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

  1. Wei-jie Guan
  2. Wen-hua Liang
  3. Yi Zhao
  4. Heng-rui Liang
  5. Zi-sheng Chen
  6. Yi-min Li
  7. Xiao-qing Liu
  8. Ru-chong Chen
  9. Chun-li Tang
  10. Tao Wang
  11. Chun-quan Ou
  12. Li Li
  13. Ping-yan Chen
  14. Ling Sang
  15. Wei Wang
  16. Jian-fu Li
  17. Cai-chen Li
  18. Li-min Ou
  19. Bo Cheng
  20. Shan Xiong
  21. Zheng-yi Ni
  22. Jie Xiang
  23. Yu Hu
  24. Lei Liu
  25. Hong Shan
  26. Chun-liang Lei
  27. Yi-xiang Peng
  28. Li Wei
  29. Yong Liu
  30. Ya-hua Hu
  31. Peng Peng
  32. Jian-ming Wang
  33. Ji-yang Liu
  34. Zhong Chen
  35. Gang Li
  36. Zhi-jian Zheng
  37. Shao-qin Qiu
  38. Jie Luo
  39. Chang-jiang Ye
  40. Shao-yong Zhu
  41. Lin-ling Cheng
  42. Feng Ye
  43. Shi-yue Li
  44. Jin-ping Zheng
  45. Nuo-fu Zhang
  46. Nan-shan Zhong
  47. Jian-xing He

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Abstract

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.

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    SciScore for 10.1101/2020.02.25.20027664: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Statistical analysis: Statistical analyses were conducted with SPSS software version 23.0 (Chicago, IL, USA).
    SPSS
    suggested: (SPSS, RRID:SCR_002865)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    The main limitation of our study was the self-report of comorbidities on admission. Underreporting 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. However, significant underreporting 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. 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. As with other observational studies, our findings did not provide direct inference about the causation or reverse causation of comorbidities and the poor clinical outcomes.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1183/13993003.00547-2020
  3. Version published to 10.1101/2020.02.25.20027664v1 on medRxiv