key: cord-0837211-rg2y2p90
authors: Wang, Zhufeng; Deng, Hongsheng; Ou, Changxing; Liang, Jingyi; Wang, Yingzhi; Jiang, Mei; Li, Shiyue
title: Clinical symptoms, comorbidities and complications in severe and non-severe patients with COVID-19: A systematic review and meta-analysis without cases duplication
date: 2020-11-25
journal: Medicine (Baltimore)
DOI: 10.1097/md.0000000000023327
sha: 771f8434a4b859362d5edb8c39a02c3795d099d1
doc_id: 837211
cord_uid: rg2y2p90

BACKGROUND: The pandemic of COVID-19 poses a challenge to global healthcare. The mortality rates of severe cases range from 8.1% to 38%, and it is particularly important to identify risk factors that aggravate the disease. METHODS: We performed a systematic review of the literature with meta-analysis, using 7 databases to identify studies reporting on clinical characteristics, comorbidities and complications in severe and non-severe patients with COVID-19. All the observational studies were included. We performed a random or fixed effects model meta-analysis to calculate the pooled proportion and 95% confidence interval (CI). Measure of heterogeneity was estimated by Cochran's Q statistic, I(2) index and P value. RESULTS: A total of 4881 cases from 25 studies related to COVID-19 were included. The most prevalent comorbidity was hypertension (severe: 33.4%, 95% CI: 25.4%–41.4%; non-severe 21.6%, 95% CI: 9.9%–33.3%), followed by diabetes (severe: 14.4%, 95% CI: 11.5%–17.3%; non-severe: 8.5%, 95% CI: 6.1%–11.0%). The prevalence of acute respiratory distress syndrome, acute kidney injury and shock were all higher in severe cases, with 41.1% (95% CI: 14.1%–68.2%), 16.4% (95% CI: 3.4%–29.5%) and 19.9% (95% CI: 5.5%–34.4%), rather than 3.0% (95% CI: 0.6%–5.5%), 2.2% (95% CI: 0.1%–4.2%) and 4.1% (95% CI: −4.8%–13.1%) in non-severe patients, respectively. The death rate was higher in severe cases (30.3%, 95% CI: 13.8%–46.8%) than non-severe cases (1.5%, 95% CI: 0.1%–2.8%). CONCLUSION: Hypertension, diabetes and cardiovascular diseases may be risk factors for severe COVID-19.

Since the end of 2019, there's been a surge in cases of COVID-19 with 24,257,989 laboratory-confirmed cases and 827,246 deaths as of August 28 st . COVID-19 causes an adverse influence globally, especially in increasing the burden on healthcare. early risk factors of severe cases during COVID-19 pandemic, which is helpful for precise treatment and prognosis improvement. Notably, previous studies have clarified that patients particularly vulnerable to severe disease are those with preexisting medical conditions such as diabetes, cardiovascular diseases, renal failure, obesity, and immunodeficiency. [5, 6] Wang et al reported 138 cases of COVID-19 and the result indicated that almost half of hospitalized patients had comorbidities, and patients admitted to ICU with comorbidities was twice as high as without comorbidities. [2] To sum up, evaluating the prevalence of underlying diseases is fundamental to mitigate COVID-19 complications. However, this effort has been hindered by the limited number of cases and confounding classification in preexisting studies.

The present study was undertaken to provide a systematic evaluation without cases duplication to compare the proportion of demographic, comorbidities, symptoms, complications and outcomes between severe and non-severe COVID-19 cases. This assessment may aid the public health sector while developing policies for surveillance and response to COVID-19 and its severe outcomes.

To compare the differences in the field of demographic, comorbidities, clinical symptoms, complications and outcomes between severe and non-severe COVID-19. To conclude the potential risk factors to severe COVID-19 patients.

We registered the study protocol with PROSPERO (registration number ID: CRD42020177414) (Supplemental material: study protocol & PRISMA Checklist).

We searched PubMed, Web of Science, Cochrane Library, CBM (Chinese Biomedical), CNKI (China National Knowledge Infrastructure), WanFang, and VIP databases up to March 16, 2020 . The search terms were used as follows: "Wuhan coronavirus" OR "COVID-19" OR "novel coronavirus" OR "2019-nCoV" OR "coronavirus disease" OR "SARS-CoV-2" OR "SARS2" OR "severe acute respiratory syndrome coronavirus 2"; the full search strategy is shown in Supplemental material: search strategy. The search was limited to English and Chinese language. We hand-searched included papers' reference lists and contacted experts in the field to ensure a comprehensive review.

We included studies which:

Examined laboratory-confirmed patients with COVID-19. Examined the demographic, comorbidities (e.g., diabetes, hypertension, cardiovascular disease, etc), clinical symptoms, complications, and outcomes of severe and (or) non-severe patients with COVID-19. Reported mean ± SDs or proportion and 95% confidence interval (95% CI) of these factors. Observational studies.

We excluded papers which:

Did not contribute to any variable (e.g., male, female, diabetes, hypertension, cardiovascular disease, COPD, fever, cough, ARDS, AKI, shock, hospitalization, discharge, death, etc) of this study. (We will include the maximum sample size of the same hospital according to each variable, so as to avoid the duplication of sample size.) Did not provide full-text. Did not publish in either English or Chinese.

After excluding duplicate papers, 1 researcher (ZW) screened the titles and abstracts using the eligibility criteria. Then 2 researchers (HD, CO) assessed the rest full-text articles for eligibility. The Kappa value for study inclusion between them was 0.82, which showed strong consistency. Consensus on the inclusion of all studies was agreed by 2 researchers (HD, CO) with any disagreements resolved in a discussion with researcher (ZW).

Where available, the following information from each article was extracted using a standardized data extracted form: title, study design, study period, location, first author, publication year, sample size of severe or non-severe cases, sex distribution, any comorbidities, diabetes, hypertension, cardiovascular disease, COPD, fever, cough, ARDS, AKI, shock, hospitalization, discharge, death, etc. Particularly, we used the definition of eligible studies as the criteria for the type of disease.

We extracted the counting data as the number of occurrences of an event versus the total number of people reported for that event (n/N). Additionally, we used the mean and standard deviation (SD), or median and interquartile range (or median and range), to record the measurement data.

Two researchers (CO, HD) assessed the risk of bias in individual papers using the Newcastle-Ottawa Scale for assessing the quality of cohort studies and case-control studies. [7] This considered the domains of selection, comparability and ascertainment of the outcome of interest. A study with a score of 0 to 3, 4 to 6 and 7 to 9 was considered as poor, intermediate and high quality, respectively. The Weighted Kappa value was 0.67 on quality rating criteria, and consensus was reached through discussion in cases of disagreement on individual rating criteria.

All analyses were conducted using STATA Version 15. Unit discordance for variables will be resolved by converting all units to a standard measurement for that variable. We conducted analyses by severity (severe vs non-severe). We used a randomeffects model or a fixed-effects model to calculate the pooled proportion or mean and 95% confidence interval (95% CI) of all reported variables. All P values were based on 2-sided tests and were considered statistically significant at P < .050. Measure of heterogeneity, including Cochran's Q statistic and the I 2 index were estimated and reported. The pooled results from a random-Wang et al. Medicine (2020) 99:48 Medicine effects model would be reported when the I 2 > 50% and P heterogeneity < .100, which indicated substantial heterogeneity. Publication bias was checked by visual inspection of funnel plots and tested using Egger's test when ten or more studies reported the variable, and the Egger test with P < .050 was considered to be an indication of substantial publication bias.

We identified 25 studies (Fig. 1 ) describing 4881 patients diagnosed COVID-19 from December, 2019 to March 16, 2020 ( Table 1 ). All included studies were from hospitals in China mainland, with 12 from Hubei, 4 from Chongqing, 3 from Beijing and 1 each from Anhui, Henan, Hunan, Shanxi, Liaoning and Wenzhou. Publication bias was assessed with a funnel plot for the standard error by logit event, with no evidence of bias ( Fig. 2) . Additionally, the Egger test (P = .312) suggested that there was no notable evidence of publication bias. We analyzed 20 variables for the meta-analysis, the pooled results were all presented in detail in Table 2 and Supplementary online content 

The average age was higher in severe cases as compared with nonsevere cases (48.5 vs 38.5, P = .010). The sex ratio (male to female) was 1.33 in severe cases and 0.95 in non-severe cases. Being aged or male were considered as risk factors to severe COVID-19 (relative ratio (RR) = 1.29, 95% CI: 1.12-1.47) (Fig. 3 ).

The proportion of having comorbidities in severe cases was remarkably higher in severe cases (58.4%, 95% CI: 48.8%-67.9%) than non-severe cases (27.6%, 95% CI: 18.6%-36.6%) (P < .050). Meta-analysis showed that in both groups, the most (Fig. 3 ).

The mortality was obviously higher in severe cases than nonsevere cases (30.3% vs 1.5%, P < .050). Severe patients were 2.30 times more likely to die than non-severe patients (RR = 2.30, 95% CI: 2.02-2.63) (Fig. 3 ).

This is the first meta-analysis that avoids the phenomenon of included cases duplication, which compares severe and nonsevere COVID-19 in the field of demographic features, clinical symptoms comorbidities, complications and outcomes. Based on 4881 laboratory-confirmed cases with COVID-19 in mainland China from 25 studies, we found that severe COVID-19 was more likely to occur in male. In terms of comorbidities, patients combining diabetes, hypertension, cardiovascular disease and COPD were more likely to develop severe COVID-19, which was consistent with the findings of Guan Wei-jie et al to some degree. [31] Fever and cough were the main clinical symptoms in both severe and non-severe cases, which was consistent with previous studies. [1, 2, 32] As for complications, ARDS, AKI or shock were much more likely to observed in severe cases, which [8] 2020 Wuhan, China (Jin-Yintan hospital) Retrospective study 463 By Feb. 6, 2020 5 Xiaobo Yang [9] 2020 Wuhan, China (Jin-Yintan hospital) Retrospective study 52 Dec. 2019 to Jan. 26, 2020 6 Xu Shen [10] 2020 Wuhan, China (Zhongnan hospital) Retrospective study 62 Jan. 8, 2020 to Feb. 24, 2020 5 Dawei Wang [2] 2020 Wuhan, China (Zhongnan hospital) Retrospective study 138 Jan. 1, 2020 to Jan. 28, 2020 7 Bai Peng [11] 2020 Wuhan, China (Xiehe hospital) Retrospective study 58 Jan. 29, 2020 to Feb. 26, 2020 6 Peng Yudong [12] 2020 Wuhan, China (Xiehe hospital) Retrospective study 112 Jan. 20, 2020 to Feb. 15, 2020 5 Wen Ke [13] 2020

Beijing, China (The Fifth Medical Center of Chinese PLA General Hospital)

Retrospective study 46 Jan. 20, 2020 to Feb. 8, 2020 4

Yuhuan Xu [14] 2020

Beijing, China (The Fifth Medical Center of Chinese PLA General Hospital)

Retrospective study 59 Jan. 2020 to Feb. 2020 5

Wan Qiu [15] 2020 Chongqing, China (Treatment center) Retrospective study 153 Jan. 26, 2020 to Feb. 5, 2020 5 Yuan Jing [16] 2020 Chongqing, China (Treatment center) Retrospective study 223 Jan. 24, 2020 to Feb. 23, 2020 6 Xiong Juan [17] 2020 Wuhan, China (Renmin Hospital of Wuhan University) Retrospective study 89 Jan. 17, 2020 to Feb. 20, 2020 6 Lu Zilong [18] 2020 Wuhan, China (Renmin Hospital of Wuhan University) Retrospective study 101 Jan. 15, 2020 to Feb. 15, 2020 4 Fang Xiaowei [19] 2020 Anhui, China Retrospective study 79 Jan. 22, 2020 to Feb. 18, 2020 5 Xiao Kaihu [20] 2020 Chongqing, China (San-Xia hospital) Retrospective study 143 Jan. 23, 2020 to Feb. 8, 2020 4 Kunhua Li [21] 2020 Chongqing, China (the Second Affiliated Hospital of Chongqing Medical University)

Retrospective study 83 Jan. 2020 to Feb. 2020 5

Cheng Jiuling [22] 2020 Henan, China Cross sectional 1265 By Feb. 19, 2020 3 Dai Zhihui [23] 2020 Hunan, China Retrospective study 918 Jan. 21, 2020 to Feb. 13, 2020 4 Gao Ting [24] 2020 Shanxi, China (Xianyang central hospital) Retrospective study 11 Jan. 20, 2020 to Feb. 15, 2020 5 Li Dan [25] 2020 Liaoning, China Retrospective study 30 Jan. 22, 2020 to Feb. 8, 2020 6 Chen Chen [26] 2020 Wuhan, China (Tongji hospital) Retrospective study 150 Jan. 2020 to Feb. 2020 5 SiJia Tian [27] 2020 Beijing, China (Emergency center) Retrospective study 262 By Feb. 10, 2020 5 Jin-jin Zhang [28] 2020 Wuhan, China (No.7 hospital of Wuhan) Retrospective study 140 Jan. 16, 2020 to Feb. 3, 2020 5 Chen Min [29] 2020 Hubei, China (the third Renmin hospital of Jianghan university)

Retrospective study 54 Jan. 24, 2020 to Feb. 8, 2020 6

Wenjie Yang [30] was in accordance with the finding on Middle East respiratory syndrome coronavirus (MERS-CoV). [6, 33] Based on results of clinical symptoms, we found a significant difference between severe and non-severe patients with COVID-19 on overall factors. But in clinical practice, it is difficult to conclude whether a patient is more likely to develop severe or non-severe COVID-19 based on such clinical symptoms. Nonetheless, clinical symptoms are undoubtedly essential for the screening of suspected cases.

Based on our results, we found that severe COVID-19 patients may be usually combined with comorbidities on admission especially as diabetes, hypertension and cardiovascular disease, which could affect some key mediators of the host's innate immune response. [33] Previous findings on MERS-CoV also found that people with severe illness were more likely to combine these underlying comorbidities. [33] This can be explained by the phenomenon of cytokine storm that a variety of cytokines gather in the body fluids. Early studies of MERS-CoV found that the amount of Th1/Th2 cytokines profile was higher in patients with diabetes, hypertension or cardiovascular disease which was linked with exacerbation of pro-inflammatory state and generation of oxidative stress. [17, [34] [35] [36] [37] [38] Studies have shown that cytokine storm indicate poor prognosis and tissue damage. [10] So far in COVID-19 patients, research has shown that ICU patients had higher plasma levels of IL-2, IL-7, IL-10, GSCF, IP10, MCP1, MIP1A, and TNF-a compared with non-ICU patients. [1] Considering that these cytokines mainly belong to Th1 or Th2 subgroups, we infer that patients with comorbidities, especially those with diabetes, hypertension or cardiovascular disease, are more likely to develop severe COVID-19. Therefore, we suggest that clinicians can pay more attention to patients with comorbidities, which may prevent the development of severe COVID-19 and its progressive complications with suitable care.

Also, it is believed that cytokine storm is also an important cause of ARDS and multiple organ failure in patients with viral infections. [39, 40] Therefore, we considered that patients having diabetes, hypertension or cardiovascular disease on admission were more likely to suffer from potentially fatal complications such as ARDS, AKI and shock during disease progression.

As mentioned on complications of severe and non-severe patients, we found that the incidence of ARDS, AKI and shock were remarkably higher in severe patients. This was also consistent with the conclusion of previous research that secondary pneumonia, ARDS, encephalitis, myocarditis and other potentially fatal complications could occur in severe patients. [6, 33] These severe clinical manifestations caused by the underlying comorbidities can also be seen in other respiratory diseases such as influenza and influenza H1N1. [32, 39, 41] With evaluating the occurrence of complications induced by SARS-CoV-2 infection, it helps us fully understand the adverse impact and disease burden of severe COVID-19.

In general, figuring out differences on comorbidities, clinical symptoms and complications between severe and non-severe patients may provide an evidence base to clinicians through the meta-analysis approach. Besides, due to the similarity between COVID-19 with SARS and MERS to a certain extent, we could draw some experience in the previous studies of SARS and MERS while comparing with the studies of COVID-19 as well. We hope that this assessment may aid the public health sector while developing policies for surveillance and response to COVID-19 and its severe outcomes.

We followed the PRISMA procedure in this meta-analysis for medical evidence searching. Additionally, we excluded the potential repeated cases from the same hospital or region according to every specific variable which we are about to analyze, avoiding to amplify the false effect of some factors by including many duplicate cases.

There are still some limitations in this study. First, all the included studies are conducted in mainland China, so the outcomes may not be suitable for the international situation at present. Second, because of the lack of available data, we could not make a statement of the comparison for geographic region (Wuhan, China vs outside Wuhan), which was designed in the study protocol. Third, there were some differences in the proportion of diabetes, hypertension or cardiovascular diseases between the studies, which may be a source of heterogeneity.

But these results can play a certain reference value and alert role for future epidemic prevention and treatment measures.

There is a significant difference between severe and non-severe patients with COVID-19 in terms of demographic features, clinical symptoms, comorbidities, complications and outcomes. Hypertension, diabetes and cardiovascular diseases may be risk factors for COVID-19 patients to develop into severe cases.

Data analysis and interpretation: Zhufeng Wang, Hongsheng Deng

Clinical features of patients infected with 2019 novel coronavirus in Wuhan

Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan, China

Clinical characteristics of coronavirus disease 2019 in China

Soluble angiotensin-converting enzyme 2: a potential approach for coronavirus infection therapy

Middle East respiratory syndrome coronavirus: a case-control study of hospitalized patients

Epidemiological, demographic, and clinical characteristics of 47 cases of Middle East respiratory syndrome coronavirus disease from Saudi Arabia: a descriptive study

The Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses

Clinical characteristics of 463 patients with normal and severe COVID-19 rehabilitation

Clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia in Wuhan, China: a singlecentered, retrospective, observational study

Clinical features of 62 cases of critical COVID-19 with acute renal injury

Clinical characteristics of 58 patients with severe and critical COVID 19

Clinical characteristics and outcomes of 112 cardiovascular disease patients infected by 2019-nCoV

Epidemiological and clinical characteristics of 46 COVID-19 patients in Beijing

Clinical and computed tomographic imaging features of novel coronavirus pneumonia caused by SARS-CoV-2

Clinical characteristics of 153 cases infected with COVID-19 in Chongqing area

Clinical characteristics of 223 COVID-19 cases in Chongqing

Clinical characteristics, treatment and prognosis of 89 patients with COVID-19

Analysis of clinical characteristics and immune function of COVID-19 patients

Clinical features and treatment of 79 cases infected by COVID-19

Analysis of clinical characteristics of 143 patients with COVID-19 in northeast Chongqing

The Clinical and Chest CT Features Associated with Severe and Critical COVID-19 Pneumonia

Epidemiological characteristics of COVID-19 in Henan province

Analysis of clinical characteristics of COVID-19 in Hunan province

Clinical characteristics of COVID-19 in 11 cases

Clinical characteristics of COVID-19 in 30 cases

Analysis of myocardial injury and cardiovascular disease in critically ill patients with COVID-19

Characteristics of COVID-19 infection in Beijing

Clinical characteristics of 140 patients infected with SARS-CoV-2 in Wuhan

Retrospective analysis of case data of COVID-19 patients with different clinical type

Clinical characteristics and imaging manifestations of the 2019 novel coronavirus disease (COVID-19): a multi-center study in Wenzhou city

Comorbidity and its impact on 1590 patients with Covid-19 in China: A Nationwide Analysis

Bacterial pneumonia: comparison between diabetics and non-diabetics

Prevalence of comorbidities in the Middle East respiratory syndrome coronavirus (MERS-CoV): a systematic review and meta-analysis

A systematic review on the functional role of Th1/Th2 cytokines in type 2 diabetes and related metabolic complications

Analysis of inflammatory mediators in type 2 diabetes patients

Apoptosis, vascular leakage and increased risk of severe dengue in a type 2 diabetes mellitus patient

Low blood and vitreal BDNF, LXA4 and altered Th1/Th2 cytokine balance are potential risk factors for diabetic retinopathy

Vascular endothelial function and hypertension: insights and directions

Populations at risk for severe or complicated influenza illness: systematic review and meta-analysis

Coexistence of Th1/Th2 and Th17/ Treg imbalances in patients with post traumatic sepsis

Clinical features of the hospitalized patients with 2009 pandemic influenza A (H1N1

The authors declare that they have no competing interests.The datasets generated and analysed for this study are available from the corresponding author upon reasonable request.