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Abstract

COVID-19 is spreading exponentially. In order to optimize medical resources allocation and reduce mortality, biomarkers are needed to differentiate between COVID-19 patients with or without severe diseases early as possible. We searched Ovid MEDLINE(R), Ovid EMBASE, CNKI, Wanfang, VIP databases, the Cochrane Library, and medRxiv for primary articles in English or Chinese up to March 30, 2020 to systematically evaluate the risk factors for severe patients in China. Mean difference or standardize mean difference and odds ratio with 95% confidence intervals were performed by random-effect or fixed models in cases of significant heterogeneity between studies. We used I² to evaluate the magnitude of heterogeneity. A total of 54 articles involving about 7000 patients were eligible for this meta-analysis. In total, 52 of 67 parameters between severe and non-severe cases were significantly different. Elderly male patients with comorbidities including hypertension, diabetes, chronic obstructive pulmonary disease (COPD) cardiovascular disease, cerebrovascular disease, chronic kidney disease, or cancer were more common in severe COVID-19 patients. Regarding the clinical manifestations on admission, fever, cough, expectoration, dyspnea, chest distress, fatigue, headache, chills, anorexia, or abdominal pain were more prevalent in severe COVID-19 patients. The results of the clinical examination showed that high C-reactive protein (CRP), high lactate dehydrogenase (LDH), high D-dimer, and decreased T lymphocytes cells subsets, decreased lymphocyte may help clinicians predict the progression of severe illness in patients with COVID-19. Our findings will be conducive for clinician to stratify the COVID-19 patients to reduce mortality under the relative shortage of medical resources.

Keywords

clinical features coronavirus disease 2019 meta-analysis risk factors severity

Introduction

On 31 December 2019, a cluster of pneumonia of unknown cause were reported in Wuhan, Hubei Province, China. The pneumonia named for coronavirus disease 2019 (COVID-19) was sustained by a novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Nowadays, the pandemic coronavirus spread across the world exponentially and the global number of cases are rising sharply, especially in United States, Italy, and Turkey. There have been 3,435,894 confirmed cases, including 239,604 deaths in 213 countries or territories according to the report of the World Health Organization (WHO) until 4 May 2020.¹

As COVID-19 pandemic rages on in the world, our understanding of the new disease is still very limited. So far, the pathogenesis of COVID-19 remains unknown, and there have been no specific drugs or vaccine for SARS-CoV-2. Therefore, the WHO endorses supportive care only.² A wide range of variability in the case fatality rate (CFR) of COVID-19 was observed in different places. A report based on 72, 314 COVID-19 cases published by the Chinese Center for Disease Control and Prevention showed the overall CFR was 2.3%, which was 49.0% among critical cases.³ Therefore, facing the global outbreak of COVID 19 and the relative shortage of medical resources, it is essential to distinguish severe patients from infected cases as early as possible for reducing the CFR.

Since the COVID-19 outbreak, a large number of research reports and case series have already been published in major international scientific and medical journals. Whereas all available clinical studies have significant limitations in sample size and the same patient might been reported in more than one article.⁴ It is obligatory to take a deeper look at the clinical evidence, because the overlapped data could affect the real understanding of this infectious disease. In addition, previous systematic reviews have shown inconclusive or incomplete results due to limited sample size and statistical power, which missed important clinical manifestation such as fatigue, cough, anorexia.^5–7 To identify the severe patients from infected cases and address the limitations of the previous reviews, we performed a systematic literature review with meta-analysis to consolidate what has been learned from each study. All documents reporting information on the clinical manifestation, comorbidities and the laboratory characteristics in COVID-19 patients with a clinically validated definition of severe disease were finally included to evaluate the risk factors of COVID-19 in China.

Methods

This article was evaluated in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statements. The protocol was registered in the International Prospective Register of Systematic Reviews (PROSPERO #CRD42020177229) and was available online. Prior to the analysis, we conducted subgroup analyzes by age group, and each of the variables of interest including the proportion of males, region, sample size, and the proportion of severe COVID-19 patients.

Search strategy

We systematically searched Ovid MEDLINE(R), Ovid EMBASE, CNKI, Wanfang, VIP databases, and the Cochrane Library for studies in English or Chinese language from January 1, 2020, to March 30, 2020, using the search terms: “2019 novel coronavirus,” “2019-nCoV infection,” “2019-nCoV disease,” “COVID-19,” “coronavirus disease-19,” “coronavirus disease 2019,” “novel coronavirus,” “SARS-CoV-2,” “COVID-19 virus,” “SARS2,” and “2019-nCoV.” The searches were limited to humans. We also searched the preprint servers medRxiv website for studies between January 1, 2020 and March 30, 2020 given that this field is developing rapidly. In addition, relevant clinical systematic reviews, meta-analysis, and references of relevant publications were scrutinized manually with the aim to identify additional potentially eligible literature.

Eligibility criteria

Studies were deemed eligible for inclusion if they were (1) case-control, cross-sectional, clinical, or cohort studies including patients diagnosed with COVID-19 and (2) described the demographics, clinical manifestations, comorbidities or laboratory features of non-severe, and severe COVID-19 patients separately. We excluded expert recommendations, reviews, editorials, case reports, family-based studies, child cases, duplicates, studies lacking in information on quality assessment and data analysis.

Study selection

Two reviewers (JL, YL) independently screened the titles and abstracts of all the records and coded them as “retrieve” (eligible or potentially eligible or unclear) or “do not retrieve” based on the inclusion criteria. However, studies and reviews that might include relevant data or information were retained initially and the full-text version was analyzed. We retrieved the full texts and publications of the “retrieve” records. Two review authors (JL, YL) independently screened the full texts for inclusion and recorded reasons for the ineligible studies. All disagreements were resolved by consensus, if not, by discussion with a third review author (XC). We recorded the selection process in sufficient detail and the protocol followed the recommendations established by the PRISMA s (Figure 1).

Figure 1.

PRISMA flow diagram of study selection.

Data extraction and risk of bias assessment

Data were extracted using a standardized electronic data entry form by two investigators (JL, YL) independently. Data extraction forms included information on authors, the area, the number of non-severe and severe COVID-19 patients in each group, study period, age, sex, unhealthy lifestyle habits (e.g. smoking), vital signs (e.g. body temperature, pulse, respiration rate, mean arterial pressure (MAP)), clinical manifestations (e.g. fever, chills, fatigue, cough, expectoration, dyspnea, chest distress, hemoptysis, nasal congestion and rhinorrhea, sore throat, headache, nausea, vomiting, dizziness, myalgia or arthralgia ache, anorexia, abdominal pain, and diarrhea), comorbidities (e.g. hypertension, diabetes, chronic obstructive pulmonary disease (COPD), cardiovascular diseases (CVD), cerebrovascular disease (CeVD), chronic liver disease (CLD), digestive system disease (DSD), malignancy, chronic renal disease (CKD)), and laboratory features (white blood cells count (WBC), lymphocyte count, monocyte count, platelet count, hemoglobin, neutrophil count, neutrophil/lymphocyte ratio (NLR), percentage of neutrophil (N%), CD3 T-lymphocyte count (CD3), CD4 T-lymphocyte count (CD4), CD8 T-lymphocyte count (CD8), C-reactive protein (CRP), procalcitonin (PCT), erythrocyte sedimentation rate (ESR), serum ferritin, interleukin-6 (IL-6), lactic dehydrogenase (LDH), creatine kinase (CK), creatine kinase-MB (CK-MB), hypersensitive troponin I (hTnI), aspartate aminotransferase (AST), alanine transaminase (ALT), total bilirubin (Tbil), albumin (ALB), creatinine (Cr), blood urea nitrogen (BUN), activated partial thromboplastin time (APTT), prothrombin time (PT), D-dimer, fibrinogen (FIB), serum sodium, serum potassium, serum chlorine, and serum calcium). Disagreements were resolved by consensus or a third reviewer (XC) when needed. Two authors (GZ, HQ) cross-checked all the information retrieved. To ensure that patients were not being counted more than once and minimize the inaccuracy of data, when more than one eligible article reported patients in the same hospital and addressed the same outcome, we included the article with the largest number of participants.

Quality of each included study was assessed using the JBI-MAStARI (JBI Meta Analysis of Statistics Assessment and Review Instrument). The questionnaire consists of eight questions that were answered with yes, no, unclear, or not applicable. The included studies were classified into three categories based on the result of each questionnaire: high methodological quality (>5 “yes” responses), moderate methodological quality (3–4 “yes” responses), or low methodological quality (0–2 “yes” responses).⁸

Data synthesis and statistical analysis

We extrapolated mean and standard deviation (SD) from median and interquartile range (IQR) values, or the median and the minimum and maximum values, according to the method presented by Luo et al.⁹ and Wan et al.¹⁰ respectively. We compared severe patients with non-severe ones in the demographics, clinical manifestations, comorbidities, and laboratory features. When COVID-19 patients were classified into more than two groups (mild, moderate, severe, and critical group), we combined mild and moderate as a non-severe group, severe, and critical/death as a severe group. The weight or standardized mean difference (WMD or SMD) and odds ratios (ORs) with 95% confidence intervals (CIs) were calculated by a random-effect or fixed-effect model based on between-study heterogeneity for continuous data and dichotomous data, respectively. Heterogeneity was assessed using the I² test and the Q test: >50% represented substantial heterogeneity. The random-effect model was used if I² > 50% and the fixed-effect model was used if I² ⩽ 50%. To evaluate potential publication bias, funnel plots, and Egger’s test were used. Sensitivity analysis was conducted employing a leave-one-out analysis. Subgroup analysis with significant difference and substantial heterogeneity were performed based on age (>50 years vs ⩽50 years old), region (Hubei vs other regions), samples size (⩽100 patients, 100–200 patients, or >200 patients), the proportion of male (>50%, ⩽50%, or no reported), and the proportion of severe patients (>30% vs ⩽30%). All statistical analysis was performed with the Stata SE version 12.1 software packages (Stata Corp, College Station, TX).

Results

Study search and selection

The PRISMA flow diagram of this meta-analysis is presented in Figure 1. A total number of 4305 unique publications were initially identified. We assessed 282 full-text records excluding duplicates and non-relevant citations based on title and abstract. Fifty-six of were selected by two review authors (JL, YL) and numbered. Prior to the data extraction, two studies were excluded because patients were classified into ICU and non-ICU groups.^11,12 Finally, the pooled analysis included 54 studies.^{13–17,18–66}

Study characteristics

The characteristics of included studies are described in Supplemental Table S1. All articles were cross-sectional studies. Guan et al.¹³ study, included 1099 patients from 552 hospitals in 30 provinces, was excluded in the manuscript due to patient overlap. Patients included in the 54 studies were from 14 provinces in China. A total of 33 articles were English language and 21 were Chinese language. The minimum sample size was 21 and maximum sample size was 918 participants. The mean ages ranged from 29.2 to 72.5 years, and the percentage of male ranged from 33.7% to 81.0%. The severity of COVID-19 was defined according to 42, 4, 3 studies which were established by China’s National Health Commission, WHO’s interim guidelines, the American Thoracic Society guideline respectively (Supplemental Table S1). In addition, three studies divided COVID-19 patients into severe and non-severe group already, two articles did not report the degree of severity (Supplemental Table S1). Of 54 studies, 46 and eight were identified fair and high quality according to the JBI-MAStARI, respectively (Supplemental Table S1 and S2). The demographics, clinical manifestations, comorbidities, and laboratory features of COVID-19 patients are presented in Supplemental Table S3. We analyzed 67 variables for the meta-analyzes. The forest plots, sensitivity analysis and funnel plots were showed in Supplemental Figure S1.

Demographics outcomes

The pooled results indicated that the proportion of male was significantly higher in severe group compared to female without significant heterogeneity (OR = 0.65, 95% CI = 0.58–0.73, I² = 29.6%). In addition, severe cases were older than non-severe ones (WMD = 10.77 years, 95% CI = 9.25–12.30, I² = 64.30%), and patients aged more than 65 years were associated with greater risk of severe disease (OR = 3.43, 95% CI = 2.57–4.59; I² = 22.4%). Besides, smoker did not have higher odds of COVID-19 progression than non-smokers (OR = 1.35, 95% CI = 0.96–1.91; I² = 0%).

Sensitivity analysis for age, sex showed that the results were robust by sequentially removal of each study. However, sensitivity analysis for smoking revealed that the results were not statistically significant.^14–18,67 There was publication bias in sex, but not in age (Supplemental Figure S1).

Vital signs outcomes

Body temperature and pulse were higher among critical patients compared to non-critical ones ((body temperature: WMD = 0.29, 95% CI = 0.12–0.46; I² = 67.5%), (pulse: WMD = 4.19, 95% CI = 1.64–6.74; I² = 47.7%)) (Table 1 and Supplemental Figure S1), while respiratory rate and MAP were not statistically significant. Moreover, the sensitivity analysis showed that the estimate of pulse were not robust.

Table 1.

Meta-analysis of the continuous variables for severe versus non-severe patients with COVID-19.

Laboratory results	No. of studies	No. of patients	WMD/SMD (95% CI)	Model	I² (%)	Egger’s
Demographic
Age	27	5107	10.77 (9.25, 12.30)	Random	64.30	0.00
Vital signs
Body temperature (°C)	7	1249	0.29 (0.12, 0.46)	Random	67.50	0.35
Pulse (bpm)	6	733	4.19 (1.64, 6.74)	Fixed	47.70	0.85
Respiratory rate (bpm)	6	733	2.20 (–0.13, 4.54)	Random	91.90	0.07
MAP (mmHg)	3	556	0.76 (–1.58, 3.11)	Fixed	33.30	0.43
Blood routine
WBC (×10⁹/L)	19	3228	0.75 (0.26, 1.24)	Random	85.50	0.00
Lymphocyte (×10⁹/L)	20	4145	–0.39 (–0.46, –0.33)	Random	75.60	0.18
Platelets (×10⁹/L)	19	2858	–23.19 (–32.61, –13.77)	Random	65.50	0.00
Hemoglobin (g/L)	14	1362	–4.03 (–6.44, –1.61)	Fixed	22.50	0.92
Neutrophil (×10⁹/L)	16	2230	1.25 (0.79, 1.72)	Random	78.20	0.02
Monocyte (×10⁹/L)	8	1211	0.01 (–0.06, 0.08)	Random	79.00	0.93
NLR	5	1161	2.04 (1.18, 2.90)	Random	73.60	0.67
N%	8	1311	13.38 (10.91, 15.86)	Random	51.90	0.56
T cells subsets
CD3 (×10⁶/L)	4	404	–443.0 (–702.1, –183.9)	Random	91.30	0.17
CD4 (×10⁶/L)	6	888	–227.6 (–344.8, –110.5)	Random	90.60	0.74
CD8 (×10⁶/L)	6	888	–138.9 (–207.6, –70.2)	Random	88.90	0.79
Inflammation-related biomarkers
CRP (mg/L)	21	3053	38.21 (31.95, 44.47)	Random	67.80	0.59
PCT (ng/mL)	19	2933	0.89† (0.67, 1.12)	Random	84.50	0.00
ESR (mm/h)	8	1342	14.18 (6.53, 21.83)	Random	69.70	0.69
IL-6 (pg/mL)	5	1254	1.68† (0.68, 2.68)	Random	98.20	0.00
Ferritin (ng/mL)	3	1016	328.0 (139.2, 516.8)	Random	3.80	0.04
Myocardial enzymes
hTnI (pg/mL)	4	574	18.60 (11.90, 25.4)	Fixed	0.00	0.37
LDH (U/L)	15	1935	1.22†s (0.91, 1.53)	Random	85.70	0.08
CK (U/L)	15	1934	0.51† (0.30, 0.72)	Random	76.30	0.89
CK-MB (U/L)	5	780	0.74† (0.30, 1.17)	Random	83.00	0.73
Biochemical findings
AST (U/L)	19	2385	1.04† (0.66, 1.42)	Random	92.20	0.48
ALT (U/L)	20	2695	0.45† (0.24, 0.65)	Random	77.90	0.07
Tbil (μmol/L)	10	1739	1.32 (0.08, 2.56)	Random	68.40	0.72
ALB (g/L)	16	2346	–4.33 (–5.35, –3.30)	Random	76.90	0.01
Cr (μmol/L)	18	2709	5.77 (1.58, 9.97)	Random	75.20	0.14
BUN (mmol/L)	13	2162	1.16 (0.77, 1.55)	Random	64.70	0.00
Coagulation function
D-dimer (mg/L)	16	2183	1.04† (0.73, 1.36)	Random	88.10	0.05
PT (s)	12	1192	0.40† (0.10, 0.69)	Random	76.70	0.61
FIB (g/L)	5	1002	0.32 (0.06, 0.57)	Random	63.70	0.22
APTT (s)	10	1244	–0.01 (–1.45, 1.44)	Random	76.60	0.34
Electrolyte
Sodium (mmol/L)	6	814	–2.09 ( –2.70, –1.48)	Fixed	0.00	0.20
Potassium (mmol/L)	9	1426	–0.02 ( –0.15, 0.10)	Random	72.10	0.51
Chlorine (mmol/L)	5	616	–0.44 (–2.24, 1.36)	Random	71.60	0.61
Calcium (mmol/L)	6	870	–0.13 (–0.21, –0.04)	Random	89.70	0.07

†

presented SMD.

MAP: Mean arterial pressure; WBC: White blood cell; NLR: Neutrophils/lymphocytes ratio; N%: percentage of neutrophils; CD3: CD3 T-lymphocyte count; CD4: CD4 T-lymphocyte count; CD8: CD8 T-lymphocyte count; CRP: C-reactive protein; PCT: Procalcitonin; ESR: Erythrocyte sedimentation rate; IL-6: Interleukin-6; LDH: Lactic dehydrogenase; CK: Creatine kinase; CK-MB: Creatine kinase-MB; hTnI: Hypersensitive troponin I; AST: Aspartate aminotransferase; ALT: Alanine transaminase; Tbil: Total bilirubin; ALB: Albumin; Cr: Creatinine; BUN: Blood urea nitrogen; APTT: Activated partial thromboplastin time; PT: Prothrombin time; FIB: Fibrinogen; WMD = weighted mean difference; SMD: standardized mean difference.

Clinical manifestations

We found the following clinical manifestations were risk factors of severe diseases: fever (OR = 1.96, 95% CI = 1.29–2.99; I² = 72.6%), chills (OR = 2.30, 95% CI = 1.50–3.52; I² = 0.0%), cough (OR = 1.22, 95% CI = 1.07–1.41; I² = 49.0%), expectoration (OR = 1.36, 95% CI = 1.13–1.65; I² = 37.8%), dyspnea (OR = 5.83, 95% CI = 3.65–9.31; I² = 80.8%), chest distress (OR = 4.09, 95% CI = 2.41–6.92; I² = 24.8%), fatigue (OR = 1.56, 95% CI = 1.15–2.10; I² = 61.7%), headache (OR = 1.36, 95% CI = 1.07–1.73; I² = 41.0%), anorexia (OR = 2.25, 95% CI = 1.32–3.84; I² = 74.20%), abdominal pain (OR = 2.76, 95% CI = 1.56–4.89; I² = 0.0%). There was no significant correlation between hemoptysis, nasal congestion or rhinorrhea, sore throat, diarrhea, nausea or vomiting, myalgia and dizziness, and severe COVID-19 (Figure 2 and Supplemental Figure S1).

Figure 2.

The outcomes of the meta-analysis for risk factors of dichotomous data in severe patients with COVID-19.

Sensitivity analysis were robust excluding sore throat, headache, nausea, or vomit. The funnel plot and Egger’s test showed there was no significant publication bias except for chest distress, cough, and fever. The associations of fever, fatigue, anorexia, dyspnea, myalgia, nausea, or vomit with the risk of severe COVID-19 were inconsistent in subgroup analysis (Supplemental Table S4–S8).

Comorbidities

Comorbidities outcomes are presented in Figure 2 and Supplemental Figure S1. The proportion of hypertension, diabetes, CVD, CKD, COPD, CeVD, and cancer was statistically significant higher in severe COVID-19 patients compared to the non-severe patients (hypertension (OR = 3.11, 95% CI = 2.38–4.08; I² = 58.4%), diabetes (OR = 2.44, 95% CI = 1.99–3.00; I² = 40.3%), CVD (OR = 3.98, 95% CI = 3.00–5.27; I² = 0.0%), CKD (OR = 2.19, 95% CI = 1.37–3.49; I² = 20.8%), COPD (OR = 4.25, 95% CI = 2.63–6.88; I² = 0.0%), CeVD (OR = 3.06, 95% CI = 1.93–4.85; I² = 33.80), cancer (OR = 1.48, 95% CI = 0.93–2.35; I² = 0.00)), but not DSD or CLD. Between-study heterogeneity was low for all comorbidities except for hypertension.

The sensitivity analysis for hypertension, diabetes, CVD, CKD, COPD, CeVD, DSD, or CLD confirmed that none of the exclusion of a specific study would change the final results. For cancer, although sensitivity analysis remained consistent, the 95% CI included the null effect when we omitted studies of Shi et al.,¹⁹ Cheng et al.,²⁰ Zhang et al.,²¹ or Hu et al.²² A funnel plot and results of Egger’s test showed significant evidence of publication bias for hypertension, diabetes COPD and CKD, and no significant evidence of publication bias for CVD, CeVD, DSD, and CLD. Subgroup analysis for hypertension still generated significant associations comparable with those of the overall analysis (Supplemental Table S4–S8).

Laboratory features

The WMDs/SMDs indicated that 21 laboratory findings including WBC, neutrophil, NLR, N%, CRP, PCT, ESR, IL-6, LDH, ferritin, hTnI, CK, CK-MB, AST, ALT, Tbil, Cr, BUN, PT, D-dimer, and FIB were significantly increased, and nine laboratory findings including lymphocyte, platelets, hemoglobin, CD3, CD4, CD8, ALB, serum sodium, and serum calcium were significantly decreased (p < 0.01), but four laboratory findings including monocyte, APTT, serum potassium, and serum chlorine levels did not differ in severe patients versus non-severe patients (Table 1). However, only differences in lymphocyte (WMD = −0.39, 95% CI = −0.64 to −0.33; I² = 75.6%), CD4 (WMD = −227.6, 95% CI = −344.8 to −110.5; I² = 90.6%), CD8 (WMD = −138.9, 95% CI = −207.6 to −70.2; I² = 88.9%), CRP (WMD = 38.21, 95% CI = 31.95–44.47; I² = 67.8%), LDH (SMD = 1.27, 95% CI = 0.95–1.60; I² = 85.7%), and D-dimer (WMD = 1.04, 95% CI = 0.73–1.36; I² = 88.10%) between severe and non-severe patients showed clinical significance. Non-severe patients had normal or slightly abnormal mean/median values of lymphocyte, CD3, CD4, CD8, CRP, LDH, and D-dimer, while severe patients had significantly abnormal mean/median values of the aforementioned indicators (Supplemental Figure S1–38, S1-44, S1-45, S1-46, S1-51, S1-63). Between-study heterogeneity was high for all laboratory findings except for MAP, sodium, hTnI, ferritin, hemoglobin.

Regarding sensitivity analysis, the WMDs/SMDs for the majority of laboratory findings remained consistent. By contrast, the estimates of Tbil and FIB were not robust after sequentially omitting each study. A funnel plot and results of Egger’s test showed significant evidence of publication bias for WBC, platelets, neutrophil, PCT, ferritin, IL-6, ALB, and BUN, and no significant evidence of publication bias for the other laboratory findings. Considering between-study heterogeneity and clinical significance, we further conducted subgroup analysis for lymphocyte, CD4, CD8, CRP, LDH, and D-dimer, which showed that lymphocyte, CD4, CRP, LDH, and D-dimer still remained a substantial difference regardless of each subgroup, but CD8 did not suggest any significant difference in patients aged less than 50 years old between severe and non-severe disease (Supplemental Table S4–S8).

Discussion

Previously, older age and male have been reported as important independent predictors of severe disease or death in SARS-CoV.¹³ The current study confirmed that severe COVID-19 patients were older and more likely to be males, which was similar to previous systematic review.⁵ The study was in accordance with the previous study of Guan et al.,¹³ which based on 1099 cases from 552 hospitals in 30 provinces of China in the early period of outbreak. Moreover, this study provided further evidence that COVID-19 patients aged more than 65 years were at a higher risk of severe disease. These findings shared similar conclusions with the study of Wu et al.,⁶⁸ which included 201 confirmed COVID-19 patients, Wu reported a 3.26 times and 6.17 times higher risk of the progression of ARDS to death for patients aged more than 65 years and patients aged less than 65 years, respectively. These phenomena may ascribe to the lower expression of angiotensin-converting enzyme 2 (ACE2), a catalytic breakdown enzyme of Ang-II, in elderly and male individuals. Lower level of ACE2 could increase the concentration of Ang II, which leads to inflammatory response.⁶⁹ Consistent with our findings, previous meta-analysis studies, which consisted of only 412 COVID-19 patients, reported that smoking also was not a risk factor for severe illness.⁵

In concert with recent studies, we found fever, cough, dyspnea, fatigue, myalgia, anorexia were the predominant symptoms of COVID-19 patients infected by SARS-CoV-2,^11,13,68,70 while chill, chest distress, hemoptysis, sore throat, nasal congestion or rhinorrhea, gastrointestinal symptoms were rare, which was different from seasonal influenza and SARS-CoV or MERS-CoV.^13,71,72 Previous pooled analysis further indicated that severe COVID-19 patients had more frequent fever and dyspnea compared to non-severe patients,^5,73 as it was in our findings. In addition, our study improves upon prior meta-analysis by exploring gastrointestinal and other clinical symptoms. Up to date, there were more frequent respiratory symptoms such as cough, expectoration, and chest distress in severe patients than non-severe patients. It was consistent with patients infected by SARS-CoV-2, which were recognized as an acute lung injury whereby an initial and rapidly developed significant respiratory distress like SARS and MERS.⁷⁴ What’s more, we found general clinical manifestations including fatigue, chills and headache as well as gastrointestinal symptoms such as anorexia and abdominal pain, not hemoptysis, sore throat, myalgia, dizziness, nasal congestion/rhinorrhea, nausea/vomit, and diarrhea, were more common in severe patients than non-severe patients. However, the latest meta-analysis indicated that cough, expectoration, headache, and fatigue were not associated with the risk of severe disease.⁷³ This latest published systematic review and meta-analysis may reflect less accurate results due to the inclusion of a study by Guan et al.,¹³ which was likely to overlap with the other included articles in the pooled analysis. Symptoms difference between severe patients and non-severe patients showed that the clinical manifestations besides respiratory illness were maybe also early signs of multiple organ injuries and were also associated with high risk of development into serious illness.

Our results indicated that patients coexisting with comorbidities such as hypertension, diabetes, CVD, cancer, and COPD had higher risk of severe COVID-19, as it was in previous studies.^5,73 Hypertension, diabetes, CVD, and cancer showed a few common characteristics with infectious diseases. Similarly the body was in a state of the proinflammatory and in the attenuation of the innate immune response, the long-term history of diabetes, hypertension, CVD, and cancer damaged the vascular structure, and it was more likely to develop into critical disease in infection. Furthermore, once patients underlying CVD or hypertension infected by SARS-CoV-2, ACE2 consumption would accelerate the deterioration of CVD or hypertension. At the same time, the loss of ACE2 may also promoted endothelial dysfunction and inflammation in diabetes patients.^75,76 What our research is superior to previous research is that we also found that CKD and CeVD are also risk factors for the occurrence of severe COVID-19, while DSD and CLD are not. A review suggested that the expression of ACE2 was lower in patients with underlying chronic diseases, especially CKD, CVD, than those without underlying chronic diseases.⁷⁷ This may be one of the reasons why patients with CKD and CeVD have a higher risk of severe disease in SARS-CoV-2 infection.

The pathogenesis of SARS-CoV-2-induced infection is still not completely understood. Cytokine storm and dysregulated immune responses are thought to play important roles in disease severity.⁶⁸ Inflammatory biomarker was also a feature associated with severity of COVID-19 disease. It is consistent with previous studies,^16,51,78 our research found CRP, PCT, ESR, and serum ferritin was higher in severe patients than non-severe. Although all inflammatory biomarkers are non-specific for severe COVID-19, patients with elevated inflammatory biomarkers need special attention of clinicians. In addition, IL-6, a key mediator regulating cytokine storm, was also associated with the severity of COVID-19, IL-6 was considered as a predictor for mortality. At present, IL-6 has not been conducted in meta-analysis, our findings suggested the level of IL-6 was higher in severe patients than non-severe patients, which demonstrated that severe patients may present more severe systemic inflammatory response. Tocilizumab, an anti-IL-6 receptor monoclonal antibody, may improve the serious injuries of COVID-19 patients with very high IL-6 and reduce mortality. Furthermore, we found that severe patients manifested cellular immune deficiency with increased neutrophil-to-lymphocyte ratio (NLR) and decreased lymphocyte, monocyte, platelets, CD3, CD4, and CD-8 T-cell counts, which was consistent with the results that serious COVID-19 patients presented with lymphocytopenia.^11,13 These results indicated that severe patients with COVID-19 were associated with more serious damage of immune system.²⁶ Moreover, previous studies shown that SARS-CoV-2 induced myocardial damage,⁷⁹ which lined with our results that severe patients had higher level of myocardial enzymatic (hTnI, LDH, CK, CK-MB). Finally, we also pooled analysis of the biochemical findings, coagulation function, and electrolyte. The tests results reported as WMDs/SMDs made the clinical utility limited. But the clinicians might still pay more attentions to COVID-19 patients with abnormal biochemical findings, coagulation function, and electrolyte.

Limitations

The review applied a systematic and rigorous analysis based on the largest sample size of COVID-19 patients so far, the conclusions are highly credible. However, there are still some limitations in this meta-analysis. First, all studies included in our meta-analysis were retrospective cross-sectional, and factors for severe patients reported in different literatures varied greatly. Second, studies included are of large heterogeneity because of the severity of COVID-19 defined and the method for laboratory tests differently among studies and hospitals. What’s more, we didn’t analyze the interaction of the different comorbidities and clinical manifestations owing to lack of individual patient data and the information on the types of clusters of clinical manifestations and diseases of each study. The future studies may focus on the types of clusters of clinical manifestations and diseases, and developing a risk score to predict severe disease in patients with COVID-19 that could alert clinicians to patients at greatest risk, such that steps could be taken to mitigate the risk. Third, most patients in our study are from China, risk factors of COVID-19 severity may vary by race and regions, as it was reported in our subgroup studies. Therefore, the conclusions of our meta-analysis still need to be verified by larger sample size and more relevant studies involved COVID-19 cases in the worldwide.

Conclusions

In this meta-analysis, we summarized and analyze 67 parameters of patient features, vital signs, symptoms, comorbidities and laboratory tests between severe and non-severe patients in about 7000 COVID-19 confirmed cases reported by 54 studies. First, we found older age and male were associated with a higher risk of development of severe COVID-19 disease. And further analysis indicated that the symptoms of fever, chills, dyspnea, cough, expectoration, chest distress, fatigue, headache, anorexia, or abdominal pain are risk factors for severe illness. In addition, the observation also reminded us that patients with underlying diseases, especially hypertension, diabetes, CVD, CeVD, CKD, COPD, and cancer were associated with higher odds of severe illness. Finally, compared to non-severe patients with COVID-19, severe patients were older and presented a higher body temperature, pulse, and levels of WBC, neutrophil, NLR, N%, inflammation-related biomarkers, myocardial enzymes, AST, ALT, Tbil, Cr, BUN, PT, D-dimer, but showed a lower levels of lymphocyte, T lymphocytes cells subsets, platelets, hemoglobin, albumin, serum sodium, and calcium. Our findings will be conducive for clinician to stratify the COVID-19 patients to reduce mortality under the relative shortage of medical resources.

Supplemental Material

sj-docx-1-eji-10.1177_20587392211000890 – Supplemental material for The risk factors for severe patients with COVID-19 in China: A systematic review and meta-analysis

Supplemental material, sj-docx-1-eji-10.1177_20587392211000890 for The risk factors for severe patients with COVID-19 in China: A systematic review and meta-analysis by Xie Yue-liang, Wang Jiang-lin, Yang Hui-qin, Zhou Ge, Deng Hongyu, Fang Wei-jin and Zuo Xiao-cong in European Journal of Inflammation

Supplemental Material

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Supplemental material, sj-docx-2-eji-10.1177_20587392211000890 for The risk factors for severe patients with COVID-19 in China: A systematic review and meta-analysis by Xie Yue-liang, Wang Jiang-lin, Yang Hui-qin, Zhou Ge, Deng Hongyu, Fang Wei-jin and Zuo Xiao-cong in European Journal of Inflammation

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Supplemental material, sj-docx-4-eji-10.1177_20587392211000890 for The risk factors for severe patients with COVID-19 in China: A systematic review and meta-analysis by Xie Yue-liang, Wang Jiang-lin, Yang Hui-qin, Zhou Ge, Deng Hongyu, Fang Wei-jin and Zuo Xiao-cong in European Journal of Inflammation

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Supplemental material, sj-pdf-5-eji-10.1177_20587392211000890 for The risk factors for severe patients with COVID-19 in China: A systematic review and meta-analysis by Xie Yue-liang, Wang Jiang-lin, Yang Hui-qin, Zhou Ge, Deng Hongyu, Fang Wei-jin and Zuo Xiao-cong in European Journal of Inflammation

Footnotes

Author contributions

JL, YL had full access to all of the data and takes responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design: JL and YL. Selection of studies: JL, YL, and XC. Extraction of data: JL, YL, and XC. Checked all the information retrieved: GZ and HQ. Analysis or interpretation of data: JL and YL. Drafting of manuscript: JL and YL. Critical revision of the manuscript for important intellectual content: JL, YL, XC, GZ, HQ, HY, WJ. Obtaining funding: YL, HY, and XC. Study supervision: JL.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Jiang-lin Wang

Supplemental material

Supplemental material for this article is available online.

References

WHO. Coronavirus disease (COVID-19) Pandemic (2020), https://www.who.int/emergencies/diseases/novel-coronavirus-2019 (accessed 4 May 2020).

Wang

, et al. (2020) A review of the 2019 novel coronavirus (COVID-19) based on current evidence. International Journal of Antimicrobial Agents 55(6): 105948.

Mcgoogan

(2020) Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China: Summary of a report of 72314 cases from the Chinese Center for Disease Control and Prevention. Journal of the American Medical Association 323: 1239.

Bauchner

Golub

Zylke

(2020) Editorial concern-possible reporting of the same patients with COVID-19 in different reports. Journal of the American Medical Association 323(13): 256.

Mao

Chen

(2020) Risk factors for 2019 novel coronavirus disease (COVID-19) patients progressing to critical illness: A systematic review and meta-analysis. Aging (Albany NY) 12(12): 12410–12421.

Wang

Yuan

, et al. (2020) Clinical characteristics of coronavirus disease 2019 (COVID-19) in China: A systematic review and meta-analysis. The Journal of Infection 80(6): 656–665.

Yang

Zheng

Gou

, et al. (2020) Prevalence of comorbidities and its effects in patients infected with SARS-CoV-2: A systematic review and meta-analysis. International Journal of Infectious Diseases 94: 91–95.

Liberali

Kupek

Assis

MAAD

. (2020) Dietary patterns and childhood obesity risk: A systematic review. Childhood Obesity 16: 70–85.

Luo

Wan

Liu

, et al. (2018) Optimally estimating the sample mean from the sample size, median, mid-range, and/or mid-quartile range. Statistical Methods in Medical Research 27: 1785–805.

10.

Wan

Wang

Liu

, et al. (2014) Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range. BMC Medical Research Methodology 14: 135.

11.

Wang

, et al. (2020) Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan, China. Journal of the American Medical Association 323: 1061–1069.

12.

Fan

Chong

VCL

Chan

SSW

, et al. (2020) Hematologic parameters in patients with COVID-19 infection. American Journal of Hematology 95(6): E131–E134.

13.

Guan

, et al. (2020) Clinical characteristics of coronavirus disease 2019 in China. The New England Journal of Medicine 382: 1708–1720.

14.

Zhang

Dong

Cao

, et al. (2020) Clinical characteristics of 140 patients infected with SARS-CoV-2 in Wuhan, China. Allergy 75(7): 1730–1741.

15.

Zhang

Wang

Chen

, et al. (2020) Outcomes of novel coronavirus disease 2019 (COVID-19) infection in 107 patients with cancer from Wuhan, China. Cancer 126(17): 4023–4031.

16.

Liu

Zhang

Yang

, et al. (2020) The role of interleukin-6 in monitoring severe case of coronavirus disease 2019. EMBO Mol Med 12(7): e12421.

17.

Liu

Xiang

, et al. (2020) Neutrophil-to-lymphocyte ratio predicts severe illness patients with 2019 novel coronavirus in the early stage. Journal of Translational Medicine 18(1): 206.

18.

Huang

Cai

, et al. (2020) Prognostic factors for COVID-19 pneumonia progression to severe symptom based on the earlier clinical features: A retrospective analysis. Front Med (Lausanne) 7: 557453

19.

Shi

Zhao

, et al. (2020) Host susceptibility to severe COVID-19 and establishment of a host risk score: Findings of 487 cases outside Wuhan. Critical Care 24: 108.

20.

Chen

Wei

Shen

, et al. (2020) Clinical characteristics of 463 patients with common and severe type coronavirus disease 2019. Shanghai Medical Journal 43(4): 224–232.

21.

Zhang

Luo

, et al. (2020) Clinical features and short-term outcomes of 221 patients with COVID-19 in Wuhan, China. Journal of Clinical Virology 127: 104364.

22.

Chen

, et al. (2020) Risk factors associated with clinical outcomes in 323 COVID-19 hospitalized patients in Wuhan, China. Clinical Infectious Diseases 71(16): 2089–2098.

23.

Liu

Wang

, et al.(2020) Management of COVID-19 in patients after liver transplantation: Beijing working party for liver transplantation. Hepatology International 14(4): 432–436.

24.

Dong

, et al. (2020) Clinical and computed tomographic imaging features of novel coronavirus pneumonia caused by SARS-CoV-2. The Journal of Infection 80: 394–400.

25.

Liu

, et al. (2020) CT manifestations of coronavirus disease-2019: A retrospective analysis of 73 cases by disease severity. European Journal of Radiology 126: 108941.

26.

Qin

Zhou

, et al. (2020) Dysregulation of immune response in patients with coronavirus 2019 (COVID-19) in Wuhan, China. Clinical Infectious Diseases 71(5): 762–768.

27.

Zhao

Zhong

Xie

, et al. (2020) Relation between chest CT findings and clinical conditions of coronavirus disease (COVID-19) pneumonia: A multicenter study. American Journal of Roentgenology 214: 1072–1077.

28.

, et al.(2020) The clinical and chest CT features associated with severe and critical COVID-19 pneumonia. Investigative Radiology 55: 327–331.

29.

Han

Yang

Liu

, et al. (2020) Prominent changes in blood coagulation of patients with SARS-CoV-2 infection. Clinical Chemistry and Laboratory Medicine 58(7): 1116–1120.

30.

Ling

Zhang

, et al. (2020) Platelet-to-lymphocyte ratio is associated with prognosis in patients with Corona Virus Disease-19. Journal of Medical Virology 92(9): 1533–1541.

31.

Fang

Zhu

Cheng

, et al. (2020) Retrospective analysis on 308 cases of COVID-19 and clinical application program of Kangyi Qianshen Gong exercise prescription. Shanghai Journal of Traditional Chinese Medicine 5: 22–27.

32.

Jiang

, et al. (2020) Clinical characteristics and immune function analysis of COVID-19. Medical Journal of Wuhan University 41(4): 529–532.

33.

Lyu

, et al. (2020) Analysis on the cardiac features of patients with different clinical types of novel coronavirus disease 2019. Guangdong Medical Journal 41(8): 797–800.

34.

Xie

, et al. (2020) Clinical characteristics and distribution of traditional Chinese medicine syndrome in 524 patients with COVID-19 in Henan province. Journal of Traditional Chinese 61(16): 1391–1396.

35.

Wei

Ying

, et al. (2020) The correlation between acute phase protein combined biochemical indicators and clinical classification of COVID-19. International Journal of Laboratory Medicine 41(13): 1602–1607.

36.

Gan

, et al. (2020) Heart and liver damages in coronavirus disease-2019. Guangdong Medical Journal 382:1708–1720.

37.

Guo

Zhu

, et al. (2020) Correlation between clinical classification of novel coronavirus pneumonia and MSCT volume scanning. Journal of Southern Medical University 40(03): 321–326.

38.

Chen

Tong

Xiang

, et al. (2020) Retrospective study on the epidemiological characteristics of 139 patients with novel coronavirus pneumonia on the effects of Severity. Chongqing Medicine 49(17): 2802–2806.

39.

Zhong

Lin

Shi

(2020) Clinical characteristics and outcome of the patients with COVID-19: A report of 62 cases. Medical Journal of Chinese People’s Liberation Army 45(4): 370–374.

40.

Fang

Mei

Yang

, et al. (2020) Clinical characteristics and treatment strategies of 79 patients with COVID-19. Chinese Pharmacological Bulletin 36(4): 453–459.

41.

Long

Huang

, et al. (2020) Clinical characteristics of 80 patients with COVID-19 in Zhuzhou city. Chinese Journal of Infection Control 19(3): 227–233.

42.

Yuan

Sun

Zuo

, et al. (2020) A retrospective analysis of the clinical characteristics of 223 patients NCP patients in Chongqing. Journal of Southwest University (Natural Science Edition) 42(3): 17–24.

43.

Chen

Shui

Pang

, et al. (2020) Clinical features of coronavirusdisease2019innortheastareaof Chongqing: Analysis of 143 cases. Journal of Third Military Medical University 42(6): 549–554.

44.

Xiang

Liu

, et al. (2020) Analysis of clinical characteristics of 49 patients with coronavirus disease 2019 in Jiangxi. Chinese Journal of Respiratory and Critical Care Medicine 19(2): 154–60.

45.

Hou

Zhang

, et al. (2020) CT features of corona virus disease 2019 (COVID-19) in different stages and its correlation with neutrophil-lymphocyte ratio (NLR) and T lymphocyte subsets. Radio Practice 35: 272–276.

46.

Zhao

, et al. (2020) Comparison of CT imaging and clinical features between common and severe/critical type of COVID-19 patients. International Journal of Radiology 43(3): 257–261.

47.

Xiong

Jiang

Zhao

, et al. (2020) Clinical characteristics, treatment, and prognosis in 89 cases of COVID-2019. Medical Journal of Wuhan University 41(4): 452–456.

48.

Chen

Xia

, et al. (2020) Retrospective analysis of COVID-19 patients with different clinical subtypes. Herald of Medicine 39(4): 459–464.

49.

Fei

, et al. (2020) Acute kidney injury at early stage as a negative prognostic indicator of patients with COVID-19: A hospital-based retrospective analysis. medRxiv preprint.

50.

Wan

Fan

, et al. (2020) Relationships among lymphocyte subsets, cytokines, and the pulmonary inflammation index in coronavirus (COVID-19) infected patients. British Journal of Haematology 189(3): 428–437.

51.

Chen

Guo

, et al.(2020)Clinical and immunological features of severe and moderate coronavirus disease 2019. The Journal of Clinical Investigation 130(5): 2620–2629.

52.

Zhao

Zha

, et al. (2020) Clinical characteristics and durations of hospitalized patients with COVID-19 in Beijing: A retrospective cohort study. medRxiv preprint.

53.

Yan

Song

Lin

, et al. (2020) Clinical characteristics of coronavirus disease 2019 in Hainan, China. medRxiv preprint.

54.

Wang

Zhou

Yang

, et al.(2020) Clinical characteristics of patients with severe pneumonia caused by the 2019 novel coronavirus in Wuhan, China[J]. Respiration 99(8): 649–657.

55.

Cao

Zhang

Wang

, et al. (2020) Clinical features of patients infected with the 2019 novel coronavirus (COVID-19) in Shanghai. medRxiv preprint.

56.

Xue

Zhao

, et al. (2020) Correlation between hypophosphatemia and the severity of Corona Virus Disease 2019 patients. medRxiv preprint.

57.

Qian

Yang

Ding

, et al. (2020) Epidemiologic and clinical characteristics of 91 hospitalized patients with COVID-19 in Zhejiang, China: A retrospective, multi-center case series. QJM: An International Journal of Medicine 113(7): 474–481.

58.

Ying

Lan

Yuan

, et al.(2020) Epidemiological and clinical characteristics of coronavirus disease 2019 in Shenzhen. medRxiv preprint.

59.

Chen

Zheng

Qing

, et al. (2020) Epidemiological and clinical features of 291 cases with coronavirus disease 2019. medRxiv preprint.

60.

Yan

Tang

, et al. (2020) Epidemiological and clinical features of 2019-nCoV acute respiratory disease cases in Chongqing municipality, China: A retrospective, descriptive, multiple-center study. medRxiv preprint.

61.

Fan

Liu

, et al. (2020) Medical treatment of 55 patients with COVID-19 from seven cities in northeast China who fully recovered a single-center, retrospective, observational study. medRxiv preprint.

62.

Mao

Jin

Wang

, et al. (2020) Neurologic manifestations of hospitalized patients with coronavirus disease 2019 in Wuhan, China. JAMA Neurology 77(6): 683–690.

63.

Zeng

Liao

, et al. (2020) Risk assessment of progression to severe conditions for patients with COVID-19 pneumonia: A single-center retrospective study. medRxiv preprint.

64.

Song

Chen

Sun

, et al. (2020) Clinical value of PCT and IL-6 combined detection for COVID-19. International Journal of Laboratory Medicine 41(18): 2281–2283.

65.

Dai

Gao

Luo

, et al. (2020) Clinical characteristics of novel coronavirus disease 2019 in Hunan province. Practice Preventive Medicine 27(4): 396–399.

66.

Wang

Chen

, et al. (2020) SARS-CoV-2 infection does not significantly cause acute renal injury: An analysis of 116 hospitalized patients with COVID-19 in a single hospital, Wuhan, China. medRxiv preprint.

67.

Tabata

Imai

Kawano

, et al. (2020) Clinical characteristics of COVID-19 in 104 people with SARS-CoV-2 infection on the Diamond Princess cruise ship: A retrospective analysis. The Lancet Infectious Diseases 20(9): 1043–1050.

68.

Chen

Cai

, et al. (2020) Risk factors associated with acute respiratory distress syndrome and death in patients with coronavirus disease 2019 pneumonia in Wuhan, China. JAMA Internal Medicine 180(7): 934–943.

69.

Shi

Wang

, et al. (2020) Pathological findings of COVID-19 associated with acute respiratory distress syndrome. The Lancet Respiratory Medicine 8: 420–422.

70.

Zhou

, et al. (2020) Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: A retrospective cohort study. Lancet 395: 1054–1062.

71.

Assiri

McGeer

Perl

, et al. (2020) Hospital outbreak of middle east respiratory syndrome coronavirus. The New England Journal of Medicine 369: 407–416.

72.

Leung

Chan

, et al. (2020) Enteric involvement of severe acute respiratory syndrome-associated coronavirus infection. Gastroenterology 125: 1011–1017.

73.

Zheng

Peng

, et al. (2020) Risk factors of critical & mortal COVID-19 cases: A systematic literature review and meta-analysis. Journal of Infection 81(2): e16–e25.

74.

Bernheim

Mei

Huang

, et al. (2020) Chest CT findings in coronavirus disease-19 (COVID-19): Relationship to duration of infection. Radiology 295(3): 685–691.

75.

South

Diz

Chappell

(2020) COVID-19, ACE2, and the cardiovascular consequences. American Journal of Physiology Heart and Circulatory Physiology 318: H1084–H1090.

76.

Schiffrin

Flack

Ito

, et al. Hypertension and COVID-19. American Journal of Hypertension 33: 373–374.

77.

Tikellis

Bernardi

Burns

(2011) Angiotensin-converting enzyme 2 is a key modulator of the renin-angiotensin system in cardiovascular and renal disease. Current Opinion in Nephrology and Hypertension 20: 62–68.

78.

Ruan

Yang

Wang

, et al. (2020) Clinical predictors of mortality due to COVID-19 based on an analysis of data of 150 patients from Wuhan, China. Intensive Care Medicine 46: 846–848.

79.

Deng

Zhang

, et al. (2020) Suspected myocardial injury in patients with COVID-19 evidence from front-line clinical observation in Wuhan, China. International Journal of Cardiology 311: 116–121.

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