Hospitalization time and outcome in patients with Coronavirus Disease 2019 (COVID-19): analysis data from China

  1. Songshan Chai
  2. Dongdong Xiao
  3. Qikai Cheng
  4. Shengzhi Huang
  5. Yihao Wang
  6. Jiajia Qian
  7. Jiajing Wang
  8. Junjun Li
  9. Peng Fu
  10. Qiangping Wang
  11. Jing Rao
  12. Hongyang Zhao
  13. Fangcheng Zhang
  14. Nanxiang Xiong

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Abstract

Objective

The mean hospitalization time and outcome among patients with coronavirus disease 2019 (COVID-19) was estimated with the purpose of providing evidence for decision-making in medical institutions and governments in epidemic areas.

Method

The data of COVID-19 patients in china were collected from the websites of provincial and municipal health commissions. The mean hospitalization time and mortality in the mild or severe patients and the mean time from severe to mild illness were calculated by Gaussian mixture modeling.

Results

The mean hospitalization time among mild patients in Hubei province, other areas except Hubei province, and the national areas was 20.71± 9.30, 16.86 ± 8.24, and 19.34 ± 9.29 days, respectively. The mean transition time from severe to mild group in the above three areas were 15.00, 17.00, and 14.99 days, respectively. The death rate of mild and severe patients in Hubei province and the national areas were 1.10% and 18.14%, and 1.10% and 17.70%, respectively. Among those patients who died of COVID-19, the mean time from severe transition to death in Hubei province and the national areas was 6.22 ± 5.12 and 6.35 ± 5.27 days, respectively.

Conclusion

There were regional differences in the average length of stay between Hubei province and other regions, which may be related to different medical configurations. For those severe patients who died of COVID-19, the average time from hospitalization to death was about one week, and proper and effective treatments in the first week were critical.

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    SciScore for 10.1101/2020.04.11.20061465: (What is this?)

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

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    In the numerical experiment part, we use the curve-fit function in the scipy.
    scipy
    suggested: (SciPy, RRID:SCR_008058)
    Optimize package in Python to help implement the Newton algorithm iterative process.
    Python
    suggested: (IPython, RRID:SCR_001658)

    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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    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.

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  2. Version published to 10.1101/2020.04.11.20061465v1 on medRxiv