Development and Validation of a Diagnostic Nomogram to Predict COVID-19 Pneumonia

  1. Zhiyi Wang
  2. Jie Weng
  3. Zhongwang Li
  4. Ruonan Hou
  5. Lebin Zhou
  6. Hua Ye
  7. Ying Chen
  8. Ting Yang
  9. Daqing Chen
  10. Liang Wang
  11. Xiaodong Liu
  12. Xian Shen
  13. Shengwei Jin

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Abstract

The COVID-19 virus is an emerging virus rapidly spread worldwide This study aimed to establish an effective diagnostic nomogram for suspected COVID-19 pneumonia patients.

METHODS

We used the LASSO aggression and multivariable logistic regression methods to explore the predictive factors associated with COVID-19 pneumonia, and established the diagnostic nomogram for COVID-19 pneumonia using multivariable regression. This diagnostic nomogram was assessed by the internal and external validation data set. Further, we plotted decision curves and clinical impact curve to evaluate the clinical usefulness of this diagnostic nomogram.

RESULTS

The predictive factors including the epidemiological history, wedge- shaped or fan-shaped lesion parallel to or near the pleura, bilateral lower lobes, ground glass opacities, crazy paving pattern and white blood cell (WBC) count were contained in the nomogram. In the primary cohort, the C-statistic for predicting the probability of the COVID-19 pneumonia was 0.967, even higher than the C-statistic (0.961) in initial viral nucleic acid nomogram which was established using the univariable regression. The C-statistic was 0.848 in external validation cohort. Good calibration curves were observed for the prediction probability in the internal validation and external validation cohort. The nomogram both performed well in terms of discrimination and calibration. Moreover, decision curve and clinical impact curve were also beneficial for COVID- 19 pneumonia patients.

CONCLUSION

Our nomogram can be used to predict COVID-19 pneumonia accurately and favourably.

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  1. ScreenIT

    SciScore for 10.1101/2020.04.03.20052068: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: The study was approved by the Ethics Committee of the Second Affiliated Hospital and Yuying Children’s Hospital of Wenzhou Medical University.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.

    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:
    Limitations of this study: Our study has several limitations. Firstly, only 178 patients were included in primary cohort and another hospital was selected for external validation (116 patients). Whether this nomogram is applicable to patients with other areas background is still unclear. A large number of patients as data need to be collected to verify its clinical application. Secondly, this nomogram is mainly used to identify COVID-19 pneumonia in the patients with suspected viral pneumonia, not all types of pneumonia. Although the decrease of lymphocyte count is more common among COVID-19 pneumonia, not observed in our study. It may be related to our inclusion criteria.

    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 10.1101/2020.04.03.20052068 on medRxiv