Comparison of the Clinical Implications among Two Different Nutritional Indices in Hospitalized Patients with COVID-19

  1. Xuebei Du
  2. Yuwei Liu
  3. Jing Chen
  4. Li Peng
  5. Yalei Jin
  6. Zhenshun Cheng
  7. Harry H.X. Wang
  8. Mingqi Luo
  9. Ling Chen
  10. Yan Zhao

Reviewed by

Read the full article

Discuss this preprint

Start a discussion What are Sciety discussions?

Listed in

Log in to save this article

Abstract

Background

Coronavirus disease 2019 (COVID-19) is an emerging infectious disease.It was first reported in Wuhan, China, and then broke out on a large scale around the world.This study aimed to assess the clinical significance of two different nutritional indices in 245 patients with COVID-19.

Methods

In this retrospective single-center study, we finally included 245 consecutive patients who confirmed COVID-19 in Wuhan University Zhongnan Hospital from January 1 to February 29. Cases were classified as either discharged or dead. Demographic, clinical and laboratory datas were registered, two different nutritional indices were calculated: (i)the Controlling nutritional status (CONUT) score; (ii) prognostic nutritional index (PNI). We used univariate and multivariate logistic regression analysis to explore the relationship between nutritional indices and hospital death.

Results

212 of them were discharged and 33 of them died. In-hospital mortality was signifcantly higher in the severe group of PNI than in the moderate and normal groups. It was also significantly worse in the severe-CONUT group than in the moderate-, mild-, and normal-CONUT groups. Multivariate logistic regression analysis showed the CONUT score (odds ratio3.371,95%CI (1.124–10.106), p = 0.030) and PNI(odds ratio 0.721,95% CI (0.581–0.896), P=0.003) were independent predictors of all-cause death at an early stage; Multivariate logistic regression analysis also showed that the severe group of PNI was the independent risk predictor of in-hospital death(odds ratio 24.225, 95% CI(2.147–273.327), p=0.010).The CONUT score cutoff value was 5.5 (56.00 and 80.81%; AUC 0.753; 95% CI (0.644–0.862); respectively). The PNI cutoff value was 40.58 (81.80 and 66.20%; AUC 0.778; 95% CI (0.686–0.809); respectively). We use PNI and the COUNT score to assess malnutrition, which can have a prognosis effect of COVID-19patients.

Conclusion

The CONUT score and PNI could be a reliable prognostic marker of all-cause deathin patients with COVID-19.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.04.28.20082644: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: This study which complied with the Declaration of Helsinki (Approval Number 2020054)was approved by the Medical Ethical Committee of Zhongnan Hospital of Wuhan University. 2.2 Clinical and Laboratory Data Collection: The demographic data collected age and gender.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    2.4 Statistical Analysis: Analysis was performed using SPSS (version 22.0).
    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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    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 10.1101/2020.04.28.20082644 on medRxiv