Differences in the clinical characteristics and outcomes of COVID-19 patients in the epicenter and peripheral areas of the pandemic from China: a retrospective, large-sample, comparative analysis

doi:10.21203/rs.3.rs-52333/v2

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Research article

Differences in the clinical characteristics and outcomes of COVID-19 patients in the epicenter and peripheral areas of the pandemic from China: a retrospective, large-sample, comparative analysis

https://doi.org/10.21203/rs.3.rs-52333/v2

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published 24 Feb, 2021

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Background: There is limited information on difference of epidemiology, clinical characteristics and outcomes of the initial outbreak of the coronavirus disease (COVID-19) in Wuhan (the epicenter) and Sichuan (the peripheral area) in the early phase of the COVID-19 pandemic. This study was conducted to investigate the differences in the epidemiological and clinical characteristics of patients with COVID-19 between the epicenter and peripheral areas of pandemic and thereby generate information that would be potentially helpful in formulating clinical practice recommendations to tackle the COVID-19 pandemic.

Methods: The Sichuan & Wuhan Collaboration Research Group for COVID-19 established two retrospective cohorts that separately reflect the epicenter and peripheral area during the early pandemic. The epidemiology, clinical characteristics and outcomes of patients in the two groups were compared. Multivariate regression analyses were used to estimate the adjusted odds ratios (aOR) with regard to the outcomes.

Results: The Wuhan (epicenter) cohort included 710 randomly selected patients, and the peripheral (Sichuan) cohort included 474 consecutive patients. A higher proportion of patients from the periphery had upper airway symptoms, whereas a lower proportion of patients in the epicenter had lower airway symptoms and comorbidities. Patients in the epicenter had a higher risk of death (aOR=7.64), intensive care unit (ICU) admission (aOR=1.66), delayed time from illness onset to hospital and ICU admission (aOR=6.29 and aOR=8.03, respectively), and prolonged duration of viral shedding (aOR=1.64).

Conclusions: The worse outcomes in the epicenter could be explained by the prolonged time from illness onset to hospital and ICU admission. This could potentially have been associated with elevated systemic inflammation secondary to organ dysfunction and prolonged duration of virus shedding independent of age and comorbidities. Thus, early supportive care could achieve better clinical outcomes.

Infectious Diseases

COVID-19

case fatality

epicenter

peripheral area

pandemic

comparative analysis

In December 2019, an outbreak of pneumonia of unknown cause was identified in Wuhan, the capital of Hubei province in China. A novel coronavirus, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which had not been detected previously in humans, was identified subsequently as the cause by Chinese scientists[1]. The disease was named the coronavirus disease 2019 (COVID-19) by the World Health Organization (WHO). The clinical spectrum of COVID-19 appears to be wide, and ranges from self-limited mild upper respiratory tract illness to severe pneumonia necessitating hospitalization or death. The clinical characteristics of some COVID-19 case series in Wuhan, which was the epicenter of the pandemic, have been previously reported in detail. The reports indicated that 26% to 33% of patients required intensive care and 4% to 15% died[2-4].

After the outbreak of COVID-19 in Wuhan, the government of the Sichuan province has implemented strict measures to combat COVID-19. The Health Commission of Sichuan Province (HCSP) focused on traditional public health outbreak response tactics, including isolation, quarantine, social distancing, and community containment, as recommended by the National Health Commission of China. All medical resources were allocated by the HCSP to ensure efficient use. An expert panel drawn from multidisciplinary teams was established and comprised 125 physicians who were led by Dr. Wei Min Li and Dr. Zong An Liang (the corresponding authors of this study) since January 15, 2020. This expert panel soon released the emergency prevention and control guidelines for COVID-19 in the medical institutions of the Sichuan province[5]. Furthermore, we funded two additional important expert panels with psychological counseling[6] and traditional Chinese medicine as a complementary and alternative treatment option[7, 8]. Severely or critically ill patients would receive consultations with the expert panel team using the 5G network whenever necessary at 16:30 every day. There were 208 designated hospitals across the Sichuan Province that would be accessible for SARS-CoV-2-suspected or -confirmed individuals in every city. Accordingly, this arrangement resulted in an improved outcome in Sichuan province as one of the peripheral areas of the pandemic, with 2% to 10.1% of patients needing intensive care, and an approximately 1.0% morality rate [9-11] in recently published studies.

The factors underlying the significantly different clinical outcomes between the epicenter and peripheral areas affected by the pandemic remains largely unexplored. Recently, Liang et al.[12] observed the clinical characteristics and outcomes of hospitalized patients with COVID-19who were treated in Hubei (epicenter) or outside Hubei (non-epicenter). However, as theirs is a multicenter study, the possibility of selection bias for the included patients cannot be ruled out. Furthermore, hospitalized patients in Hubei but not in Wuhan, would not be well representative of the first-generation COVID-19 cases. Considering the rapidly increasing number of cases confirmed SARS-CoV-2 infection worldwide, the existing researches into the differences between the epicenter and peripheral areas of the pandemic in the clinical characteristics and outcomes of COVID-19 patients were insufficient.

Here, we performed a comparative study to analyze the differences in the epidemiological and clinical characteristics of patients with COVID-19 between the epicenter and peripheral areas of pandemic. This study could provide information that would be potentially helpful in formulating clinical practice recommendations to tackle the COVID-19 pandemic worldwide.

Study design and subjects

This was a retrospective study based on two cohorts evaluated by the Sichuan and Wuhan Collaboration Research Group for COVID-19, China. The Sichuan cohort, as the group of patients from the peripheral area of the pandemic, consisted of SARS-CoV-2-confirmed patients who were consecutively recruited from 41 designated hospitals until March 12, 2020. Based on the exposure history, we further divided the Sichuan cohort into two sub-cohorts, with or without Wuhan exposure history. The Wuhan cohort, drawn from the epicenter area of the pandemic, was formed using a computer-generated simple random sampling method that was applied to enroll subjects from two designated hospitals, namely the Wuhan Red Cross Hospital and Renmin Hospital of Wuhan University, Wuhan, China. All patients enrolled in this study were diagnosed with COVID-19 according to the interim guidance issued by the National Health Commission of China and the WHO[13]. SARS-CoV-2 infection was confirmed by a positive result on a real-time reverse-transcriptase-polymerase-chain-reaction of nasopharyngeal, pharyngeal, throat-swab or sputum specimens. Some of these patients were included in studies reported by Wei et al.[14], Xiong et al.[15] and Xiong et al.[16]; however, their study purposes are significantly different from that of this study.

Data collection

The medical records of patients with COVID-19 were reviewed by a trained research team. Epidemiological, demographic, clinical, laboratory, radiological characteristics, treatment and outcome data were collected by using standardized data collection forms (modified case record form for the clinical characterization of severe acute respiratory infection that was shared by the International Severe Acute Respiratory and Emerging Infection Consortium [ISARIC]) from the electronic medical records. The cutoff date was Mar 12, 2020. We collected details of the exposure history, clinical symptoms and signs, and laboratory findings on admission. Laboratory examinations were performed according to the clinical care needs of the patients. Data on radiological abnormalities were extracted from the selected documentation. Patients were excluded if their medical records were not available. A team of trained researchers abstracted the data and entered the structured spreadsheet. All data were cross-checked.

Study outcomes

The primary outcomes included death or mechanical ventilation involving admission to the intensive care unit (ICU) for mechanical ventilation or ward, if ventilation was not performed in the ICU due to overwhelming numbers of COVID-19 patients. The secondary outcomes were the rate of ICU admission, time from illness onset to ICU admission and discharge, hospital stay, and duration of viral shedding after COVID-19 onset. The shedding of SARS-CoV-2 was defined as two consecutive negative results with qPCR detection at 24-h intervals. The criteria for discharge were absence of fever for at least 3 days, substantial improvement in both lungs on chest computed tomography (CT), clinical remission of respiratory symptoms and comorbidities, and shedding of SARS-Cov-2.

Statistical analysis

Continuous variables were compared using the Student's t test or Mann-Whitney U test; categorical variables were compared by the chi-square test or Fisher’s exact test as appropriate. Logistic or linear regression was performed to identify clinical variables that were associated with outcomes. The detailed statistical analysis is described in the supplementary data.

Epidemiological and clinical characteristics at hospitalization

As of March 12, 2020, there were 538 patients with COVID-19 who were consecutively admitted to 41 designated hospitals in Sichuan Province, and a total of 1979 cases from the two hospitals in Wuhan were identified. Thus, the Sichuan cohort comprised 474 patients (Figure 1A), when 64 patients with inaccessible medical records were excluded. On the other hand, 35.9% (n=710) of all patients from Wuhan, who were selected using a computer-generated simple random sampling method, formed the Wuhan cohort. Epidemiological data indicated that the onset of SARS-CoV-2 infection in the Sichuan cohort first occurred in December 2019, and the first case was admitted to the designated hospital on January 16, 2020 (Figure 1B). The daily Wuhan-related exposure cases with onset of COVID-19 in the Sichuan cohort peaked on January 23, 2020, and those without Wuhan-related exposure peaked on February 1, 2020 (Figure 1C and 1D). In the Wuhan cohort, illness onset in the first case was noted on December 24, 2019, and the first hospitalization occurred on January 5, 2020 (Figure 2A). The median time from illness onset to hospital in the Sichuan cohort was significantly shorter than that in the Wuhan cohort (5.0 [2.0, 9.0] vs. 10.0 [7.0, 15.0] days, P<0.001). The Sichuan cohort had a lower proportion of patients with an exposure history than that in the Wuhan cohort (64.3% vs. 99.3%, P<0.001).

The demographic and clinical characteristics of these patients are shown in Table 1 and S1. Patients in the Sichuan cohort were younger (44 [32.0, 54.0] vs. 58 [43.0, 67.0] yrs, P<0.001), there were fewer females (46.4% vs. 54.1%, P=0.010), and included a higher number of current smokers (14.5% vs. 5.1%, P<0.001). Two patients (0.4%) in the Sichuan cohort and 13 (1.8%) in the Wuhan cohort were healthcare workers (P=0.033). The commonest comorbidity in both cohorts was hypertension (23.6%), followed by diabetes (11.9%). The Wuhan cohort had more cases with comorbidity (51.2% vs. 43.8%, P=0.012) as assessed by the Charlson Comorbidity Index[17] (CCI) (2.0 [2.0, 3] vs. 0 [0, 1.0], P<0.001). Fewer patients in the Sichuan cohort had a history of coronary heart disease (P =0.004), liver disease (P<0.001), stroke (P=0.026), hypertension (P<0.001), and malignancy (P=0.012) than those in the Wuhan cohort.

Fever was the commonest symptom and was present in 61.8% of patients in the Sichuan cohort or 65.1% of patients in the Wuhan cohort, but no significant difference was detected between the two groups. The Sichuan cohort had a higher incidence of productive cough than the Wuhan cohort (P=0.012). However, the Wuhan cohort seemed to have a higher symptomatic burden with regard to the lower respiratory tract, including shortness of breath (25.4% vs. 9.0%, P<0.001), chest distress (23.8% vs. 9.0%, P<0.001), wheeze (13.9% vs. 4.8%, P<0.001), and general symptomatic burden, including fatigue (36.2% vs. 22.3%, P<0.001), hemoptysis (3.0% vs. 1.1%, P=0.028), altered consciousness (1.8% vs. 0.2%, P=0.011), and diarrhea (12.1% vs. 6.3%, P=0.001). In contrast, the Sichuan cohort was more likely to have upper respiratory symptoms, including pharyngalgia (13.9% vs. 7.5%, P<0.001), rhinorrhea (5.0% vs. 1.4%, P<0.001), nasal obstruction (3.4% vs. 1.1%, P=0.007), and headache (10.1% vs. 4.6%, P<0.001) (Figure 3A). Different severity distributions were observed between the two cohorts (P<0.001), as assessed by CURB-65 and MuLBSTA (both P<0.001). More than 75% of patients in both cohorts had mild or general disease, although the Sichuan cohort had a higher proportion of severe cases (17.0% vs. 8.4%) and the Wuhan cohort had more critically ill patients (13.6% vs. 6.3%). Chest CT radiographs in the Wuhan cohort were more likely to show bilateral lung involvement (P=0.012) and consolidation (P=0.006).

Laboratory findings and treatments

A comparison of laboratory findings and treatments between the two cohorts is shown in Table 2. There was no difference in white blood cell count, lymphocyte count, prothrombin time, albumin, alanine aminotransferase, aspartate aminotransferase, procalcitonin and interleukin 6 (IL-6) between the two cohorts. The Sichuan cohort had lower neutrophil count (P<0.001), platelet count (P<0.001), D-dimer levels (P=0.001), and C-reactive protein levels (P<0.001) and higher levels of hemoglobin (P=0.015), activated partial thromboplastin time (P<0.001), creatinine (P=0.040), and creatine kinase (P < 0.001). Almost all patients received antiviral treatment in Sichuan (93.9%) or Wuhan (92.7%). Fewer patients in the Sichuan cohort received antibiotics (P<0.001), corticosteroids (P<0.001) and supplemental oxygen therapy (P<0.001).

Clinical outcomes

The clinical outcomes are summarized in Table 2. The case fatality rate in the Sichuan cohort was obviously lower than that in the Wuhan cohort (0.6% vs. 8.3%, P<0.001). However, there was no significant difference in the proportion of patients receiving noninvasive mechanical ventilation or invasive mechanical ventilation between the two cohorts (5.7% vs. 5.9%, P=0.872 and 1.7% vs. 1.4%, P=0.701). With regard to the secondary outcomes, the proportion of patients who were admitted to the ICU in the Sichuan cohort was significantly lower than that in the Wuhan cohort (6.3% vs. 13.6%, P<0.001). The time from illness onset to ICU admission and time from illness onset to discharge in the Sichuan cohort were evidently shorter than that in the Wuhan cohort (7.0 [4.0, 10.5] vs. 11.5 [8.8, 24.3] days, P<0.001 and 23.0 [18.0, 31.0] vs. 28.0 [18.0, 38.0] days, P<0.001). The hospital stay in the Sichuan cohort was much longer than that in the Wuhan cohort (17.0 [12.0, 24.0] vs. 14.0 [9.0, 24.0] days, P<0.001). In addition, the Wuhan cohort had a significantly prolonged duration of SARS-CoV-2 shedding than that in the Sichuan cohort (19.0 [13.0, 28.0] vs. 14.0 [10.0, 19.0] days, P<0.001).

Logistic regression analyses

Multivariable logistic regression models were used to explore the differences in clinical outcomes between the Sichuan and Wuhan cohorts (Table 3). The results showed that the Wuhan cohort had higher risk of death (aOR=7.64, 95% CI=[2.31, 25.27], P=0.001), ICU admission (aOR=1.66, 95% CI=[1.05, 2.63], P=0.031), delayed time from illness onset to hospital (aOR=6.29, 95% CI=[4.70, 8.40], P<0.001) and ICU admission (aOR=8.03, 95% CI=[1.74, 37.06], P<0.001) admissions, prolonged duration of viral shedding after COVID-19 onset (aOR=1.64, 95% CI=[1.15, 2.33], P=0.006), a decreased hospital stay (aOR=0.41, 95% CI=[0.32, 0.53], P<0.001) after adjusting for age, sex, smoking status and the CCI. There was no difference in time from illness onset to discharge (aOR=0.99, 95% CI=[0.77, 1.28], P=0.968) after adjusting for these confounders. When we additionally adjusted for time from illness onset to hospitalization, the risk of the Wuhan cohort was nearly unchanged; however, the Sichuan cohort had a lower risk for extended time from illness onset to discharge (aOR=0.46, 95% CI=[0.34, 0.63], P<0.001).

In the overall study population of COVID-19 patients from the two cohorts, we constructed multivariable logistic regression models to detect the risk factors at admission for death, ICU admission, mechanical ventilation and duration of viral shedding after COVID-19 onset (Table 4). After adjusting for the cohort sites, sex, age, smoking status and the CCI, we found that white blood cells (>10×10⁹/L), neutrophils (>6.3×10⁹/L), lymphocytes (>1.0×10⁹/L), hemoglobin (<90 g/L), D-dimer (>0.5 mg/L), creatine kinase (>185 IU/L), hyper-sensitive troponin I (>0.04 ng/mL), alanine aminotransferase (>50 IU/L), aspartate aminotransferase (>40 IU/L), procalcitonin (>0.5 ng/mL) and delayed hospitalization were associated with death, ICU admission and mechanical ventilation. In addition, we found that time from illness onset to hospitalization was associated with prolonged duration of virus shedding (adjusted β=0.11, 95% CI= [0.03, 0.24], P=0.009).

We analyzed the relationship between the delay in hospitalization and elevated systemic inflammation and features of organ dysfunction. The time from illness onset to hospitalization was positively correlated with systemic inflammatory cells such as white blood cells (r=0.086, P=0.004), neutrophils (r=0.089, P=0.003), eosinophils (r=0.116, P<0.001), platelets (r=0.212, P<0.001), and inflammatory biomarkers, such as D-dimer (r=0.101, P=0.004), procalcitonin (r=−0.093, P=0.019), features of organ dysfunction, such as hemoglobin (r=−0.155, P<0.001), BUN (r=0.10, P=0.002), creatine (r=−0.094, P=0.003), albumin (r=−0.263, P<0.001), and APTT (r=−0.247, P<0.001) (Table 5). After adjusting for age, the correlations of delay in hospitalization with BUN and D-dimer did not achieve statistical significance, which indicated that the delay in hospitalization was independent of age. We further analyzed the relationship between the delay in hospitalization and ICU admission with elevated systemic inflammation after adjusting for age, sex, smoking, and steroid use. In general, the relationship between the delay in hospitalization and elevated systemic inflammation did not change, which implied that these relationships were independent of age, sex, smoking, and steroid use.

Subgroup analyses between Sichuan sub-cohorts with vs. without Wuhan-related exposure

There was almost no difference in clinical characteristics and outcomes between the two sub-cohorts with and without Wuhan-related exposure in Sichuan. Detailed information is provided in Supplementary Data (Tables S2, S3 and S4).

Sichuan sub-cohort with Wuhan-related exposure vs. Wuhan cohort

The differences in the clinical characteristics and outcomes between the Sichuan sub-cohort with Wuhan-related exposure and Wuhan cohort were similar to the differences between the Sichuan and Wuhan cohorts. The results are described in detail in the Supplementary Data (Tables S5, S6 and S7).

To the best of our knowledge, there exists a paucity in information obtained from a comparative large-sample study on the differences in epidemiology, clinical characteristics and outcomes of patients with COVID-19 between the epicenter (Wuhan) and the peripheral areas of pandemic. This comparative study provides important insights. First, the outbreak and transmission of COVID-19 within the region of Sichuan as the peripheral epidemic area has been well contained within two months through the use of traditional public health outbreak response tactics. Second, the Sichuan cohort is characterized by a higher incidence of upper airway symptoms, whereas the Wuhan cohort was older, had fewer lower airway symptoms and comorbidities, and had elevated pivotal systemic inflammation indicative of organ dysfunction as well as worse clinical outcomes independent of sex, age, smoking and comorbidities. Third, the subgroup analysis indicated that, within the Sichuan cohort, the patients with Wuhan-related exposure had similar clinical features and outcomes to those with non-Wuhan-related exposure. Fourth, the Wuhan-related exposure patients in the Sichuan cohort had improved clinical outcomes than those in the Wuhan cohort, although these two groups of patients had a similar Wuhan-related exposure history.

As indicated in recently published studies[12], the COVID-19 patients in Wuhan, at the epicenter area of the epidemic, were older, had more co-existing conditions assessed by the CCI, had extended time from illness onset to hospitalization, and included more severely ill patients. However, the Sichuan cohort, as the peripheral area, had some characteristics features. First, there were fewer healthcare workers in the Sichuan cohort than in the Wuhan cohort, which could be at least partially explained by the insufficient implementation of precautions and the overwhelmed health system during the earlier stage of this outbreak in Wuhan. Second, intriguingly, there was a higher incidence of upper airway symptoms, rather than high incidence of lower airway symptoms in the Wuhan cohort at the epicenter epidemic, which was similar to the findings from exported cases in Singapore[18]. Accordingly, the exported patients from the epicenter were usually diagnosed with a “common cold” at the beginning of the COVID-19 outbreak. The different populations, the airway proliferation location, or the evolution of SARS-CoV-2 possibly could account for these differential symptoms[18-21]. Third, within the consecutively recruited cases in the Sichuan cohort as a well-defined population, the subgroup analyses indicated a higher proportion of males and older patients among the non-Wuhan-related exposure patients, which supported the theory of the propensity for SARS-CoV-2 infection in males and elders[9, 22, 23]. Recent studies from the USA and Italy have reported that a greater proportion of elderly and male COVID-19 patients would experience more critical illness[24, 25].

Until now no antiviral treatment for COVID-19 has proven effective, and supportive care is the mainstay of treatment is. Compared with the Wuhan cohort, the use of antibiotics (i.e. cephalosporin and quinolones) and glucocorticoids in the Sichuan cohort decreased by 26.4% and 16.1%, respectively. These results could possibly be explained as follows. First, as indicated earlier, the expert panel drawn from the multidisciplinary team established by HCSP together developed and adjusted the treatment plan for severely or critically ill patients according to the interim guidance from the National Health Commission of China and the WHO across the 208 designated hospitals in Sichuan by using the 5G network every day. Accordingly, the use of systemic corticosteroids was strictly managed and they were not routinely administered for the treatment of COVID-19 patients. Second, the COVID-19 patients in the Wuhan cohort would actually be more severe or critically ill, which was supported by the increased use of supplemental oxygen in case of acute hypoxia. In addition, prone-position ventilation, physical rehabilitation and a variety of traditional Chinese medicines were used more often in Sichuan under the guidance of the expert panel; however, this aspect needs to be investigated further in randomized controlled trials[7, 8].

In terms of clinical outcomes, several important findings were identified in this study. An epidemic outbreak provided an opportunity to obtain important information, some of which were associated with a limited window of opportunity. This study showed that there was a delay from illness onset to hospitalization in the Wuhan cohort, which might be an important risk factor for the progression of COVID-19. Multivariate regression analysis showed that the time from illness onset to hospitalization was significantly associated with mortality and ICU admission, which suggested some important implications with regard to the pathogenesis of SARS-CoV-2 and may provide insights into a unique window of opportunity for intervention[7]. Liang et al.[12] recently found that Wuhan-related exposure patients have worse clinical outcomes compared with the non-Wuhan-related exposure cases; they attributed the attenuated disease to the onward transmission of SARS-CoV-2. In fact, this is paradoxical to the findings reported from Liang et al.’s study[12] because the relationship between Wuhan-related exposure and prognosis disappeared after adjusting for confounders. Our study firstly found that COVID-19 patients in the Wuhan cohort had worse clinical outcomes including case fatality rate, ICU admission, and duration of virus shedding, independent of sex, age, smoking, comorbidities, and even time from illness onset to hospitalization. The severity of COVID-19 and the shortage of medical resources would partly account for these worse outcomes. For example, during an earlier stage of the outbreak, some patients would not have received sufficient oxygen support because of insufficient oxygen pressure.

The duration of infectious virus replication is an important factor in assessing the risk of transmission and for guiding decisions on the isolation of patients; however, the duration of SARS-CoV-2 RNA detection has not been well explored. Our study found that the Wuhan cohort in the epicenter area had the prolonged virus shedding, which may contribute to the disease severity and clinical course[26, 27]. Furthermore, we found for the first time that the duration of virus shedding was independently associated with age and time from illness onset to hospitalization. Our findings are supported by those of other studies. Liu et al.[28] found that the viral load in severe cases was higher than that of mild cases, which had early viral shedding. Wolfel et al.[29] found that virus shedding in the upper airway, which is the location of mild COVID-19, was very high during the first week of symptoms, whereas shedding of viral RNA from sputum derived from the lower airway, which is the region of general to critical illness in COVID-19, outlasts the disappearance of symptoms. Xu et al.[30] found that elderly patients had prolonged virial shedding, but the correlation of age with the duration of viral shedding disappeared after adjusting for confounders, although this might be partly attributed to the small sample size.

As the pandemic evolves, mutations and natural selection of SARS-CoV-2 inevitably occur, although this virus a lower mutation rate than that of other RNA viruses[31]. The China National Center for Bioinformation aligned 77,801 genome sequences of SARS-CoV-2 that were detected globally and identified a total of 15,018 mutations[32]. Studies have shown that mutations play an important role in the virulence and infectivity of SARS-CoV-2, although no significant association was found between mutations and outcomes pertaining to hospitalization or death[33-35]. Thus, it is unclear whether the different clinical outcomes of patients with COVID-19 between the epicenter and peripheral areas affected by the pandemic are due to mutations in SARS-CoV-2.

This large-sample comparative study provides informative insights into the differences in epidemiology, clinical characteristics and outcomes of patients with COVID-19 between the epicenter (Wuhan) and peripheral (Sichuan) areas of the pandemic. However, there are several limitations that need to be addressed. First, due to the retrospective study design, data generation was clinically driven, and not all laboratory data were available for all patients. Accordingly, the missing data for some patients may have biased the findings. Second, the Sichuan cohort, which represented the peripheral area of the COVID-19 pandemic, was incomplete although consecutive patients accounting for 88.1% of total cases with COVID-19 were recruited from 41 designated hospitals in Sichuan. Third, we did not analyze the genetic diversity of virus strains and the evolutionary history, which may well explain these biological differences between the epicenter and peripheral areas affected by the pandemic.

This comparative study found that there were significant differences in the epidemiology, clinical characteristics, and outcomes of patients with COVID-19 between the epicenter and peripheral areas affected by the pandemic. The worse outcomes in the epicenter could be partly explained by the overwhelming of health systems and the delayed time from illness onset to hospitalization that was associated with elevated systemic inflammation could indicate organ dysfunction and prolonged duration of virus shedding, independent of sex, age, smoking and comorbidities. This indicates potential implications that are of clinical relevance in interventions for COVID-19, wherein urgent or early supportive care would achieve improved clinical outcomes, including a lower mortality rate, although no proven effective therapies for this virus currently exist. However, no differences were found in the epidemiology, clinical characteristics, and outcomes between the first generation and secondary generation of patients in the peripheral area of pandemic. Biological differences accounting for the differences between the Wuhan-related exposure patients in the Sichuan cohort and Wuhan cohort need to be further investigated.

aOR: adjusted odds ratio

CCI: Charlson comorbidity index

CI: confidence interval

COVID-19: coronavirus disease 2019

HCSP: The Health Commission of Sichuan Province

ISARIC: International Severe Acute Respiratory and Emerging Infection Consortium

OR: odds ratio

SARS-CoV-2: severe acute respiratory syndrome coronavirus 2

WHO: world health organization

Ethics approval and consent to participate

This study was approved by the Biological and Medical Ethics Committee of West China Hospital (No. 2020-304 and 2020-126) and the Ethic Committee of Renmin Hospital of Wuhan University (No. WDRY2020-K068). Written informed consent from each participant was waived for design of a retrospective study.

Consent for publication

Not applicable.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Competing interests

The authors declare that they have no competing interests.

Funding

This study was supported by the Sichuan Provincial Program for Diagnostic and Treatment of COVID-19 (No. 2020YFS002 and 2020YFS005), and the National Key Development Plan for Precision Medicine Research (2017YFC091004), and the National Natural Science Foundation of China (No. 81920108002). Funding sources played no role in the study design, data analysis, decision to publish, or preparation of the manuscript.

Author's contributions

GW, FML, DL, NX, JFL, ZAL, WML had roles in the study design, data analysis, data interpretation, and writing of the manuscript. YW, HC, PWT, TF, LT, HY, LW, MF, ZN, BW, ZFS, XLW, XT, MX, HJW, XYL, BL, CJ, JX, JS had roles in the collection, processing, cleaning, and interpretation of data. All authors read and approved the final manuscript.

Acknowledgments

We would like to thank all the patients who donate their data for this analysis and all medical staffs working in the front line for taking care of the patients. We show thanks to Dr. Xuan Zhang, PhD, Dr. Xin Zhang, MD, Dr. Ting Wang, MD, and Dr. Ji Wang, MD, for their statistical analyses and editing work. We are indebted to the coordination of Ms. Jun Hua Li from the Health Commission of Sichuan Province, and Mr. Nian Li and Ms. Bei Bai from West China Hospital, Sichuan University, who greatly facilitate the collection of patients’ data.

We appreciate the expert panel from multiple disciplines team as follows: (1) Respiratory and Critical Care Medicine: Zong An Liang, Feng Min Luo, Chun Tao Liu, Hong Fan, Dan Liu, He Yu at West China Hospital, Sichuan University, Yang Yang from Sichuan Academy of Medical Sciences, Sichuan Provincial People's Hospital ,Song Ping Wang, Wen Jun Wang from Affiliated Hospital of Southwest Medical University, Xiao Ju Chen, Li Jiang at Affiliated Hospital of North Sichuan Medical College, Zhen Liang Xiao at General Hospital of Western War Zone, Qing Song Huang, Yan Dong at Affiliated Hospital of Chengdu University of Traditional Chinese Medicine, De Hai Yu from the Second Traditional Chinese Medicine Hospital of Sichuan, Xiao Hui Zhang from Sichuan Integrative Medicine Hospital, Su Hua Ao from Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University, Yong Sheng Wang from The first People's Hospital of Chengdu, Guo Ping Li from The Third People's Hospital of Chengdu, Ya Lin Liu, Gui Hui Wu at Public Health Clinical Center of Chengdu, Wei Guo Xu from Mianyang Central Hospital, Zheng Guang He from Suining Central Hospital; (2) Nosocomial infection: Zhi Yong Zong, Wei Jia Yin at West China Hospital, Sichuan University, Jian Jun Deng at The Second West China Hospital, Sichuan University, Jia Yu Wu at Sichuan Provincial Peoples Hospital, Rong Li Wang, Jiao Xia at The affiliated hospital of Southwest Medical University, Zong Ying Jing at The TSC hospital of Southwest Medical University, Yong Fang Liu, Bi Feng Xu at The affiliated hospital of North Sichuan Medical College, Zhang Chen at The General Hospital of Western theater Command, Wen Sheng Zhang at Affiliated Hospital of Chengdu University of TCM, Hua Guo at Chengdu Third People`s Hospital, Ping Chen, Xiao Fei Duan at Public Health and Clinical Center of Chengdu, Chun Hua Ma at Mianyang 404 Hospital; (3) Radiology: Bin Song, Hong Li Bai, Jian Qun Yu at West China Hospital, Sichuan University, Jin Jiang at Sichuan Provincial Peoples Hospital, Jian Shu, Fu Gang Hang at The affiliated hospital of Southwest Medical University, Tian Wu Chen, Qiong Hui Zhao at The affiliated hospital of North Sichuan Medical College, Rui Jiang at The General Hospital of Western theater Command, Ming Guo Xie at Affiliated Hospital of Chengdu University of TCM, Bin Yang at The TSC hospital of Southwest Medical University, Na Zhang, Xin Wei Wang at Public Health and Clinical Center of Chengdu, Qi Yu Liu at Mianyang Central Hospital; (4) Department of Laboratory Medicine: Kang Mei, Xie Yi at West China Hospital, Sichuan University, Jiang Li, Yu Hua from Sichuan Provincial People’s Hospital ,Liu Jin Bo, Li Bao Lin from Affiliated Hospital of Southwest Medical University, Zhang Guo Yuan, Huang Yi Shan from Affiliated Hospital of North Sichuan Medical College, Hu Zong Hai, Dan Gang from the General Hospital of Western Theater Command, Xiong Da Qian from Chengdu University of Traditional Chinese Medicine, Guo Yong Can from Southwest Medical University of Traditional Chinese Medicine, Li Qing Feng, Cao Ling from the Public Health Clinical Center of Chengdu; (5) Diagnosis and Treatment of Infectious Diseases: Tang Hong, Feng Ping, Lv Xiao Ju, Bai Lang at West China Hospital, Sichuan University, Yang Xing Xiang from Sichuan Provincial People’s Hospital, Huang Yong Mao, Huang Fu Li from Affiliated Hospital of Southwest Medical University, Xiong Liang Shi, Liu Feng Jun from Affiliated Hospital of North Sichuan Medical College, Zhang Ling ,Zeng Yi Lan from the General Hospital of Western Theater Command, He Sheng Hua from the Public Health Clinical Center of Chengdu;(6) Critical Care Medicine: Yan Kang, Xiao Dong Jin, Zhong Wei Zhang at West China Hospital, Sichuan University, Xiao Bo Huang from Provincial People’s Hospital, Xian Ying Lei, Chang Xue Wu at the Affiliated Hospital of Southwest Medical University, Ying Ge, Da Qing Wang at the Affiliated Hospital of North Sichuan Medical College, Fu Xiang Li, Zhang Wang at the General Hospital of Western Theater Command, Pei Yang Gao from Affiliated Hospital of Chengdu University of Traditional Chinese Medicine, Xiao Bin Li from Affiliated Hospital of Traditional Chinese Medicine of Southwest Medical University, Yun Feng Chen from Chengdu Integrated Traditional Chinese Medicine & Western Medicine Hospital, Chuan Zhang from Chengdu Third People’s Hospital, Hong Chen, Hong De Chen, Ling Zhang at Chengdu Public Health Clinical Medical Center; (7) Emergency treatment: Jun Zeng, Ping Zhou, Xiao Mei Wang at Provincial People’s Hospital, Yu Cao from West China Hospital, Sichuan University, Wu Zhong, Ying Liu at the Affiliated Hospital of Southwest Medical University, Shi Ping Liu, Zhi Jiang at the Affiliated Hospital of North Sichuan Medical College, Gui Sen Xu from the General Hospital of Western Theater Command, Yun Lu, Xiao Yun Zhang at Affiliated Hospital of Chengdu University of Traditional Chinese Medicine, Zhi Hong Zhang from Affiliated Hospital of Traditional Chinese Medicine of Southwest Medical University, Yu Fei Hou from Chengdu Emergency Command Center, Wen Jun Li, Bo Leng at Chengdu Public Health Clinical Medical Center; (8) Traditional Chinese Medicine: Shao Hong Chen, Cheng Shi He,Xiao Yu Hu, Kun Yang at Affiliated Hospital of Chengdu University of Traditional Chinese Medicine, Gang Suo from Sichuan Provincial Orthopedics Hospital.

We also show thanks to Li Yan A, Ling Chen, Jing Yu Cui, Ke Deng, Su Jun Deng, Xue Mei Fang, Min Feng, Xiao Min Fu, Yu Yu Han, Jia Xin Hou, Yue Hong Hu, Dan Huang, Qiao Yun Huang, Min Li, Ren Jiao Li, Xiao Qian Li, Yu Qi Li, Jun Zhe Liao, Ping Lin, Zhi Lin, Dan Liu, Jia Liu, Lei Liu, Sha Liu, Shi Yao Liu, Zhi Hao Liu, Ya Lan Luo, Zhi Wen Ma, Min Qi, Xing Yu Qiu, Fang Yu Shi, Tao Lin Qin, Xiao Ju Tang, Rui Tian, Xin Tian, Dan Wang, Ji Wang, Qin Wang, Ting Wang, Yi Wei Wang, Yu Wang, Jia Hao Wu, Wen Wen Wu, Lin Rui Xu, Zhuan Hua Yang, Dong Fan Ye, Yuan Ye, Ze Lin Yin, Yu Lai Yuan, Jing Xuan Zhang, Liang Liang Zhang, Xin Zhang, Ya Li Zhang, Yu Yang Zhang, Yu Chen Zhang, Li Chun Zhong, Qiao Zhou, Yu Yan Zhou, Wen Juan Zhu, Wen Jun Zhu, Ying Ying Zhu, En Qiang Chen, Ming Jin Dai, Hui Gao, Hong Bo Jia, Yang Li, Min Tao Lin, Qi Liu, Lu Lu, Ruo Ting Men, Zhi Ming Ni, Da Nan Pan, Yuan Ya Qin, Tian Rui Ren, Si Si Shen, Yan Jun Si, Jia Jia Song, Xing Bo Song, Li Yi Wang, Wen Xiu Wang, Yong Wang, Xi Zhan Weng, Qin Wu, Yu Jie Wu, Xian Xing, Rong Fang Yan, Zhi Qing Yan, Lan Yang, Le Tian Yang, Ying Ying Yang, Jia Yu Ye, Ling Zhang, Qi Zhang, Sheng Zhang, Ying Teng Zhang, Ying Ying Zhang, Yu Wei Zhang, Han Jiang Zeng for their dedication to data entry and verification.

We also show thanks to hospitals who recruited the included cases as following: West China Hospital of Sichuan University, The Public Health Clinical Center of Chengdu, People’s hospital of Jianyang, Pidu District People’s Hospital of Chengdu, Zigong First People’s Hospital, Jintang County First People’s Hospital, People’s Hospital of Deyang City, Sichuan Mianyang 404 Hospital, The Third People’s Hospital of Guangyuan, The Fourth People’s Hospital of Panzhihua, Luzhou Infectious Diseases Hospital, Suining Central Hospital, The First People’s Hospital of Neijiang, Neijiang Second Municipal People’s Hospital, People’s Hospital of Leshan, Affiliated Hospital of North Sichuan Medical College, The Second People’s Hospital of Yibin, Linshui County Peoples’s Hospital of Guangan, Yuechi County Peoples’s Hospital, Guang’an People’s Hospital (West China-Guang’an Hospital, Sichuan University), People’s Hospital of Huaying, Dazhou Central Hospital, Bazhong Central Hospital, Third Branch of Yaan People’s Hospital (Hospital of Infectious Diseases), Meishan City People’s Hospital, The First People’s Hospital of Ziyang, The First People’s Hospital of Liangshan Yi Autonomous Prefecture, Ganluo County People’s Hospital of Liangshan Yi Autonomous Prefecture, People’s Hospital of Aba Tibetan and Qiang Autonomous Prefecture, Ganzi Tibetan Autonomous Prefecture People’s Hospital, Nanchong Central Hospital, Pengan County People’s Hospital, People’s Hospital of Langzhong, Nanbu County People’s Hospital, The Fifth People’s Hospital of Nanchong, Yilong County People’s Hospital, Yingshan County People’s Hospital, Pinchang County People’s Hospital, Tongjiang County People’s Hospital, Medical Center Hospital of Qionglai, Jiangyou Infectious Diseases Hospital

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Table 1. Demographics and clinical characteristics of patients in Sichuan and Wuhan cohorts

Variable	Total	Sichuan cohort	Wuhan cohort	Statistics	P value
n	1184	474	710
Female, n (%)	604 (51.0)	220 (46.4)	384 (54.1)	6.693	0.010
Age, years	50.50(37.00,64.00)	44.00(32.00,54.00)	58.00(43.00,67.00)	12.054	<0.001
<18	18 (1.5)	16 (3.4)	2 (0.3)	87.521	<0.001
18~64	870 (73.9)	403 (85.2)	467 (66.2)
≥65	290 (24.6)	54 (11.4)	236 (33.5)
Exposure history, n (%)				527.493	<0.001
Travel in Wuhan	141 (11.9)	128 (27.0)	13 (1.8)
Residents in Wuhan	869 (73.4)	177 (37.3)	692 (97.5)
No exposure history	174 (14.7)	169 (35.7)	5 (0.7)
Health care workers, n (%)	15 (1.3)	2 (0.4)	13 (1.8)	4.532	0.033
Smoking, n (%)				26.843	<0.001
Current smoker	96 (9.3)	67 (14.5)	29 (5.1)
Ever smoker	42 (4.1)	18 (3.9)	24 (4.2)
No smoker	892 (86.6)	376 (81.6)	516 (90.7)
Any comorbidity	574 (48.2)	208 (43.8)	366 (51.2)	6.258	0.012
Asthma, n (%)	9 (0.8)	5 (1.1)	4 (0.6)	0.925	0.336
Coronary heart disease, n (%)	84 (7.1)	21 (4.4)	63 (8.8)	8.384	0.004
Chronic obstructive pulmonary disease, n (%)	24 (2.0)	9 (1.9)	15 (2.1)	0.060	0.807
Chronic lung disease, n (%)	35 (2.9)	15 (3.2)	20 (2.8)	0.130	0.718
Chronic kidney disease, n (%)	24 (2.0)	12 (2.5)	12 (1.7)	1.039	0.308
Liver disease, n (%)	90 (7.6)	56 (11.7)	34 (4.7)	25.018	<0.001
Nervous system disorder, n (%)	9 (0.8)	4 (0.8)	5 (0.7)	0.078	0.781
Cancer, n (%)	29 (2.4)	5 (1.1)	24 (3.4)	6.372	0.012
Diabetes, n (%)	142 (11.9)	48 (10.1)	94 (13.1)	2.530	0.282
Immune disease, n (%)	19 (1.6)	7 (1.5)	12 (1.7)	0.076	0.783
Dementia, n (%)	8 (0.7)	2 (0.4)	6 (0.8)	0.747	0.387
Malnutrition, n (%)	3 (0.3)	2 (0.4)	1 (0.1)	0.897	0.343
Hematological system diseases, n (%)	7 (0.6)	4 (0.8)	3 (0.4)	0.871	0.351
Stroke, n (%)	23 (1.9)	4 (0.8)	19 (2.7)	4.962	0.026
Hypertension, n (%)	281 (23.6)	76 (16.0)	205 (28.7)	25.405	<0.001
Charlson Comorbidity Index (CCI)	1.0 (0-2.0)	0 (0-1.0)	2 (2.0-3.0)	-9.190	<0.001
Disease severity status, n (%)				97.524	<0.001
Mild	194 (16.3)	28 (5.9)	166 (23.3)
General	725 (61.1)	337 (70.8)	388 (54.6)
Severe	141 (11.9)	81 (17.0)	60 (8.4)
Critical	127 (10.7)	30 (6.3)	97 (13.6)
Curb-65 score, n (%)
0-1	837 (90.5)	369 (96.3)	468 (86.3)	26.430	<0.001
2	68 (7.4)	12 (3.1)	56 (10.3)
3~5	20 (2.2)	2 (0.5)	18 (3.3)
MuLBSTA score	7.00(5.00,9.00)	5.00(5.00,9.00)	7.00(5.00,9.00)	3.96	<0.001
Laboratory findings
White blood cell count, ×10⁹ /L	5.45(4.22,7.07)	5.37(4.18,5.37)	5.58(4.22,7.28)	1.570	0.088
Neutrophil count, ×10⁹ /L	3.45(2.50,4.99)	3.45(2.53,4.73)	3.45(2.47,5.26)	2.723	<0.001
Lymphocyte count, ×10⁹ /L	1.18(0.83,1.63)	1.18(0.81,1.60)	1.18(0.84,1.64)	1.074	0.157
Eosinophil count, ×10⁹ /L	0.30(0.00,1.30)	0.20(0.01,0.80)	0.50(0.0,1.7)	5.751	<0.001
Haemoglobin, g/L	131.00(119.00,144.00)	137.00(126.00,151.00)	127.00(117.00,137.00)	8.049	0.015
Platelet count, ×10⁹ /L	197.00(148.00,262.50)	175.00(137.00,230.50)	215.00(165.00,281.00)	7.909	<0.001
Activated partial thromboplastin time, s	28.50(25.90,32.20)	30.90(27.70,34.90)	27.40(25.20,29.90)	10.129	<0.001
Prothrombin time, s	12.20(11.50,13.00)	12.60(11.70,13.30)	12.00(11.33,12.70)	2.052	0.115
D-dimer, mg/L	0.56(0.29,1.59)	0.50(0.22,1.17)	0.63(0.33,1.79)	3.194	0.001
Albumin, g/L	39.70(35.50,43.30)	43.00(39.60,45.70)	37.70(34.00,40.80)	11.556	0.128
Creatinine, μmol/L	63.00(51.00,76.15)	65.20(53.00,77.33)	62.00(50.70,75.00)	2.051	0.040
Creatine kinase, U/L	62.70(41.00,106.25)	71.00(50.00,122.00)	56.00(34.40,99.00)	5.181	<0.001
Alanine aminotransferase, U/L	23.55(16.00,39.05)	24.00(16.00,39.90)	23.00(15.48,39.00)	0.433	0.665
Aspartate aminotransferase, U/L	25.00(19.67,35.73)	25.60(20.00,35.00)	25.00(19.00,36.00)	0.459	0.647
C-reactive protein, mg/L	19.95(6.80,53.92)	10.16(2.67,24.72)	28.45(8.70,65.20)	6.066	<0.001
Procalcitonin, ng/mL	0.05(0.04,0.12)	0.06(0.04,0.17)	0.05(0.03,0.11)	2.06	0.039
Hypersensitive troponin I, pg/ml	0.01(0.01,0.01)	0.01(0.01,0.01)	0.02(0.01,0.05)	3.615	<0.001
Chest CT
Bilateral lungs involved, n (%)	741 (93.1)	328 (90.6)	413 (95.2)	6.363	0.012
Consolidation, n (%)	180 (19.6)	73 (16.0)	107 (23.2)	7.533	0.006
Ground-glass opacity, n (%)	666 (71.3)	328 (71.6)	338 (71.0)	0.042	0.837
Linear opacity, n (%)	257 (27.8)	112 (24.5)	145 (31.2)	5.200	0.023
Pleural effusion, n (%)	49 (5.4)	19 (4.2)	30 (6.6)	2.559	0.110

Table 2. Treatments and outcomes of the patients in Sichuan and Wuhan cohorts

Variable	Total	Sichuan cohort	Wuhan cohort	Statistics	P value
n	1184	474	710
Treatments
Antiviral treatment, n (%)	1110 (93.8)	448 (94.7)	662 (93.2)	1.067	0.302
Antibiotics, n (%)	698 (59.0)	204 (43.0)	494 (69.6)	82.734	<0.001
Penicillins, n (%)	49 (4.1)	30 (6.3)	19 (2.7)	9.561	0.002
Cephalosporins, n (%)	233 (19.7)	78 (16.5)	155 (21.8)	5.196	0.023
β-lactam antibiotic, n (%)	52 (4.4)	17 (3.6)	35 (4.9)	1.221	0.269
Aminoglycosides, n (%)	6 (0.5)	1 (0.2)	5 (0.7)	1.372	0.242
Macrolides, n (%)	28 (2.4)	10 (2.1)	18 (2.5)	0.223	0.637
Lincomycin, n (%)	3 (0.3)	1 (0.2)	2 (0.3)	0.056	0.813
Quinolones, n (%)	507 (42.8)	151 (31.9)	356 (50.1)	38.810	<0.001
Antifungal treatment, n (%)	35 (3.0)	15 (3.2)	20 (2.8)	0.120	0.729
Corticosteroids, n (%)	272 (23.0)	63 (13.3)	209 (29.4)	41.872	<0.001
Tranquilizing drug, n (%)	52 (4.4)	26 (5.5)	26 (3.7)	2.032	0.154
Intravenous immunoglobin, n (%)	9 (0.8)	3 (0.6)	6 (0.8)	0.170	0.680
Inotropic agents, n (%)	46 (3.9)	8 (1.7)	38 (5.3)	10.232	0.001
Muscle relaxant, n (%)	16 (1.4)	11 (2.3)	5 (0.7)	5.367	0.021
Oxygen therapy, n (%)	791(66.9)	273 (57.7)	518 (731)	30.175	<0.001
Prone position ventilation, n (%)	30 (2.5)	22 (4.6)	8 (1.1)	14.217	<0.001
Tracheotomy, n (%)	8 (0.7)	4 (0.8)	4 (0.6)		0.720*
Renal replacement, n (%)	9 (0.8)	5 (1.1)	4 (0.6)		0.497*
Blood transfusion, n (%)	165 (14.0)	30 (6.3)	135 (19.1)	38.587	<0.001
Nutrition support, n (%)	131 (11.1)	52 (11.0)	79 (11.2)	0.014	0.906
TCM treatments, n (%)	912 (77.0)	418 (88.2)	494 (69.6)	55.620	<0.001
Physiotherapy, n (%)	29 (2.4)	24 (5.1)	5 (0.7)	22.605	<0.001
Outcomes
ICU admission, n (%)	127 (10.7)	30 (6.3)	97 (13.7)	15.961	<0.001
Non-invasive mechanical ventilation, n (%)	69 (5.8)	27 (5.7)	42 (5.9)	0.025	0.875
Invasive mechanical ventilation, n (%)	18 (1.5)	8 (1.7)	10 (1.4)	0.148	0.700
ECMO, n (%)	3 (0.3)	1 (0.2)	2 (0.3)		1.000*
Death	62 (5.2)	3 (0.6)	59 (8.3)	33.758	<0.001
Time from illness onset to admission, days	8.00(4.00,13.00)	5.00(2.00,9.00)	10.00(7.00,15.00)	13.626	<0.001
Hospital length of stay, days	16.00 (9.00,24.00)	17.00 (12.00,24.00)	14.00(9.00, 24.00)	-2.726	<0.001
Time from illness onset to ICU admission, days	9.00(6.00,17.00)	7.00(4.00,10.50)	11.50(8.75,24.25)	3.192^a	<0.001
Time from hospital admission to ICU admission, days	3.00(0.00,9.00)	4.00(0.00,9.00)	3.00(0.00,10.50)	0.415	0.678
Time from illness onset to discharge, days	26.00(18.00,35.00)	23.00(18.00,31.00)	28.00(18.00,38.00)	5.693	<0.001
Time from illness onset to death, days	16.50(13.00,21.75)	13.00 (11, -)	17.00 (13.00,23.50)	1.240^a	0.235
Time from hospital admission to death, days	5.00(3.00,7.00)	10.00(6.00,-)	4.00(3.00,7.00)	1.427	0.153
Duration of viral shedding, days	14.00(9.00，22.00)	13.00(8.00,18.00)	17.00(11.00,27.00)	6.665	<0.001
* Fisher's exact test. ECMO, extracorporeal membrane oxygenation; TCM, traditional Chinese medicine.

Table 3. Risk of adverse outcomes in Wuhan cohort when taking Sichuan cohort as reference

Outcomes	Unadjusted			Adjusted#
Outcomes	OR	95%CI	P	OR	95%CI	P
ICU admission	2.347	1.531-3.597	<0.001	1.659	1.047-2.627	0.031
Non-invasive mechanical ventilation	1.044	0.635-1.718	0.865	0.651	0.376-1.127	0.125
Invasive mechanical ventilation	0.835	0.327-2.132	0.705	0.381	0.138-1.054	0.063
Tracheotomy	0.668	0.166-2.681-	0.569	0.225	0.050-1.015	0.052
Death	14.286	4.444- 45.455	<0.001	7.643	2.311-25.274	0.001
Time from illness onset to admission (>5 days)	6.849	5.208-9.009	<0.001	6.289	4.695-8.403	<0.001
Hospital length of stay (>17 days)	0.481	0.380-0.609	<0.001	0.411	0.316-0.533	<0.001
Time from illness onset to ICU admission (>7 days)	6.364	1.836-22.061	0.027	8.030	1.740-37.057	<0.001
Time from illness onset to discharge (>23 days)	1.180	0.935-1.489	0.163	0.995	0.772-1.281	0.968
Time from illness onset to death (>10 days)	4.706	0.399-55.447	0.218	4.731	0.314-71.265	0.261
Time from hospital admission to ICU admission (>4 days)	0.857	0.243-3.024	0.811	0.665	0.122-3.620	0.637
Time from hospital admission to death (>10 days)	0.426	0.035-5.161	0.502	0.155	0.007-3.694	0.249
Duration of viral shedding (> 13 days)	1.881	1.363-2.597	<0.001	1.640	1.153-2.333	0.006
# Adjusted for gender, age, smoking and Charlson Comorbidity Index.

Table 4. Regression analysis of risk factors for death, ICU admission and mechanical ventilation in all patients from Sichuan and Wuhan cohorts*

Variables	Death			ICU admission			Non-invasive mechanical ventilation			Invasive mechanical ventilation
Variables	aOR	95% CI	P	aOR	95% CI	P	aOR	95% CI	P	aOR	95% CI	P
White blood cell count, ×10⁹/L
<4	0.77	0.31-1.89	0.563	0.72	0.41-1.27	0.261	0.50	0.21-1.21	0.123	0.87	0.23-3.23	0.829
4~10	-	-	-	-	-	-	-	-	-	-	-	-
>10	8.74	4.10-18.63	<0.001	3.35	1.86-6.02	<0.001	5.00	2.51-9.80	<0.001	0.74	0.09-6.09	0.777
Neutrophil count, ×10⁹/L
<1.8	0.87	0.19-3.91	0.851	1.50	0.73-3.08	0.270	1.41	0.48-4.19	0.533	1.25	0.15-10.21	0.839
1.8~6.3	-	-	-	-	-	-	-	-	-	-	-	-
>6.3	7.93	4.13-15.24	<0.001	4.79	3.02-7.59	<0.001	6.49	3.63-11.59	<0.001	1.59	0.48-5.33	0.451
Lymphocyte count, <1.0×109/L	0.09	0.04-0.21	<0.001	0.36	0.24-0.55	<0.001	0.21	0.11-0.39	<0.001	0.14	0.03-0.66	0.013
Haemoglobin, <90 g/L	7.35	2.63-20.53	<0.001	2.64	1.07-6.52	0.035	2.98	0.98-9.07	0.055	3.42	0.54-21.74	0.193
Platelet count, <100×10⁹/L	2.35	0.83-6.69	0.109	0.51	0.17-1.51	0.223	0.41	0.09-1.79	0.234	0.61	0.07-5.22	0.649
D-dimer, ≥0.5mg/L	5.69	1.97-16.47	0.001	2.72	1.59-4.68	<0.001	3.39	1.58-7.25	0.002	3.86	.79-18.86	.095
Creatinine, >133 μmol/L	5.45	1.87-15.94	0.002	2.33	0.89-6.06	0.084	1.19	0.31-4.52	0.800	0.74	0.08-6.71	0.790
Creatine kinase, >185U/L	10.23	4.41-23.73	<0.001	3.17	1.72-5.82	<0.001	4.23	2.09-8.55	<0.001	2.33	0.63-8.54	0.204
Hypersensitive troponin I, >0.04 pg/ml	13.33	5.06-35.12	<0.001	3.89	1.60-9.48	0.003	2.60	0.76-8.84	0.128	10.24	0.82-127.94	0.071
Alanine aminotransferase, >50 U/L	2.63	1.22-5.69	0.014	1.70	1.01-2.85	0.045	1.74	0.89-3.41	0.103	1.28	0.33-4.94	0.719
Aspartate aminotransferase, >40 U/L	7.11	3.60-14.06	<0.001	2.36	1.48-3.77	<0.001	2.73	1.51-4.96	0.001	2.21	0.67-7.27	0.193
CRP, ≥10 mg/L	-	-	-	2.18	1.22-3.88	0.008	9.97	2.32-42.91	0.002	-	-	-
Procalcitonin, ≥0.5 ng/mL	23.87	7.62-74.75	<0.001	1.72	0.73-4.08	0.217	1.01	0.31-3.25	0.991	4.26	0.88-20.71	0.072
Chest CT
Bilateral lungs involved	0.37	0.04-3.17	0.363	1.41	0.42-4.75	0.577	1.31	0.30-5.69	0.723	0.31	0.03-2.91	0.307
Consolidation	0.84	0.23-3.10	0.794	1.09	0.63-1.89	0.771	1.53	0.78-2.99	0.214	1.04	0.21-5.02	0.965
Ground-glass opacity	7.33	0.94-57.28	0.057	0.83	0.51-1.34	0.437	1.11	0.58-2.15	0.749	4.11	0.50-33.46	0.187
Linear opacity	0.54	0.17-1.73	0.299	0.40	0.22-0.72	0.002	0.47	0.22-1.00	0.005	1.69	0.48-5.98	0.413
Pleural effusion	2.57	0.63-10.43	0.186	4.87	2.49-9.55	<0.001	6.92	3.26-14.70	<0.001	5.18	1.19-22.58	0.029
Time from illness onset to admission (>5 days)	2.40	1.00-5.78	0.049	1.42	0.60-3.37	0.424	1.62	0.86-3.03	0.133	1.34	0.44-4.09	0.609
* Adjusted for study region, gender, age, smoking, and Charlson Comorbidity Index.

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Differences in the clinical characteristics and outcomes of COVID-19 patients in the epicenter and peripheral areas of the pandemic from China: a retrospective, large-sample, comparative analysis

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