Clinical characteristics of COVID-19 and the model for predicting the occurrence of critically ill patients: a retrospective cohort study

  1. Jing Ouyang
  2. Xuefeng Shan
  3. Xin Wang
  4. Xue Zhang
  5. Yaling Chen
  6. Miaomiao Qi
  7. Chao Xia
  8. Dongqing Gu
  9. Yaokai Chen
  10. Ben Zhang

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Abstract

Background

The present study aim to comprehensively report the epidemiological and clinical characteristics of the COVID-19 patients and to develop a multi-feature fusion model for predicting the critical ill probability.

Methods

It was a retrospective cohort study that incorporating the laboratory-confirmed COVID-19 patients in the Chongqing Public Health Medical Center. The prediction model was constructed with least absolute shrinkage and selection operator (LASSO) logistic regression method and the model was further tested in the validation cohort. The performance was evaluated by the receiver operating curve (ROC), calibration curve and decision curve analysis (DCA).

Results

A total of 217 patients were included in the study. During the treatment, 34 patients were admitted to intensive care unit (ICU) and no developed death. A model incorporating the demographic and clinical characteristics, imaging features and laboratory findings were constructed to predict the critical ill probability and it was proved to have good calibration, discrimination ability and clinic use.

Conclusions

The prevalence of critical ill was relatively high and the model may help the clinicians to identify the patients with high risk for developing the critical ill, thus to conduct timely and targeted treatment to reduce the mortality rate.

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  1. ScreenIT

    SciScore for 10.1101/2020.08.13.20173799: (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 institutional review board of the Chongqing Public Health Medical Center
    RandomizationFor the development of the model for predicting the critical ill patients, all of the included patients were randomly splitting into two cohorts, namely the construction cohort (70%) and validation cohort (30%).
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Statistical analyses were conducted using Statistical Package for the Social Sciences (SPSS) version 23.0 software package for Windows (SPSS Inc), and R version 3.4.1 (R Foundation for Statistical Computing, Vienna, Austria; www.r-project.org).
    Statistical Package for the Social Sciences
    suggested: (SPSS, RRID:SCR_002865)
    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:
    Our study has some limitations. Firstly, the sample size incorporated into the research was relatively small, which may partly affect the statistic power of the results. Secondly, not all of the laboratory tests were done in all of the included patients such as D-dimer and proinflammatory cytokines, which were proved to play important roles in the critical ill occurrence [9]. But with all of the included features, the prediction model was proved to be have good performance and clinical use, thus they may not significantly affect the results. To sum up, this study comprehensively describes the clinical characteristics of the COVID-19 patients and then to construct and validate a model for predicting the occurrence of critical ill probability. The results may help the clinicians to have an unrivalled understanding of the characteristics of COVID-19 patients and then to stratified the patients into different risk subgroups for developing critical ill and tailor targeted treatment regimens for the high risk patients and reduce the mortality rate.

    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 10.1101/2020.08.13.20173799 on medRxiv