration, myalgia, fatigue, and dyspnoea. 1 However, some patients presented initially with atypical symptoms, such as diarrhoea and nausea. 40, 41 By statistically combining the data on common signs and symptoms, the incidence of fever, expectoration, headache, fatigue, myalgia and dyspnoea were more common in the severe group than in the non-severe group. However, only the incidence of dyspnoea was statistically different across groups. Pathologic studies on biopsy samples of lung, liver, and heart ob- Thirdly, more and more articles on Covid-19 are being published every day. There might be lots of articles evaluating the clinical differences between severe and non-severe Covid-19 that are not published. And the funnel plot of this meta-analysis revealed that publication bias might exist. Thus, it is necessary for clinicians to interpret our outcomes carefully. In conclusion, older males presenting with dyspnoea, whose blood routine tests revealed lymphopenia should gain more caution for which might be severe Covid-19. Patients with comorbidities, such as hypertension, diabetes and heart disease were more susceptible to severe Covid-19. Compromised respiratory status on admission (eg, COPD) was also associated with severe illness. Specially, although reticulation, intrathoracic lymph node enlargement and pleural effusions were relatively rare, meta-analysis revealed that patients with such presentations in chest CT were more likely to be associated with severe Covid-19. Although lots of risk factors were filtrated in this article, exploration of predicted value of these factors in severe Covid-19 patients was impossible with aggregated data extracted from published studies. Further diagnostic articles evaluating how to differentiate severe from non-severe Covid-19 manifested in chest CT and studies assessing the relationship between clinical characteristics and severity of Covid-19 with the aid of logistic regression analysis are needed. The intent of this statement is to display our idea on severe Covid-19. There are no financial and personal relationships with other people or organisations that could inappropriately influence (bias) our work. The data that support the finding of this study are available on request from the corresponding author upon reasonable request. 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