COVID-19 in rheumatic diseases: A random cross-sectional telephonic survey

  1. Rudra Prosad Goswami
  2. Uma Kumar
  3. Maumita Kanjilal
  4. Debaditya Ray
  5. Pallavi Vij
  6. Dheeraj K Mittal
  7. Laxman Meena
  8. Sandeep Nagar
  9. Danveer Bhadu

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Abstract

Objective

To describe the incidence, clinical course, and predictive factors of coronavirus 2019 (COVID-19) infection in a cohort of rheumatological patients residing in New Delhi (National Capital Region), India.

Methods

We performed a cross-sectional, random telephonic survey from 20 th April to 20 th July 2020 on patients with rheumatic diseases. Patients were interviewed with a predesigned questionnaire. The incidence of COVID-19 in the general population was obtained from open access government data repository. Report of reverse transcriptase polymerase chain reaction report was taken as confirmatory of COVID-19 infection.

Results

Among the 900 contacted patients 840 responded (713 with rheumatoid arthritis (RA), 100 with systemic lupus erythematosus (SLE), 20 with spondylarthritis (SpA) and 7 with others; mean age 45 ±13 years, mean duration 11.3 ± 6.3 years; 86% female). Among them 29 reported flu-like symptoms and four RA patients had confirmed COVID-19 infection. All of them were hospitalized with uneventful recovery. Rheumatological drugs were discontinued during the infectious episode. Disease modifying agents and biologics were equally received by those with or without COVID-19. The incidence of COVID-19 was similar to general Delhi population (0.476% vs 0.519% respectively, p=0.86). Two patients had relapse of rheumatic disease after recovery. After recovery from COVID-19 or Flu-like illness, eight patients (27.6%, 95% confidence interval 14.7-45.7) reported disease flare.

Conclusion

Patients with rheumatic diseases in India have similar incidence of COVID-19 infection compared to the community. Relapse of underlying rheumatic disease after recovery is not uncommon and continuation of glucocorticoid through the infection should be considered.

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  1. ScreenIT

    SciScore for 10.1101/2020.08.09.20170985: (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

    No key resources detected.

    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 a few limitations: single-centre cross-sectional survey, less than a 100% response to the telephonic survey and all suspected patients were not confirmed with rt-PCR. However, long-term follow up and detailed diagnostic and therapeutic information and details of flu-like symptoms and COVID-19 natural history over repeated and multiple telephonic conversations with the patients are the strengths of our study. To conclude, we observed that the incidence of COVID-19 infection among patients with rheumatic diseases was similar to the community. The incidences of Flu-like symptoms were similar across different diagnostic classes. DMARDs neither increased risk of the infection nor provided any protection to the same. Since, relapse of disease, especially after stoppage of disease modifying agents, is a concern after this infection, continuation of glucocorticoid through the infection might be a prudent strategy.

    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.
    • Thank you for including a protocol registration statement.

    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 10.1101/2020.08.09.20170985v1 on medRxiv