Chest CT Scan of Hospitalized Patients with COVID-19: A Case-Control Study

  1. Lianpin Wu
  2. Qike Jin
  3. Jie Chen
  4. Jiawei He
  5. Jianghu (James) Dong

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Abstract

Introduction

This paper sought to investigate the clinical characteristic differences between suspected and confirmed patients with COVID-19 from CT scan to prevent and treat this infectious disease, since the coronavirus outbreak in the world has seriously affected the quality of life.

Methods

We proposed to use a retrospective case-control study to give a comparison between suspected patients and confirmed patients in the clinical characteristics.

Results

(56%) patients were confirmed for COVID-19 from suspected 167 patients. We find that elder people were more likely to be infected by COVID-19. Among the confirmed 94 patients, 2 (2%) patients were admitted to an intensive care unit, and 0 (0%) patients died during the study period. We find that images of CT scan of patients with a COVID-19 are significantly different from patients without a COVID-19.

Conclusions

To our best knowledge, it is the first time to use the case-control design to study the coronavirus disease, since it is particularly appropriate for investigating infectious disease outbreaks. The clinical treatment experience in this study can supply a guideline for treating COVID-19 as the number of the infected patients is increasing in the world. Compared with other studies, we find that the mortality rate and the intensive care unit rate can be reduced if patients can be treated timely in the right identification and detection with nucleic acid testing and chest CT scan. Therefore, we recommend nucleic acid testing and chest CT scan for the clinical treatment practice from this successful clinical treatment study.

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  1. ScreenIT

    SciScore for 10.1101/2020.04.07.20056762: (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
    All analyses were done with SAS software, version 9.4.
    SAS
    suggested: (SASqPCR, RRID:SCR_003056)

    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.

    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 10.1101/2020.04.07.20056762v1 on medRxiv