The clinical characteristics and mortal causes analysis of COVID-19 death patients

  1. Ao-Xiang Guo
  2. Jia-Jia Cui
  3. Qian-Ying OuYang
  4. Li He
  5. Cheng-Xian Guo
  6. Ji-Ye Yin

Reviewed by

Read the full article

Listed in

Log in to save this article

Abstract

Purpose

Currently, COVID-19 is causing a large number of deaths globally. However, few researches focused on the clinical features of death patients. This study conducted a retrospective analysis of clinical characteristics and mortal causes in Chinese COVID-19 death patients.

Patients and methods

The clinical characteristics of death patients were collected from publicized by local health authorities in China. Expressions of virus targets in human organs were obtained from GTEx database.

Results

159 patients from 24 provinces in China were recruited in our study, including 26 young patients under 60 and 133 aged 60 or older. The median age was 71 years, which indicated that most death patients were elderly. More male patients died of COVID-19 than females (1.65 fold). Hypertension was the most common coexisting disorder and respiratory failure was the most common direct cause of death. Fever (71.19%) and cough (55.08%) were the predominant presenting symptoms. There was one asymptomatic patient. In addition, by comparing young and old patients, heart disease was identified as an important risk factor for death in the aged patients. ACE2 and TMPRSS2 were the targets of SARS-CoV-2, we analyzed their expression in different organs. TMPRSS2 and ACE2 had a high expression in the organs which had corresponding clinical features in death patients.

Conclusion

Male, age and heart disease were the main risk factors of death. Beside, asymptomatic patients with serious coexisting disorders may also die of SARS-CoV-2. Thus, more attention should be paid to the old patients with heart disease and asymptomatic patients in the treatment.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.04.12.20062380: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: The study protocol was approved by Institutional Review Board (AAHRPP-accredited) of the Third Xiangya Hospital of Central South University.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Statistical analyses in this study were performed with use of SPSS 17.0 software (IBM, NY, 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:
    There were some limitations in our study. The most important one is limited sample size, especially for the stratified analysis. There is a large gap between the sample size of young and old patients, which may cause some findings to be missed. In addition, we may ignore some important information because of the heterogeneity of anonymous COVID-19 data promulgated by local health authorities. We hope that these results can provide valuable information for effective cure and reducing death of COVID-19.

    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 10.1101/2020.04.12.20062380v1 on medRxiv