A Tool for Early Prediction of Severe Coronavirus Disease 2019 (COVID-19): A Multicenter Study Using the Risk Nomogram in Wuhan and Guangdong, China

  1. Jiao Gong
  2. Jingyi Ou
  3. Xueping Qiu
  4. Yusheng Jie
  5. Yaqiong Chen
  6. Lianxiong Yuan
  7. Jing Cao
  8. Mingkai Tan
  9. Wenxiong Xu
  10. Fang Zheng
  11. Yaling Shi
  12. Bo Hu

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Abstract

Background

Because there is no reliable risk stratification tool for severe coronavirus disease 2019 (COVID-19) patients at admission, we aimed to construct an effective model for early identification of cases at high risk of progression to severe COVID-19.

Methods

In this retrospective multicenter study, 372 hospitalized patients with nonsevere COVID-19 were followed for > 15 days after admission. Patients who deteriorated to severe or critical COVID-19 and those who maintained a nonsevere state were assigned to the severe and nonsevere groups, respectively. Based on baseline data of the 2 groups, we constructed a risk prediction nomogram for severe COVID-19 and evaluated its performance.

Results

The training cohort consisted of 189 patients, and the 2 independent validation cohorts consisted of 165 and 18 patients. Among all cases, 72 (19.4%) patients developed severe COVID-19. Older age; higher serum lactate dehydrogenase, C-reactive protein, coefficient of variation of red blood cell distribution width, blood urea nitrogen, and direct bilirubin; and lower albumin were associated with severe COVID-19. We generated the nomogram for early identifying severe COVID-19 in the training cohort (area under the curve [AUC], 0.912 [95% confidence interval {CI}, .846–.978]; sensitivity 85.7%, specificity 87.6%) and the validation cohort (AUC, 0.853 [95% CI, .790–.916]; sensitivity 77.5%, specificity 78.4%). The calibration curve for probability of severe COVID-19 showed optimal agreement between prediction by nomogram and actual observation. Decision curve and clinical impact curve analyses indicated that nomogram conferred high clinical net benefit.

Conclusions

Our nomogram could help clinicians with early identification of patients who will progress to severe COVID-19, which will enable better centralized management and early treatment of severe disease.

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  1. ScreenIT

    SciScore for 10.1101/2020.03.17.20037515: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementConsent: Written informed consent was waived by the Ethics Commission of each hospital for emerging infectious diseases.
    IRB: The study was approved by the Ethics Committee of the Eighth People’s Hospital of Guangzhou, the Ethics Commission of the Third Affiliated Hospital of Sun Yat-sen University and the Ethics Commission of Zhongnan Hospital.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The missing values were imputed by expectation-maximization (EM) method using SPSS statistical software, version 25 (SPSS, Inc., Chicago, IL, 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 the study. First, this is a retrospective study, including 372 patients with non-severe COVID-19 on admission. ACE2, the receptor for SARS-COV-2, has been reported to be differentially expressed in different populations[21]. The differences in patient profiles and healthcare might have effect on the performance of nomogram in other populations outside of China. Further studies on different populations with larger patient cohorts are required to verify our findings. Second, some patients are still in hospital and their condition may change with follow-up. The final survival outcome is lacking. Third, the study has not included IgM and IgG antibodies detection. More comprehensive investigations need to be conducted to explain the characteric of the 7 features. In summary, our data suggest that our nomogram could early identify the severe COVID-19 patients, and RDW was vaulable for prediction of severe diseases. Our nomogram is especially valuable for risk stratification management, which will be helpful for alleviating insufficient medical resources and reducing mortality.

    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.
    • Thank you for including a protocol registration statement.

    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 10.1093/cid/ciaa443
  3. Version published to 10.1101/2020.03.17.20037515 on medRxiv