Clinical Profile of First 1000 COVID-19 Cases Admitted at Tertiary Care Hospitals and the Correlates of their Mortality: An Indian Experience

  1. Sandeep Budhiraja
  2. Aakriti Soni
  3. Vinitaa Jha
  4. Abhaya Indrayan
  5. Arun Dewan
  6. Omender Singh
  7. Yogendra Pal Singh
  8. Indermohan Chugh
  9. Vijay Arora
  10. Rajesh Pande
  11. Abdul Ansari
  12. Sujeet Jha

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Abstract

Objective

To describe the clinical profile and factors leading to increased mortality in coronavirus disease (COVID-19) patients admitted to a group of hospitals in India.

Design

A records-based study of the first 1000 patients with a positive result on real-time reverse transcriptase-polymerase-chain-reaction assay for SARS-CoV-2 admitted to our facilities. Various factors such as demographics, presenting symptoms, co-morbidities, ICU admission, oxygen requirement and ventilator therapy were studied.

Results

Of the 1000 patients, 24 patients were excluded due to lack of sufficient data. Of the remaining 976 in the early phase of the epidemic, males were admitted twice as much as females (67.1% and 32.9%, respectively). Mortality in this initial phase was 10.6% and slightly higher for males and steeply higher for older patients. More than 8% reported no symptoms and the most common presenting symptoms were fever (78.3%), productive cough (37.2%), and dyspnea (30.64%). More than one-half (53.6%) had no co-morbidity. The major co-morbidities were hypertension (23.7%), diabetes without (15.4%), and with complications (9.6%). The co-morbidities were associated with higher ICU admissions, greater use of ventilators as well as higher mortality. A total of 29.9% were admitted to the ICU, with a mortality rate of 32.2%. Mortality was steeply higher in those requiring ventilator support (55.4%) versus those who never required ventilation (1.4%). The total duration of hospital stay was just a day longer in patients admitted to the ICU than those who remained in wards.

Conclusion

Male patients above the age of 60 and with co-morbidities faced the highest rates of mortality. They should be admitted to the hospital in early stage of the disease and given aggressive treatment to help reduce the morbidity and mortality associated with COVID-19.

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  1. ScreenIT

    SciScore for 10.1101/2020.11.16.20232223: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    SPSS 20.0 (IBM Corp., Armonk, NY, US) was used for all the calculations.
    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 10.1101/2020.11.16.20232223v1 on medRxiv