COVID-19 outcomes associated with clinical and demographic characteristics in patients hospitalized with severe and critical disease in Peshawar

  1. Muhammad Imran
  2. Azharuddin
  3. Muhammad Yousaf
  4. Sajjad Khan
  5. Abdul Jalil Khan
  6. Zafar Iqbal
  7. RahmanUllah Jan
  8. Shahid Khan
  9. Muhammad Salman Khan

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Abstract

Background

As a novel disease, understanding the relationship between the clinical and demographic characteristics of coronavirus disease 2019 (COVID-19) patients and their outcome is critical. We investigated this relationship in hospitalized patients in a tertiary healthcare setting.

Aims/objectives

To study COVID-19 severity and outcomes in relation to clinical and demographic characteristics of in admitted patients

Methodology

In this cross-sectional study, medical records for 1087 COVID-19 patients were reviewed to extract symptoms, comorbidities, demographic characteristics, and outcomes data. Statistical analyses included the post-stratification chi-square test, independent sample t-test, multivariate logistic regression, and time-to-event analysis.

Results

The majority of the study participants were >50 years old (67%) and male (59%) and had the following symptoms: fever (96%), cough (95%), shortness of breath (73%), loss of taste (77%), and loss of smell (77%). Regarding worst outcome, multivariate regression analysis showed that these characteristics were statistically significant: shortness of breath (adjusted odds ratio [aOR] 31.3; 95% CI, 11.87–82.53; p < 0.001), intensive care unit (ICU) admission (aOR 28.3; 95% CI,9.0–89.6; p < 0.001), diabetes mellitus (aOR 5.1; 95% CI;3.2–8.2; p < 0.001), ischemic heart disease (aOR 3.4; 95% CI,1.6–7; p = 0.001), nausea and vomiting (aOR 3.3; 95% CI, 1.7–6.6; p = 0.001), and prolonged hospital stay (aOR 1.04; 95% CI, 1.02–1.08; p = 0.001), while patients with rhinorrhea were significantly protected (aOR 0.3; 95% CI, 0.2–0.5; p < 0.001). A Kaplan–Meier curve showed that the symptoms of shortness of breath, ICU admission, fever, nausea and vomiting, and diarrhea increased the risk of mortality.

Conclusion

Increasing age, certain comorbidities and symptoms, and direct admission to the ICU increased the risk of worse outcomes. Further research is needed to determine risk factors that may increase disease severity and devise a proper risk-scoring system to initiate timely management.

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  1. ScreenIT

    SciScore for 10.1101/2022.03.24.22272884: (What is this?)

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

    Table 1: Rigor

    EthicsConsent: Informed written consent was obtained from all patients at admission to the hospital.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The analysis was performed using SPSS, version 25 (IBM, Armonk, NY, USA) [16] The association of various characteristics with mortality was analyzed.
    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:
    The limitations of this study are that we did not include patients from other hospitals for comparative analysis. Furthermore, we did not include the response to various treatments, and lastly, the patient’s social characteristics, such as local residence, occupation, and income, were not included in this analysis. Further research is needed to develop a comprehensive scoring system for COVID-19. The system for scoring should be practical and straightforward to predict the disease severity and the management steps for COVID-19. Both prospective and retrospective analyses from the global north and the global south are needed to identify the critical factors in managing this disease.

    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.

    Results from scite Reference Check: We found no unreliable references.

    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 10.1101/2022.03.24.22272884v1 on medRxiv