Abstract
Background The coronavirus disease 2019 (Covid-19) outbreak is evolving rapidly worldwide.
Objective To evaluate the risk of serious adverse outcomes in patients with coronavirus disease 2019 (Covid-19) by stratifying the comorbidity status.
Methods We analysed the data from 1590 laboratory-confirmed hospitalised patients 575 hospitals in 31 province/autonomous regions/provincial municipalities across mainland China between December 11th, 2019 and January 31st, 2020. We analyse the composite endpoints, which consisted of admission to intensive care unit, or invasive ventilation, or death. The risk of reaching to the composite endpoints was compared according to the presence and number of comorbidities.
Results The mean age was 48.9 years. 686 patients (42.7%) were females. Severe cases accounted for 16.0% of the study population. 131 (8.2%) patients reached to the composite endpoints. 399 (25.1%) reported having at least one comorbidity. The most prevalent comorbidity was hypertension (16.9%), followed by diabetes (8.2%). 130 (8.2%) patients reported having two or more comorbidities. After adjusting for age and smoking status, COPD [hazards ratio (HR) 2.681, 95% confidence interval (95%CI) 1.424–5.048], diabetes (HR 1.59, 95%CI 1.03–2.45), hypertension (HR 1.58, 95%CI 1.07–2.32) and malignancy (HR 3.50, 95%CI 1.60–7.64) were risk factors of reaching to the composite endpoints. The HR was 1.79 (95%CI 1.16–2.77) among patients with at least one comorbidity and 2.59 (95%CI 1.61–4.17) among patients with two or more comorbidities.
Conclusion Among laboratory-confirmed cases of Covid-19, patients with any comorbidity yielded poorer clinical outcomes than those without. A greater number of comorbidities also correlated with poorer clinical outcomes.
Abstract
The presence and number of comorbidities predicted clinical outcomes of Covid-19.
Introduction
Since November 2019, the rapid outbreak of coronavirus disease 2019 (Covid-19), which arose from severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, has recently become a public health emergency of international concern [1]. Covid-19 has contributed to an enormous adverse impact globally. Hitherto, there have been 109 577 laboratory-confirmed cases and 3809 deaths globally as of March 10th, 2020 [2].
The clinical manifestations of Covid-19 are, according to the latest reports [3–12], heterogeneous. On admission, 20–51% of patients reported as having at least one comorbidity, with diabetes (10–20%), hypertension (10–15%) and other cardiovascular and cerebrovascular diseases (7–40%) being most common [3, 4, 6]. Previous studies have demonstrated that the presence of any comorbidity has been associated with a 3.4-fold increased risk of developing acute respiratory distress syndrome in patients with H7N9 infection [13]. Similar with influenza [14–18], Severe Acute Respiratory Syndrome coronavirus (SARS-CoV) [19] and Middle East Respiratory Syndrome coronavirus (MERS-CoV) [20–28], Covid-19 more readily predisposed to respiratory failure and death in susceptible patients [4, 5]. Nonetheless, previous studies have been certain limitations in study design including the relatively small sample sizes and single center observations. Studies that address these limitations is needed to explore for the factors underlying the adverse impact of Covid-19.
Our objective was to evaluate the risk of serious adverse outcomes in patients with Covid-19 by stratification according to the number and type of comorbidities, thus unravelling the sub-populations with poorer prognosis.
Methods
Data sources and data extraction
This was a retrospective case study that collected data from patients with Covid-19 throughout China, under the coordination of the National Health Commission which mandated the reporting of clinical information from individual designated hospitals which admitted patients with Covid-19. After careful medical chart review, we compiled the clinical data of laboratory-confirmed hospitalised cases from 575 hospitals (representing 31.7% of the certified hospitals for admitting patients with Covid-19) between December 11th, 2019 and January 31st, 2020. The diagnosis of Covid-19 was made based on the World Health Organization interim guidance [29]. Because of the urgency of data extraction, complete random sampling could not be applied in our settings. All clinical profiles outside Hubei province were centrally provided by the National Health Commission. Three respiratory experts from Guangzhou were dispatched to Wuhan for raw data extraction from Wuhan JinYinTan Hospital where most cases in Wuhan were located. Our cohort included 132 patients from Wuhan JinYinTan Hospital, and one each from 338 hospitals. Our cohort represented the overall situation as of Jan 31st, taking into account the proportion of hospitals (∼ one fourth) and patient number (13.5%, 1590/11 791 cases) as well as the broad coverage (covering all major provinces/cities/autonomous regions). Confirmed cases denoted the patients whose high-throughput sequencing or real-time reverse-transcription polymerase chain reaction (RT-PCR) assay findings for nasal and pharyngeal swab specimens were positive [3]. See Online Supplement for further details. The interval between the potential earliest date of transmission source (wildlife, suspected or confirmed cases) contacts and the potential earliest date of symptom onset (i.e., cough, fever, fatigue, myalgia) was adopted to calculate the incubation period. In light that the latest date was recorded in some patients who had continuous exposure to contamination sources, the incubation periods of less than 1.0 day would not be included in our analysis. The incubation periods were summarised based on the patients who had delineated the specific date of exposure.
The clinical data (including recent exposure history, clinical symptoms and signs, comorbidities, and laboratory findings upon admission) were reviewed and extracted by experienced respiratory clinicians, who subsequently entered the data into a computerised database for further double-check of all cases. Manifestations on chest X-ray or computed tomography (CT) was summarised by integrating the documentation or description in medical charts and, if available, a further review by our medical staff. Major disagreement of the radiologic manifestations between the two reviewers was resolved by consultation with another independent reviewer. Because the disease severity reportedly predicted poorer clinical outcomes of avian influenza [13], patients were classified as having severe or non-severe Covid-19 based on the 2007 American Thoracic Society Infectious Disease Society of America guidelines [30], taking into account its global acceptance for severity stratification of community-acquired pneumonia although no validation was conducted in patients with viral pneumonia. The predictive ability of the need for ICU admission and mortality has been validated previously [31, 32]. Briefly, severe cases denoted at least one major criterion (septic shock requiring vasoactive medications, or respiratory failure requiring mechanical ventilation), or at least three minor criteria (respiratory rate being 30 times per minute or greater, oxygen index being 250 or lower, multiple lobe infiltration, delirium or loss of consciousness, blood urea nitrogen level being 20 mg·dL−1 or greater, blood leukocyte count being 4000 per deciliter or lower, blood platelet count being 100 000 per deciliter or lower, body temperature being lower than 36 degrees, hypotension necessitating vasoactive drugs for maintaining blood pressure).
Comorbidities were determined based on patient's self-report on admission. Comorbidities were initially treated as a categorical variable (Yes versus No), and subsequently classified based on the number (Single versus Multiple). Furthermore, comorbidities were sorted according to the organ systems (i.e. respiratory, cardiovascular, endocrine). Comorbidities that were classified into the same organ system (i.e. coronary heart disease, hypertension) would be merged into a single category.
The primary endpoint of our study was a composite measure which consisted of the admission to intensive care unit (ICU), or invasive ventilation, or death. This composite measure was adopted because all individual components were serious outcomes of H7N9 infections [13]. The secondary endpoint was the mortality rate.
Statistical analysis
Statistical analyses were conducted with SPSS software version 23.0 (Chicago, IL, USA). No formal sample size estimation was made because there has not been any published nationwide data on Covid-19. Nonetheless, our sample size was deemed sufficient to power the statistical analysis given its representativeness of the national patient population. Continuous variables were presented as means and standard deviations or medians and interquartile ranges (IQR) as appropriate, and the categorical variables were presented as counts and percentages. In light that no random sampling was conducted, all statistical analyses were descriptive and no p values would be presented for the statistical comparisons except for the Cox proportional hazards regression model. Cox proportional hazards regression models were applied to determine the potential risk factors associated with the composite endpoints, with the hazards ratio (HR) and 95% confidence interval (95%CI) being reported. Our findings indicated that the statistical assumption of proportional hazards analysis was not violated. Moreover, Cox regression model was considered more appropriate than logistic regression model because it has taken into account the potential impact of the various duration of follow-up from individual patients. The age and smoking status were adjusted for in the proportional hazards regression model because they have been recognised as the risk factors of comorbidities even in the general population. The smoking status was stratified as current smokers, ex-smokers and never smokers in the regression models.
Results
Demographic and clinical characteristics
The National Health Commission has issued 11 791 patients with laboratory-confirmed Covid-19 in China as of January 31st, 2020. At this time point for data cut-off, our database has included 1590 cases from 575 hospitals in 31 province/autonomous regions/provincial municipalities (see Online Supplement for details). Of these 1590 cases, the mean age was 48.9 years. 686 patients (42.7%) were females. 647 (40.7%) patients were managed inside Hubei province, and 1334 (83.9%) patients had a contact history of Wuhan city. The most common symptom was fever on or after hospitalisation (88.0%), followed by dry cough (70.2%). Fatigue (42.8%) and productive cough (36.0%) were less common. At least one abnormal chest CT manifestation (including ground-glass opacities, pulmonary infiltrates and interstitial disorders) was identified in more than 70% of patients. Severe cases accounted for 16.0% of the study population. 131 (8.2%) patients reached to the composite endpoints during the study (table 1). Overall, the median follow-up duration was 10 days (interquartile range: 8, 14).
Demographics and clinical characteristics of patients with or without any comorbidities
Presence of comorbidities and the clinical characteristics and outcomes of Covid-19
Of the 1590 cases, 399 (25.1%) reported having at least one comorbidity. The prevalence of specific comorbidities was: hypertension (269; 16.9%), other cardiovascular diseases (53.7%) cerebrovascular diseases (30; 1.9%), diabetes (130; 8.2%), hepatitis B infections (28; 1.8%), chronic obstructive pulmonary disease (24; 1.5%), chronic kidney diseases (21; 1.3%), malignancy (18; 1.1%) and immunodeficiency (3; 0.2%). None of the cases had physician-diagnosed asthma. At least one comorbidity was seen more commonly in severe cases than in non-severe cases (32.8% versus 10.3%). Patients with at least one comorbidity were older (mean: 60.8 versus 44.8 years), were more likely to have shortness of breath (41.4% versus 17.8%), nausea or vomiting (10.4% versus 4.3%), and tended to have abnormal chest X-ray manifestations (29.2% versus 15.1%) (table 1).
Clinical characteristics and outcomes of Covid-19 stratified by the number of comorbidities
We have further identified 130 (8.2%) patients who reported having two or more comorbidities. Two or more comorbidities were more commonly seen in severe cases than in non-severe cases (40.0% versus 29.4%). Patients with two or more comorbidities were older (mean: 66.2 versus 58.2 years), were more likely to have shortness of breath (55.4% versus 34.1%), nausea or vomiting (11.8% versus 9.7%), unconsciousness (5.1% versus 1.3%) and less abnormal chest X-ray (20.8% versus 23.4%) compared with patients who had single comorbidity (table 2).
Demographics and clinical characteristics of patients with 1 or ≥2 comorbidities
Clinical characteristics and outcomes of Covid-19 stratified by organ systems of comorbidities
A total of 269 (16.9%), 59 (3.7%), 30 (1.9%), 130 (8.2%), 28 (1.8%), 24 (1.5%), 21 (1.3%), 18 (1.1%) and 3 (0.2%) patients reported having hypertension, cardiovascular diseases, cerebrovascular diseases, diabetes, hepatitis B infections, COPD, chronic kidney diseases, malignancy and immunodeficiency, respectively. Severe cases were more likely to have hypertension (32.7% versus 12.6%), cardiovascular diseases (33.9% versus 15.3%), cerebrovascular diseases (50.0% versus 15.3%), diabetes (34.6% versus 14.3%), hepatitis B infections (32.1% versus 15.7%), COPD (62.5% versus 15.3%), chronic kidney diseases (38.1% versus 15.7%) and malignancy (50.0% versus 15.6%) compared with non-severe cases. Furthermore, comorbidities were more common patients treated in Hubei province as compared with those managed outside Hubei province as well as patients with an exposure history of Wuhan as compared with those without (table 3).
Demographics and clinical characteristics of patients stratified by different comorbidities
Prognostic analyses
Overall, 131 patients (8.3%) reached to the composite endpoints during the study. 50 patients (3.1%) died, 99 patients (6.2%) were admitted to the ICU and 50 patients (3.1%) received invasive ventilation. The composite endpoint was documented in 77 (19.3%) of patients who had at least one comorbidity as opposed to 54 (4.5%) patients without comorbidities. This figure was 37 cases (28.5%) in patients who had two or more comorbidities. Significantly more patients with hypertension (19.7% versus 5.9%), cardiovascular diseases (22.0% versus 7.7%), cerebrovascular diseases (33.3% versus 7.8%), diabetes (23.8% versus 6.8%), COPD (50.0% versus 7.6%), chronic kidney diseases (28.6% versus 8.0%) and malignancy (38.9% versus 7.9%) reached to the composite endpoints compared with those without (table 3).
Patients with two or more comorbidities had significantly escalated risks of reaching to the composite endpoint compared with those who had a single comorbidity, and even more so as compared with those without (all p<0.05, fig. 1). After adjusting for age and smoking status, patients with COPD (HR 2.68, 95%CI 1.42–5.05), diabetes (HR 1.59, 95%CI 1.03–2.45), hypertension (HR 1.58, 95%CI 1.07–2.32) and malignancy (HR 3.50, 95%CI 1.60–7.64) were more likely to reach to the composite endpoints than those without (fig. 2). Results of unadjusted analysis was presented in Table E1 and 2. Overall, findings of unadjusted and adjusted analysis were not materially altered. As compared with patients without comorbidity, the HR (95%CI) was 1.79 (95%CI 1.16–2.77) among patients with at least one comorbidity and 2.59 (95%CI 1.61–4.17) among patients with two or more comorbidities (fig. 2). Subgroup analysis by stratifying patients according to their age (<65 years versus ≥65 years) did not reveal substantial difference in the strength of associations between the number of comorbidities and mortality of Covid-19 (Table E3).
Comparison of the time-dependent risk of reaching to the composite endpoints. a) The time-dependent risk of reaching to the composite endpoints between patients with (orange curve) or without any comorbidity (dark blue curve). b) The time-dependent risk of reaching to the composite endpoints between patients without any comorbidity (orange curve), patients with a single comorbidity (dark blue curve), and patients with two or more comorbidities (green curve). Cox proportional hazard regression models were applied to determine the potential risk factors associated with the composite endpoints, with the hazards ratio (HR) and 95% confidence interval (95%CI) being reported.
Predictors of the composite endpoints in the proportional hazards model. Shown in the figure are the hazards ratio (HR) and the 95% confidence interval (95%CI) for the risk factors associated with the composite endpoints (admission to intensive care unit, invasive ventilation, or death). The comorbidities were classified according to the organ systems as well as the number. The scale bar indicates the HR. Cox proportional hazard regression models were applied to determine the potential risk factors associated with the composite endpoints, with the hazards ratio (HR) and 95% confidence interval (95%CI) being reported. The model has been adjusted with age and smoking status.
Discussion
Our study is the first nationwide investigation that systematically evaluates the impact of comorbidities on the clinical characteristics and prognosis in patients with Covid-19 in China. Circulatory and endocrine comorbidities were common among patients with Covid-19. Patients with at least one comorbidity, or more even so, were associated with poor clinical outcomes. These findings have provided further objective evidence, with a large sample size and extensive coverage of the geographic regions across China, to take into account baseline comorbid diseases in the comprehensive risk assessment of prognosis among patients with Covid-19 on hospital admission.
Overall, our findings have echoed the recently published studies in terms of the commonness of comorbidities in patients with Covid-19 [3–7]. Despite considerable variations in the proportion in individual studies due to the limited sample size and the region where patients were managed, circulatory diseases (including hypertension and coronary heart diseases) remained the most common category of comorbidity [3–7]. Apart from circulatory diseases, endocrine diseases such as diabetes were also common in patients with Covid-19. Notwithstanding the commonness of circulatory and endocrine comorbidities, patients with Covid-19 rarely reported as having comorbid respiratory diseases (particularly COPD). The reasons underlying this observation have been scant, but could have arisen from the lack of awareness and the lack of spirometric testing in community settings that collectively contributed to the under-diagnosis of respiratory diseases [33]. It should be stressed that the observed frequency of comorbidity may also reflect the transmission dynamics within particular age groups, case detection or testing practices or hospital admission policies during the early phases of the epidemic. Consistent with recent reports [3–7], the percentage of patients with comorbid renal disease and malignancy was relatively low. Our findings have therefore added to the existing literature the spectrum of comorbidities in patients with Covid-19 based on the larger sample sizes and representativeness of the whole patient population in China.
A number of existing literature reports have documented the escalated risks of poorer clinical outcomes in patients with avian influenza [14–18], SARS-CoV [19] and MERS-CoV infections [20–28]. The most common comorbidities associated with poorer prognosis included diabetes [25, 29], hypertension [28], respiratory diseases [19, 28], cardiac diseases [19, 28], pregnancy [16], renal diseases [28] and malignancy [19]. Our findings suggested that, similar with other severe acute respiratory outbreaks, comorbidities such as COPD, diabetes, hypertension and malignancy predisposed to adverse clinical outcomes in patients with Covid-19. The strength of association between different comorbidities and the prognosis, however, was less consistent when compared with the literature reports [16, 19, 25, 28]. For instance, the risk between cardiac diseases and poor clinical outcomes of influenza, SARS-CoV or MERS-CoV infections was inconclusive [16, 19, 25, 28]. Except for diabetes, no other comorbidities were identified to be the predictors of poor clinical outcomes in patients with MERS-CoV infections [25]. Few studies, however, have explored the mechanisms underlying these associations. Kulscar et al. showed that MERS-CoV infections resulted in prolonged airway inflammation, immune cell dysfunction and an altered expression profile of inflammatory mediators in diabetic mice models [27]. A network-based analysis indicated that SARS-CoV infections led to immune dysregulation that could help explain the escalated risk of cardiac diseases, bone diseases and malignancy [34]. Therefore, immune dysregulation and prolonged inflammation might be the key drivers of the poor clinical outcomes in patients with Covid-19 but await verification in more mechanistic studies.
It has been well accepted that some comorbidities frequently co-exist. For instance, diabetes [35] and COPD [36] frequently co-exist with hypertension or coronary heart diseases. Therefore, patients with co-existing comorbidities are more likely to have poorer baseline well-being. Importantly, we have verified the significantly escalated risk of poor prognosis in patients with two or more comorbidities as compared with those who had no or only a single comorbidity. Our findings implied that both the category and number of comorbidities should be taken into account when predicting the prognosis in patients with Covid-19.
Our findings suggested that patients with comorbidities had greater disease severity compared with those without. Furthermore, a greater number of comorbidities correlated with greater disease severity of Covid-19. The proper triage of patients should be implemented by carefully inquiring the medical history because this will help identify patients who would be more likely to develop serious adverse outcomes of Covid-19. Moreover, better protection should be given to the patients with COIVD-19 who had comorbidities upon confirmation of the diagnosis.
A main limitation was the self-report of comorbidities on admission. Under-reporting of comorbidities, which could have stemmed from the lack of awareness and/or the lack of diagnostic testing, might contribute to the underestimation of the true strength of association with the clinical prognosis. Under-reporting of comorbidities could also lead to over-estimation of strength of association with adverse outcome. However, significant under-reporting was unlikely because the spectrum of our report was largely consistent with existing literature [3–7] and all patients were subject to a thorough history taking after hospital admission. The relatively low age might help explain the low prevalence of COPD in our cohort. Moreover, the duration of follow-up was relatively short and some patients remained in the hospital as of the time of writing. More studies that explore the associations in a sufficiently long time frame are warranted. Caution should be exercised when extrapolating our findings to other countries where there are outbreaks of Covid-19 since the prevalence of comorbidities may differ among different countries. Therefore, future studies that include an external validation of the results would be desirable. Although the temperature and systolic blood pressure differed between some subgroups, they were unlikely to be clinically relevant. Finally, because of the rapid evolving outbreak globally, ongoing studies with the inclusion of more patients would be needed to increase the statistical power and lend support to subgroup analyses stratified by the specific comorbidities (i.e. COPD) and their association with the risk of death.
Conclusions
Among laboratory-confirmed cases of Covid-19, patients with any comorbidity yielded poorer clinical outcomes than those without. A greater number of comorbidities also correlated with poorer clinical outcomes. A thorough assessment of comorbidities may help establish risk stratification of patients with Covid-19 upon hospital admission.
Acknowledgment
We thank the hospital staff (see Supplementary Appendix for the full list) for their efforts in collecting the information. We are indebted to the coordination of Drs. Zong-jiu Zhang, Ya-hui Jiao, Bin Du, Xin-qiang Gao and Tao Wei (National Health Commission), Yu-fei Duan and Zhi-ling Zhao (Health Commission of Guangdong Province), Yi-min Li, Zi-jing Liang, Nuo-fu Zhang, Shi-yue Li, Qing-hui Huang, Wen-xi Huang and Ming Li (Guangzhou Institute of Respiratory Health) which greatly facilitate the collection of patient's data. Special thanks are given to the statistical team members Prof. Zheng Chen, Drs. Dong Han, Li Li, Zheng Chen, Zhi-ying Zhan, Jin-jian Chen, Li-jun Xu, Xiao-han Xu (State Key Laboratory of Organ Failure Research, Department of Biostatistics, Guangdong Provincial Key Laboratory of Tropical Disease Research, School of Public Health, Southern Medical University). We also thank Li-qiang Wang, Wei-peng Cai, Zi-sheng Chen (the sixth affiliated hospital of Guangzhou medical university), Chang-xing Ou, Xiao-min Peng, Si-ni Cui, Yuan Wang, Mou Zeng, Xin Hao, Qi-hua He, Jing-pei Li, Xu-kai Li, Wei Wang, Li-min Ou, Ya-lei Zhang, Jing-wei Liu, Xin-guo Xiong, Wei-juna Shi, San-mei Yu, Run-dong Qin, Si-yang Yao, Bo-meng Zhang, Xiao-hong Xie, Zhan-hong Xie, Wan-di Wang, Xiao-xian Zhang, Hui-yin Xu, Zi-qing Zhou, Ying Jiang, Ni Liu, Jing-jing Yuan, Zheng Zhu, Jie-xia Zhang, Hong-hao Li, Wei-hua Huang, Lu-lin Wang, Jie-ying Li, Li-fen Gao, Jia-bo Gao, Cai-chen Li, Xue-wei Chen, Jia-bo Gao, Ming-shan Xue, Shou-xie Huang, Jia-man Tang, Wei-li Gu, Jin-lin Wang (Guangzhou Institute of Respiratory Health) for their dedication to data entry and verification. We are grateful to Tecent Co. Ltd. for their provision of the number of certified hospitals for admission of patients with Covid-19 throughout China. Finally, we thank all the patients who consented to donate their data for analysis and the medical staffs working in the front line.
Footnotes
This article has supplementary material available from erj.ersjournals.com
Ethics approval: This study is approved by the ethics committee of the First Affiliated Hospital of Guangzhou Medical University.
Support statement: Supported by National Health Commission, Department of Science and Technology of Guangdong Province. The funder had no role in the conduct of the study. National Health Commission; Guangdong Science and Technology Department; DOI: http://dx.doi.org/10.13039/501100007162.
Author's contributions: W. J. G., W. H. L., J. X. H., and N. S. Z. participated in study design and study conception; W. H. L., Y. Z., H. R. L., Z. S. C., C. Q. O., L. L., P. Y. C., J. F. L., C. C. L., L. M. O., B. C., W. W. and S. X. performed data analysis; R. C. C., C. L. T., T. W., L. S., Z. Y. N., J. X., Y. H., L. L., H. S., C. L. L., Y. X. P., L. W., Y. L., Y. H. H., P. P., J. M. W., J. Y. L., Z. C., G. L., Z. J. Z., S. Q. Q., J. L., C. J. Y., S. Y. Z., L. L. C., F. Y., S. Y. L., J. P. Z., N. F. Z., and N. S. Z. recruited patients; W. J. G., J. X. H., W. H. L., and N. S. Z. drafted the manuscript; all authors provided critical review of the manuscript and approved the final draft for publication.
Conflict of interest: Dr. Guan has nothing to disclose.
Conflict of interest: Dr. Liang has nothing to disclose.
Conflict of interest: Dr. Zhao has nothing to disclose.
Conflict of interest: Dr. Liang has nothing to disclose.
Conflict of interest: Dr. Chen has nothing to disclose.
Conflict of interest: Dr. Li has nothing to disclose.
Conflict of interest: Dr. Liu has nothing to disclose.
Conflict of interest: Dr. Chen has nothing to disclose.
Conflict of interest: Dr. Tang has nothing to disclose.
Conflict of interest: Dr. Wang has nothing to disclose.
Conflict of interest: Dr. Ou has nothing to disclose.
Conflict of interest: Dr. Li has nothing to disclose.
Conflict of interest: Dr. Chen has nothing to disclose.
Conflict of interest: Dr. Sang has nothing to disclose.
Conflict of interest: Dr. Wang has nothing to disclose.
Conflict of interest: Dr. Li has nothing to disclose.
Conflict of interest: Dr. Li has nothing to disclose.
Conflict of interest: Dr. Ou has nothing to disclose.
Conflict of interest: Dr. Cheng has nothing to disclose.
Conflict of interest: Dr. Xiong has nothing to disclose.
Conflict of interest: Dr. Ni has nothing to disclose.
Conflict of interest: Dr. Xiang has nothing to disclose.
Conflict of interest: Dr. Hu has nothing to disclose.
Conflict of interest: Dr. Liu has nothing to disclose.
Conflict of interest: Dr. Shan has nothing to disclose.
Conflict of interest: Dr. Lei has nothing to disclose.
Conflict of interest: Dr. Peng has nothing to disclose.
Conflict of interest: Dr. Wei has nothing to disclose.
Conflict of interest: Dr. Liu has nothing to disclose.
Conflict of interest: Dr. Hu has nothing to disclose.
Conflict of interest: Dr. Peng has nothing to disclose.
Conflict of interest: Dr. Wang has nothing to disclose.
Conflict of interest: Dr. Liu has nothing to disclose.
Conflict of interest: Dr. Chen has nothing to disclose.
Conflict of interest: Dr. Li has nothing to disclose.
Conflict of interest: Dr. Zheng has nothing to disclose.
Conflict of interest: Dr. Qiu has nothing to disclose.
Conflict of interest: Dr. Luo has nothing to disclose.
Conflict of interest: Dr. Ye has nothing to disclose.
Conflict of interest: Dr. Zhu has nothing to disclose.
Conflict of interest: Dr. Cheng has nothing to disclose.
Conflict of interest: Dr. Ye has nothing to disclose.
Conflict of interest: Dr. Li has nothing to disclose.
Conflict of interest: Dr. Zheng has nothing to disclose.
Conflict of interest: Dr. Zhang has nothing to disclose.
Conflict of interest: Dr. Zhong reports grants from National Health Commission, grants from Department of Science and Technology of Guangdong Province, during the conduct of the study.
Conflict of interest: Dr. He has nothing to disclose.
- Received March 4, 2020.
- Accepted March 13, 2020.
- Copyright ©ERS 2020
This article is open access and distributed under the terms of the Creative Commons Attribution Non-Commercial Licence 4.0.
Reference
