Outcome evaluation of COVID-19 infected patients by disease symptoms: a cross-sectional study in Ilam Province, Iran

  1. Jamil Sadeghifar
  2. Habib Jalilian
  3. Khalil Momeni
  4. Hamed Delam
  5. Tadesse Sheleme
  6. Ayoub Rashidi
  7. Fariba Hemmati
  8. Shahab Falahi
  9. Morteza Arab-Zozani

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Abstract

Background

Novel coronavirus disease-19 (COVID-19) was declared as a global pandemic in 2020. With the spread of the disease, a better understanding of patient outcomes associated with their symptoms in diverse geographic levels is vital. This study aimed to evaluate clinical outcomes of COVID-19 patients by disease symptoms in Ilam province, Iran.

Methods

This was a cross-sectional study. Data were collected from integrated health system records for all hospitals affiliated with the Ilam University of Medical Sciences between 26-Jan-2020 and 02-May-2020. All patients with a confirmed positive test were included in this study. Descriptive analyses, chi-square test, and binary logistic regression model were performed by using SPSS version 22.

Results

The mean age of participants was 46.47 ± 18.24 years. Of the 3608 patients, 3477 (96.1%) were discharged, and 129 (3.9%) died. 54.2% of the patients were male and were in the age group of 30–40 years. Cough, sore throat, shortness of breath or difficulty breathing, and fever or chills were the most common symptoms. Patients with symptoms of shortness of breath, abnormal radiographic findings of the chest, and chest pain and pressure were relatively more likely to die. According to binary logistic regression results, the probability of death in patients with shortness of breath, abnormal chest radiographic findings, and chest pain was 1.34, 1.24, and 1.32 times higher, respectively, than for those without.

Conclusion

Our study provides evidence that the presentation of some symptoms significantly impacts outcomes of patients infected with SARS-CoV-2. Early detection of symptoms and proper management of outcomes can reduce mortality in patients with COVID-19.

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  1. Version published to 10.1186/s12879-021-06613-7
  2. ScreenIT

    SciScore for 10.1101/2020.11.10.20228908: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: The proposal was approved by the ethics committee of Ilam University of Medical Sciences (No. IR.MEDILAM.REC.1399.043).
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Statistical analysis: Retrieved data were recorded into MicrosoftExcel (version 13) and analyzed.
    MicrosoftExcel
    suggested: None
    The SPSS version 22.0 (SPSS 22.0; SPSSInc, Chicago, IL, USA) were used.
    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.

  3. Version published to 10.1101/2020.11.10.20228908 on medRxiv