Hospitalization time and outcome in patients with Coronavirus Disease 2019 (COVID-19): analysis data from China
This article has been Reviewed by the following groups
Listed in
- Evaluated articles (ScreenIT)
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.
Article activity feed
-
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 Sentences Resources In the numerical experiment part, we use the curve-fit function in the scipy. scipysuggested: (SciPy, RRID:SCR_008058)Optimize package in Python to help implement the Newton algorithm iterative process. Pythonsuggested: (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.Resul…
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 Sentences Resources In the numerical experiment part, we use the curve-fit function in the scipy. scipysuggested: (SciPy, RRID:SCR_008058)Optimize package in Python to help implement the Newton algorithm iterative process. Pythonsuggested: (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.
-