Significance of neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio for predicting clinical outcomes in COVID-19

  1. Shaoping Huang
  2. Min Liu
  3. Xiaolu Li
  4. Zhiyin Shang
  5. Ting Zhang
  6. Hongzhou Lu

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Abstract

Background

The epidemic of 2019 novel coronavirus (COVID-19) struck China in late December, 2019, resulting in about 200000 deaths all over the world. Numerous observational studies have suggested that the neutrophil-to-lymphocyte ratio (NLR) and lymphocyte proportion and the platelet-to-lymphocyte ratio (PLR) are inflammatory markers. Our study aimed to detect the role of NLR, PLR in predicting the prognosis of COVID-19.

Methods

Four hundred and fifteen consecutive patients were enrolled in Shanghai Public Health Clinical Center affiliated to Fudan University, between 20 January and 11 April 2020 with confirmed COVID-19. Epidemiology, symptoms, signs, and laboratory examinations during the hospital stay were collected and compared between non-severe and severe patients. Statistical analysis was performed by SPSS 25.0 software.

Results

Four hundred and fifteen laboratory-confirmed COVID-19 patients were included in our study, among which 386 (93%) patients were not severe, and 27 (7%) were severe. The proportion of males in severe cases is higher than in non-severe cases (75.86% vs. 50.52%, P=0.008). The age between the two groups is different (p=0.022). Compared with non-severe patients, severe patients exhibited more comorbidities, including hypertension (48.28% vs. 19.43%, p<0.001), diabetes (20.69% vs. 6.99%, p=0.009), chronic obstructive pulmonary disease (51.72% vs. 6.22%, p<0.001), and fatty liver (37.93% vs. 15.8%, p=0.002), respectively. NLR and PLR showed significant difference (p<0.001). Diabetes (OR 0.28; 95% CI 15.824-187.186), fatty liver (OR 21.469; 95% CI 2.306-199.872), coronary heart disease (OR 18.157; 95% CI 2.085-158.083), NLR (OR 1.729; 95% CI 1.050-2.847) were significantly associated with severe cases with COVID-19. The NLR of patients in severe group had a 1.729-fold higher than that of no-severe group (OR 1.729; 95% CI 1.050-2.847, P=0.031).

Conclusions

NLR is an independent risk factor of severe COVID-19 patients. PLR, NLR were significantly different between severe and non-severe patients, so assessment of NLR, PLR may help identify high risk cases with COVID-19.

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  1. ScreenIT

    SciScore for 10.1101/2020.05.04.20090431: (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 Medical Ethics Committee of Shanghai Public Health Clinical Center affiliated to Fudan University. 2. Statistical Analysis: Statistical analysis was performed by SPSS 25.0 software.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    This study was approved by the Medical Ethics Committee of Shanghai Public Health Clinical Center affiliated to Fudan University. 2. Statistical Analysis: Statistical analysis was performed by SPSS 25.0 software.
    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 are some limitations in our study. First, the number of observed events is to some extent small which may limit the statistical power of this research. However, the sample size is sufficient to draw a conclusion. Second, in our group, there were fewer severe patients which may not balance for analysis. Third, the causal relationship between abnormal laboratory findings and severity could not be estimated since laboratory findings were measured on admission and may not indicate the severity of COVID-19.

    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 10.1101/2020.05.04.20090431v1 on medRxiv