A Pattern Categorization of CT Findings to Predict Outcome of COVID-19 Pneumonia

  1. Chao Jin
  2. Cong Tian
  3. Yan Wang
  4. Carol C. Wu
  5. Huifang Zhao
  6. Ting Liang
  7. Zhe Liu
  8. Zhijie Jian
  9. Runqing Li
  10. Zekun Wang
  11. Fen Li
  12. Jie Zhou
  13. Shubo Cai
  14. Yang Liu
  15. Hao Li
  16. Zhongyi Li
  17. Yukun Liang
  18. Heping Zhou
  19. Xibin Wang
  20. Zhuanqin Ren
  21. Jian Yang

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Abstract

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  1. Version published to 10.3389/fpubh.2020.567672
  2. ScreenIT

    SciScore for 10.1101/2020.05.19.20107409: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: Participants: The internal review board approved this retrospective study.
    Consent: Written informed consent was waived with approval.
    RandomizationDuring the session, 209 CT images from 56 cases randomly selected from this study cohort were individually evaluated and then differences were discussed with a final consensus.
    BlindingAll CT images and pattern categorization were independently evaluated by two experienced radiologists respectively with 4 and 10 years of pulmonary imaging experience, who were blinded to the clinical and laboratory data of patients.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    All statistical analyses were performed using SPSS 17.0 (SPSS; Chicago, IL, USA) and Medcalc 19.1.7 (MedCals Software Ltd; Ostend, Belgium).
    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:
    This study had some limitations. The first was the small sample, especially for those with adverse outcomes. A larger sample is required to further verify the findings regarding the risk factors affecting the adverse outcome. Second, because discharged patients remained during the recovery and pulmonary CT residuals were unknown at the time of our analysis, a long-term follow-up is required to further trace the outcome of lesion absorption, as well as changes in lung functions. Last, multicenter data collection may lead to selective bias of patients with various CT patterns. Although no significance in univariate analysis (see more in Supplement), potential impacts from varying hospital, epicenter vs. non-epicenter should be considered in further studies. In conclusion, CT pattern categorization of COVID-19 pneumonia based on chest CT within 2 weeks after symptom onset has prognostic significance. CT pattern 4 cases present high risk of admission to ICU, need for mechanical ventilation or death, while Pattern 3 and 4 signal likelihood of pulmonary residuals on CT. These findings would help early prognostic stratification of COVID-19 and facilitate the decision making for treatment strategy and optimal use of healthcare resources.

    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.

  3. Version published to 10.1101/2020.05.19.20107409 on medRxiv