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      Host susceptibility to severe COVID-19 and establishment of a host risk score: findings of 487 cases outside Wuhan

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          Abstract

          The recent outbreak of coronavirus disease 2019 (COVID-19), caused by a new zoonotic coronary virus, SARS-CoV-2 [1], is being a great threat to public health. Up to February 11, 2020, it is reported that over 70,000 persons have been infected with SARS-CoV-2 in China [2]. The COVID-19 caused by SARS-CoV-2 infection represents a spectrum of clinical severity [3–5]. Some patients are asymptomatic or have merely mild upper respiratory tract symptoms. However, SARS-CoV-2 causes pneumonia that can be severe and characterized by fever, cough, dyspnea, bilateral pulmonary infiltrates, and acute respiratory injury. It is estimated that approximately 20% of patients are developing severe respiratory illness, with the overall mortality around 2.3% [2]. Thereby, it is critical to identify individuals who confer intrinsic susceptibility to become severe or even critically ill upon infection, for the purposes of prevention and treatment, especially when there is no drug directly targeting at SARS-CoV-2 that has been proven to be clinically effective. In the study, we explored potential host risk factors associated with severe cases at admission in a retrospective cohort of 487 patients in Zhejiang Province of China and attempt to establish a score system to identify high-risk individuals. We reviewed medical records, laboratory findings, and pulmonary CT scan of each patient with COVID-19, provided by the local health authority and inputted into a pre-specified electronic data collection form. Clinical outcomes were followed up to February 17, 2020. The primary endpoint was occurrence of death and severe cases. A total of 487 COVID-19 patients were included for analysis, with 49 (10.1%) severe cases at admission. As shown in Table 1, severe cases are elderly (56 (17) vs. 45 (19), P < 0.001), with more male (73.5% vs. 50.9%, P = 0.003). They have a higher incidence of hypertension (53.1% vs. 16.7%, P < 0.001), diabetes (14.3% vs. 5.0%, P = 0.009), cardiovascular diseases (8.2% vs. 1.6%, P = 0.003), and malignancy (4.1% vs. 0.7%, P = 0.025), and less exposure to epidemic area (49.0% vs. 65.1%, P = 0.027), but more infected family members (P = 0.031). On multivariate analysis, elder age (OR 1.06 [95% CI 1.03–1.08], P < 0.001), male (OR 3.68 [95% CI 1.75–7.75], P = 0.001), and presence of hypertension (OR 2.71 [95% CI 1.32–5.59], P = 0.007) are independently associated with severe disease at admission, irrespective of adjustment of time to admission. Table 1 Demographic, epidermiological characteristics, and underlying comorbidities of patients with confirmed 2019-nCoV infection Variables Total (N = 487) Mild (N = 438) Severe (N = 49) P value Age (years) 46 (19) 45 (19) 56 (17) < 0.001 Sex  Male 259 (53.2%) 223 (50.9%) 36 (73.5%)  Female 228 (46.8%) 215 (49.1%) 13 (26.5%) 0.003 Occupation  Agricultural worker 140 (28.7%) 122 (27.9%) 18 (36.7%)  Self-employed 219 (45.0%) 203 (46.3%) 16 (32.7%)  Employee 82 (16.8%) 79 (18.0%) 3 (6.1%)  Retired 38 (7.8%) 26 (5.9%) 12 (24.5%)  Student 8 (1.6%) 8 (1.8%) 0 (0%) < 0.001 Smoking history  Yes 40 (8.2%) 34 (7.8%) 6 (12.2%)  No 434 (89.1%) 391 (89.3%) 43 (87.8%)  Unknown 13 (2.7%) 13 (2.7%) 0 (0%) 0.331 Comorbidities  Hypertension 99 (20.3%) 73 (16.7%) 26 (53.1%) < 0.001  Diabetes 29 (6.0%) 22 (5.0%) 7 (14.3%) 0.009  Cardiovascular disease 11 (2.3%) 7 (1.6%) 4 (8.2%) 0.003  Malignancy 5 (1%) 3 (0.7%) 2 (4.1%) 0.025  Chronic liver diseases 22 (4.5%) 20 (4.6%) 2 (4.1%) 0.877  Chronic renal diseases 7 (1.4%) 5 (1.1%) 2 (4.1%) 0.101  Others 32 (6.6%) 27 (6.1%) 5 (10.2%) 0.279 Exposure to confirmed cases 186 (38.2%) 173 (39.5%) 13 (26.5%) 0.077 Family cluster  0 392 (80.5%) 352 (80.4%) 40 (81.6%)  1 67 (13.8%) 63 (14.4%) 4 (8.2%)  2 12 (2.5%) 12 (2.7%) 0 (0%)  ≥ 3 16 (3.3%) 11 (2.5%) 5 (10.2%) 0.031 Recent travel or residence to/in epidemic area 309 (63.4%) 285 (65.1%) 24 (49.0%) 0.027 Time from onset of symptom to admission 2 (3) 2 (3) 3 (5) 0.10 Data are expressed as mean ± standard deviation (SD), median (interquartile range), or number (percent). Comparisons between mild and severe cases were performed by the Mann-Whitney U test or a chi-square test Then, we defined a host risk score on the basis of the three risk factors, to assess the intrinsic host susceptibility to develop severe cases of COVID-19 (Fig. 1a). As shown in Fig. 1b, a step-wise increase in the incidence of severe COVID-19 at admission was observed with the increment of the host risk score (P < 0.001). The performance of the score was also validated in 66 patients who presented mild at admission and were under follow-up during hospital stay. Fifteen patients progressed to severe COVID-19 within a median follow-up time of 15 days. No death was reported by the end of follow-up. A similar trend to the above was confirmed when analyzing the correlation between host risk score and occurrence of severe COVID-19 (P = 0.014) (see Fig. 1c). Fig. 1 Definition of host risk factor score and incidences of severe cases by host risk score. The host risk factor score was calculated by the sum of three variables (a). The incidences of severe cases at admission (b) or developing during hospitalization (c) were compared across the different score groups by a linear-by-linear association test In summary, by identifying host risk factors associated with severe COVID-19, this study shed light on the underlying mechanisms of disease progression. In particular, the major finding that hypertension is a host risk factor for severe COVID-19 may underscore the involvement of renin-angiotensin system (RAS) in the pathogenesis of this disease. Additionally, the host risk score provides a useful tool to identify high-risk individuals, which is helpful for designing specific strategies for prevention and treatment of this disease. But further studies, particularly those enrolling Wuhan patients, are needed to validate the findings.

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          Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China

          Summary Background A recent cluster of pneumonia cases in Wuhan, China, was caused by a novel betacoronavirus, the 2019 novel coronavirus (2019-nCoV). We report the epidemiological, clinical, laboratory, and radiological characteristics and treatment and clinical outcomes of these patients. Methods All patients with suspected 2019-nCoV were admitted to a designated hospital in Wuhan. We prospectively collected and analysed data on patients with laboratory-confirmed 2019-nCoV infection by real-time RT-PCR and next-generation sequencing. Data were obtained with standardised data collection forms shared by WHO and the International Severe Acute Respiratory and Emerging Infection Consortium from electronic medical records. Researchers also directly communicated with patients or their families to ascertain epidemiological and symptom data. Outcomes were also compared between patients who had been admitted to the intensive care unit (ICU) and those who had not. Findings By Jan 2, 2020, 41 admitted hospital patients had been identified as having laboratory-confirmed 2019-nCoV infection. Most of the infected patients were men (30 [73%] of 41); less than half had underlying diseases (13 [32%]), including diabetes (eight [20%]), hypertension (six [15%]), and cardiovascular disease (six [15%]). Median age was 49·0 years (IQR 41·0–58·0). 27 (66%) of 41 patients had been exposed to Huanan seafood market. One family cluster was found. Common symptoms at onset of illness were fever (40 [98%] of 41 patients), cough (31 [76%]), and myalgia or fatigue (18 [44%]); less common symptoms were sputum production (11 [28%] of 39), headache (three [8%] of 38), haemoptysis (two [5%] of 39), and diarrhoea (one [3%] of 38). Dyspnoea developed in 22 (55%) of 40 patients (median time from illness onset to dyspnoea 8·0 days [IQR 5·0–13·0]). 26 (63%) of 41 patients had lymphopenia. All 41 patients had pneumonia with abnormal findings on chest CT. Complications included acute respiratory distress syndrome (12 [29%]), RNAaemia (six [15%]), acute cardiac injury (five [12%]) and secondary infection (four [10%]). 13 (32%) patients were admitted to an ICU and six (15%) died. Compared with non-ICU patients, ICU patients had higher plasma levels of IL2, IL7, IL10, GSCF, IP10, MCP1, MIP1A, and TNFα. Interpretation The 2019-nCoV infection caused clusters of severe respiratory illness similar to severe acute respiratory syndrome coronavirus and was associated with ICU admission and high mortality. Major gaps in our knowledge of the origin, epidemiology, duration of human transmission, and clinical spectrum of disease need fulfilment by future studies. Funding Ministry of Science and Technology, Chinese Academy of Medical Sciences, National Natural Science Foundation of China, and Beijing Municipal Science and Technology Commission.
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            Author and article information

            Contributors
            jifang_sheng@zju.edu.cn
            Journal
            Crit Care
            Critical Care
            BioMed Central (London )
            1364-8535
            1466-609X
            18 March 2020
            18 March 2020
            2020
            : 24
            Affiliations
            ISNI 0000 0004 1803 6319, GRID grid.452661.2, State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, , The First Affiliated Hospital, College of Medicine, Zhejiang University, ; Qingchun Road, No. 79, Hangzhou, 310003 China
            Article
            2833
            10.1186/s13054-020-2833-7
            7081524
            32188484
            © The Author(s). 2020

            Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

            Funding
            Funded by: FundRef http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
            Award ID: 81670567
            Award ID: 81870425
            Award Recipient :
            Funded by: FundRef http://dx.doi.org/10.13039/501100012226, Fundamental Research Funds for the Central Universities;
            Categories
            Research Letter
            Custom metadata
            © The Author(s) 2020

            Emergency medicine & Trauma

            covid-2019, disease severity, risk factors, host susceptibility

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