Comorbidity and its impact on 1590 patients with COVID-19 in China: a nationwide analysis

Wei-Jie Guan, Wen-Hua Liang, Yi Zhao, Heng-Rui Liang, Zi-Sheng Chen, Yi-Min Li, Xiao-Qing Liu, Ru-Chong Chen, Chun-Li Tang, Tao Wang, Chun-Quan Ou, Li Li, Ping-Yan Chen, Ling Sang, Wei Wang, Jian-Fu Li, Cai-Chen Li, Li-Min Ou, Bo Cheng, Shan Xiong, Zheng-Yi Ni, Jie Xiang, Yu Hu, Lei Liu, Hong Shan, Chun-Liang Lei, Yi-Xiang Peng, Li Wei, Yong Liu, Ya-Hua Hu, Peng Peng, Jian-Ming Wang, Ji-Yang Liu, Zhong Chen, Gang Li, Zhi-Jian Zheng, Shao-Qin Qiu, Jie Luo, Chang-Jiang Ye, Shao-Yong Zhu, Lin-Ling Cheng, Feng Ye, Shi-Yue Li, Jin-Ping Zheng, Nuo-Fu Zhang, Nan-Shan Zhong, Jian-Xing He, China Medical Treatment Expert Group for COVID-19, Wei-Jie Guan, Wen-Hua Liang, Yi Zhao, Heng-Rui Liang, Zi-Sheng Chen, Yi-Min Li, Xiao-Qing Liu, Ru-Chong Chen, Chun-Li Tang, Tao Wang, Chun-Quan Ou, Li Li, Ping-Yan Chen, Ling Sang, Wei Wang, Jian-Fu Li, Cai-Chen Li, Li-Min Ou, Bo Cheng, Shan Xiong, Zheng-Yi Ni, Jie Xiang, Yu Hu, Lei Liu, Hong Shan, Chun-Liang Lei, Yi-Xiang Peng, Li Wei, Yong Liu, Ya-Hua Hu, Peng Peng, Jian-Ming Wang, Ji-Yang Liu, Zhong Chen, Gang Li, Zhi-Jian Zheng, Shao-Qin Qiu, Jie Luo, Chang-Jiang Ye, Shao-Yong Zhu, Lin-Ling Cheng, Feng Ye, Shi-Yue Li, Jin-Ping Zheng, Nuo-Fu Zhang, Nan-Shan Zhong, Jian-Xing He, China Medical Treatment Expert Group for COVID-19

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 COVID-19 by stratifying the comorbidity status.

Methods: We analysed data from 1590 laboratory confirmed hospitalised patients from 575 hospitals in 31 provinces/autonomous regions/provincial municipalities across mainland China between 11 December 2019 and 31 January 2020. We analysed the composite end-points, which consisted of admission to an intensive care unit, invasive ventilation or death. The risk of reaching the composite end-points was compared according to the presence and number of comorbidities.

Results: The mean age was 48.9 years and 686 (42.7%) patients were female. Severe cases accounted for 16.0% of the study population. 131 (8.2%) patients reached the composite end-points. 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 (HR (95% CI) 2.681 (1.424-5.048)), diabetes (1.59 (1.03-2.45)), hypertension (1.58 (1.07-2.32)) and malignancy (3.50 (1.60-7.64)) were risk factors of reaching the composite end-points. The hazard ratio (95% CI) was 1.79 (1.16-2.77) among patients with at least one comorbidity and 2.59 (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.

Conflict of interest statement

Conflict of interest: Wei-jie Guan has nothing to disclose. Conflict of interest: Wen-hua Liang has nothing to disclose. Conflict of interest: Yi Zhao has nothing to disclose. Conflict of interest: Heng-rui Liang has nothing to disclose. Conflict of interest: Zi-sheng Chen has nothing to disclose. Conflict of interest: Yi-min Li has nothing to disclose. Conflict of interest: Xiao-qing Liu has nothing to disclose. Conflict of interest: Ru-chong Chen has nothing to disclose. Conflict of interest: Chun-li Tang has nothing to disclose. Conflict of interest: Tao Wang has nothing to disclose. Conflict of interest: Chun-quan Ou has nothing to disclose. Conflict of interest: Li has nothing to disclose. Conflict of interest: Ping-yan Chen has nothing to disclose. Conflict of interest: Ling Sang has nothing to disclose. Conflict of interest: Wei Wang has nothing to disclose. Conflict of interest: Jian-fu Li has nothing to disclose. Conflict of interest: Cai-chen Li has nothing to disclose. Conflict of interest: Li-min Ou has nothing to disclose. Conflict of interest: Bo Cheng has nothing to disclose. Conflict of interest: Shan Xiong has nothing to disclose. Conflict of interest: Zheng-yi Ni has nothing to disclose. Conflict of interest: Jie Xiang has nothing to disclose. Conflict of interest: Yu Hu has nothing to disclose. Conflict of interest: Lei Liu has nothing to disclose. Conflict of interest: Hong Shan has nothing to disclose. Conflict of interest: Chun-liang Lei has nothing to disclose. Conflict of interest: Yi-xiang Peng has nothing to disclose. Conflict of interest: Li Wei has nothing to disclose. Conflict of interest: Yong Liu has nothing to disclose. Conflict of interest: Ya-hua Hu has nothing to disclose. Conflict of interest: Peng has nothing to disclose. Conflict of interest: Jian-ming Wang has nothing to disclose. Conflict of interest: Ji-yang Liu has nothing to disclose. Conflict of interest: Zhong Chen has nothing to disclose. Conflict of interest: Gang Li has nothing to disclose. Conflict of interest: Zhi-jian Zheng has nothing to disclose. Conflict of interest: Shao-qin Qiu has nothing to disclose. Conflict of interest: Jie Luo has nothing to disclose. Conflict of interest: Chang-jiang Ye has nothing to disclose. Conflict of interest: Shao-yong Zhu has nothing to disclose. Conflict of interest: Lin-ling Cheng has nothing to disclose. Conflict of interest: Feng Ye has nothing to disclose. Conflict of interest: Shi-yue Li has nothing to disclose. Conflict of interest: Jin-ping Zheng has nothing to disclose. Conflict of interest: Nuo-fu Zhang has nothing to disclose. Conflict of interest: Nan-shan Zhong reports grants from the National Health Commission and Dept of Science and Technology of Guangdong Province, during the conduct of the study. Conflict of interest: Jian-xing He has nothing to disclose.

Copyright ©ERS 2020.

Figures

FIGURE 1
FIGURE 1
a) The time-dependent risk of reaching the composite end-points between patients with or without any comorbidity. b) The time-dependent risk of reaching the composite end-points between patients without any comorbidity, patients with a single comorbidity and patients with two or more comorbidities. Cox proportional hazard regression models were applied to determine the potential risk factors associated with the composite end-points, with the hazard ratio and 95% confidence interval being reported.
FIGURE 2
FIGURE 2
Predictors of the composite end-points in the proportional hazards model. Hazard ratio (95% confidence interval) are shown for the risk factors associated with the composite end-points (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 hazard ratio. Cox proportional hazard regression models were applied to determine the potential risk factors associated with the composite end-points, with the hazard ratio (95% confidence interval) being reported. The model has been adjusted with age and smoking status.

References

    1. World Health Organization. Date last accessed: 10 March 2020.
    1. World Health Organization. Coronavirus disease (COVID-19) situation reports Date last accessed: 10 March 2020.
    1. Huang C, Wang Y, Li X, et al. . Clinical features of patients with 2019 novel coronavirus in Wuhan, China. Lancet 2020; 395: 497–506. doi:10.1016/S0140-6736(20)30183-5
    1. Chen N, Zhou M, Dong X, et al. . Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: a descriptive study. Lancet 2020; 395: 507–513. doi:10.1016/S0140-6736(20)30211-7
    1. Wang D, Hu B, Hu C, et al. . Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan, China. JAMA 2020; in press []. doi:10.1001/jama.2020.1585
    1. Kui L, Fang YY, Deng Y, et al. . Clinical characteristics of novel coronavirus cases in tertiary hospitals in Hubei Province. Chin Med J 2020; in press []. doi:10.1097/CM9.0000000000000744
    1. Xu XW, Wu XX, Jiang XG, et al. . Clinical findings in a group of patients infected with the 2019 novel coronavirus (SARS-Cov-2) outside of Wuhan, China: retrospective case studies. BMJ 2020; 368: m606. doi:10.1136/bmj.m606
    1. Chan JF, Yuan S, Kok KH, et al. . A familial cluster of pneumonia associated with the 2019 novel coronavirus indicating person-to-person transmission: a study of a family cluster. Lancet 2020; 395: 514–523. doi:10.1016/S0140-6736(20)30154-9
    1. Zhang S, Li H, Huang S, et al. . High-resolution CT features of 17 cases of coronavirus disease 2019 in Sichuan province, China. Eur Respir J 2020; in press []. doi:10.1183/13993003.00334-2020
    1. Wang L, Gao YH, Iou L, et al. . The clinical dynamics of 18 cases of COVID-19 outside of Wuhan, China. Eur Respir J 2020; in press []. doi:10.1183/13993003.00398-2020
    1. Yao Y, Tian Y, Zhou J, et al. . Epidemiological characteristics of SARS-CoV-2 infections in Shaanxi, China by 8 February 2020. Eur Respir J 2020; in press []. doi:10.1183/13993003.00310-2020
    1. Guan WJ, Ni ZY, Hu Y, et al. . Clinical characteristics of coronavirus disease 2019 in China. N Engl J Med 2020; in press []. doi:10.1056/NEJMoa2002032
    1. Gao HN, Lu HZ, Cao B, et al. . Clinical findings in 111 cases of influenza A (H7N9) virus infection. N Engl J Med 2013; 368: 2277–2285. doi:10.1056/NEJMoa1305584
    1. Placzek HED, Madoff LC. Association of age and comorbidity on 2009 influenza A pandemic H1N1-related intensive care unit stay in Massachusetts. Am J Public Health 2014; 104: e118–e125. doi:10.2105/AJPH.2014.302197
    1. Mauskopf J, Klesse M, Lee S, et al. . The burden of influenza complications in different high-risk groups. J Med Economics 2013; 16: 264–277. doi:10.3111/13696998.2012.752376
    1. Shiley KT, Nadolski G, Mickus T, et al. . Differences in the epidemiological characteristics and clinical outcomes of pandemic (H1N1) 2009 influenza, compared with seasonal influenza. Infect Control Hosp Epidemiol 2010; 31: 676–682. doi:10.1086/653204
    1. Martinez A, Soldevila N, Romeo-Tamarit A, et al. . Risk factors associated with severe outcomes in adult hospitalized patients according to influenza type and subtype. PLoS One 2019; 14: e0210353.
    1. Gutiérrez-González E, Cantero-Escribano JM, Redondo-Bravo L, et al. . Effect of vaccination, comorbidities and age on mortality and severe disease associated with influenza during the season 2016–2017 in a Spanish tertiary hospital. J Infect Public Health 2019; 12: 486–491. doi:10.1016/j.jiph.2018.11.011
    1. Booth CM, Matukas LM, Tomlinson GA, et al. . Clinical features and short-term outcomes of 144 patients with SARS in the greater Toronto area. JAMA 2003; 289: 2801–2809. doi:10.1001/jama.289.21.JOC30885
    1. Alqahtani FY, Aleanizy FS, Ali Hadi Mohammed R, et al. . Prevalence of comorbidities in cases of Middle East respiratory syndrome coronavirus: a retrospective study. Epidemiol Infect 2018; 5: 1–5.
    1. Badawi A, Ryoo SG. Prevalence of comorbidities in the Middle East respiratory syndrome coronavirus (MERS-CoV). Int J Infect Dis 2016; 49: 129–133. doi:10.1016/j.ijid.2016.06.015
    1. Rahman A, Sarkar A. Risk factors for fatal Middle East Respiratory Syndrome coronavirus infections in Saudi Arabia: analysis of the WHO line list, 2013–2018. Am J Public Health 2019; 109: 1288–1293. doi:10.2105/AJPH.2019.305186
    1. Alanazi KH, Abedi GR, Midgley CM, et al. . Diabetes mellitus, hypertension, and death among 32 patients with MERS-CoV infection, Saudi Arabia. Emerg Infect Dis 2020; 26: 166–168. doi:10.3201/eid2601.190952
    1. Yang YM, Hsu CY, Lai CC, et al. . Impact of comorbidity on fatality rate of patients with Middle East Respiratory Syndrome. Sci Rep 2017; 7: 11307. doi:10.1038/s41598-017-10402-1
    1. Garbati MA, Fagbo SF, Fang VJ, et al. . A comparative study of clinical presentation and risk factors for adverse outcome in patients hospitalised with acute respiratory disease due to MERS coronavirus or other causes. PLoS One 2016; 11: e0165978. doi:10.1371/journal.pone.0165978
    1. Rivers CM, Majumder MS, Lofgren ET. Risks of death and severe disease in patients with Middle East Respiratory Syndrome coronavirus, 2012–2015. Am J Epidemiol 2016; 184: 460–464. doi:10.1093/aje/kww013
    1. Kulscar KA, Coleman CM, Beck S, et al. . Comorbid diabetes results in immune dysregulation and enhanced disease severity following MERS-CoV infection. JCI Insight 2019; 20: e131774.
    1. Matsuyama R, Nishiura H, Kutsuna S, et al. . Clinical determinants of the severity of Middle East respiratory syndrome (MERS): a systematic review and meta-analysis. BMC Public Health 2016; 16: 1203. doi:10.1186/s12889-016-3881-4
    1. World Health Organization. Clinical management of severe acute respiratory infection when COVID-19 is suspected Date last updated: 13 March 2020; date last accessed: 10 March 2020.
    1. Metlay JP, Waterer GW, Long AC, et al. . Diagnosis and treatment of adults with community-acquired pneumonia: an official clinical practice guideline of the American Thoracic Society and Infectious Disease Society of America. Am J Respir Crit Care Med 2019; 200: e45–e67. doi:10.1164/rccm.201908-1581ST
    1. Li HY, Guo Q, Song WD, et al. . Mortality among severe community-acquired pneumonia patients depends on combinations of 2007IDSA/ATS minor criteria. Int J Infect Dis 2015; 38: 141–145. doi:10.1016/j.ijid.2015.07.026
    1. Gearhart AM, Furmanek S, English C, et al. . Predicting the need for ICU admission in community-acquired pneumonia. Respir Med 2019; 155: 61–65. doi:10.1016/j.rmed.2019.07.007
    1. Fang L, Gao P, Bao H, et al. . Chronic obstructive pulmonary disease in China: a nationwide prevalence study. Lancet Respir Med 2018; 6: 421–430. doi:10.1016/S2213-2600(18)30103-6
    1. Moni MA, Lionel P. Network-based analysis of comorbidities risk during an infection: SARS and HIV case studies. BMC Bioinformatics 2014; 15: 333. doi:10.1186/1471-2105-15-333
    1. Naqvi AA, Shah A, Ahmad R, et al. . Developing an integrated treatment pathway for a post-coronary artery bypass grating (CABG) geriatric patient with comorbid hypertension and type 1 diabetes mellitus for treating acute hypoglycemia and electrolyte imbalance. J Pharm Bioallied Sci 2017; 9: 216–220. doi:10.4103/jpbs.JPBS_33_17
    1. Murphy TE, McAvay GJ, Allore HG, et al. . Contributions of COPD, asthma, and ten comorbid conditions to health care utilization and patient-centered outcomes among US adults with obstructive airway disease. Int J Chron Obstruct Pulmon Dis 2017; 12: 2515–2522. doi:10.2147/COPD.S139948

Source: PubMed

Спонсоры и соавторы

Заболевания

Медикаментозные вмешательства

Подписаться