A New Predictor of Disease Severity in Patients with COVID-19 in Wuhan, China

  1. Ying Zhou
  2. Zhen Yang
  3. Yanan Guo
  4. Shuang Geng
  5. Shan Gao
  6. Shenglan Ye
  7. Yi Hu
  8. Yafei Wang

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Abstract

Background

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) broke out in Wuhan, Hubei, China. This study sought to elucidate a novel predictor of disease severity in patients with coronavirus disease-19 (COVID-19) cased by SARS-CoV-2.

Methods

Patients enrolled in this study were all hospitalized with COVID-19 in the Central Hospital of Wuhan, China. Clinical features, chronic comorbidities, demographic data, and laboratory and radiological data were reviewed. The outcomes of patients with severe pneumonia and those with non-severe pneumonia were compared using the Statistical Package for the Social Sciences (IBM Corp., Armonk, NY, USA) to explore clinical characteristics and risk factors. The receiver operating characteristic curve was used to screen optimal predictors from the risk factors and the predictive power was verified by internal validation.

Results

A total of 377 patients diagnosed with COVID-19 were enrolled in this study, including 117 with severe pneumonia and 260 with non-severe pneumonia. The independent risk factors for severe pneumonia were age [odds ratio (OR): 1.059, 95% confidence interval (CI): 1.036–1.082; p < 0.001], N/L (OR: 1.322, 95% CI: 1.180–1.481; p < 0.001), CRP (OR: 1.231, 95% CI: 1.129–1.341; p = 0.002), and D-dimer (OR: 1.059, 95% CI: 1.013–1.107; p = 0.011). We identified a product of N/L*CRP*D-dimer as having an important predictive value for the severity of COVID-19. The cutoff value was 5.32. The negative predictive value of less than 5.32 for the N/L*CRP*D-dimer was 93.75%, while the positive predictive value was 46.03% in the test sets. The sensitivity and specificity were 89.47% and 67.42%. In the training sets, the negative and positive predictive values were 93.80% and 41.32%, respectively, with a specificity of 70.76% and a sensitivity of 89.87%.

Conclusions

A product of N/L*CRP*D-dimer may be an important predictor of disease severity in patients with COVID-19.

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  1. ScreenIT

    SciScore for 10.1101/2020.03.24.20042119: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: This study was approved by the ethics committee of Wuhan Central Hospital.
    Consent: As the study is a retrospective study and does not involve patients’ privacy, the informed consent can be exempted Data collection: Chronic comorbidities, demographic data, laboratory examinations, and chest CT scans were reviewed using electronic medical records.
    RandomizationInternal validation: Patients were randomly assigned in a 2:1 fashion to undergo training and hold-out test sets.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    We used the Statistical Package for the Social Sciences, version 22.0 (IBM Corp., Armonk, NY, USA) for statistical analysis.
    Statistical Package for the Social Sciences
    suggested: (SPSS, RRID:SCR_002865)

    Results from OddPub: Thank you for sharing your data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Importantly, our study has limitations. First, laboratory testing methods may not be the same in every hospital and the optimal cutoff point for the product value may vary. Further, only 377 patients were included in this study, where the product of N/L*CRP*D-dimer was found for the first time, so larger-scale data from randomized trials are needed to estimate whether to use this predictor in clinical practice.

    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 10.1101/2020.03.24.20042119v1 on medRxiv