Outcome of hospitalisation for COVID-19 in patients with Interstitial Lung Disease: An international multicentre study.

  1. Thomas M Drake
  2. Annemarie B Docherty
  3. Ewen M Harrison
  4. Jennifer K Quint
  5. Huzaifa Adamali
  6. Sarah Agnew
  7. Suresh Babu
  8. Christopher M Barber
  9. Shaney Barratt
  10. Elisabeth Bendstrup
  11. Stephen Bianchi
  12. Diego Castillo Villegas
  13. Nazia Chaudhuri
  14. Felix Chua
  15. Robina Coker
  16. William Chang
  17. Anjali Crawshaw
  18. Louise E. Crowley
  19. Davinder Dosanjh
  20. Christine A Fiddler
  21. Ian A. Forrest
  22. Peter George
  23. Michael A Gibbons
  24. Katherine Groom
  25. Sarah Haney
  26. Simon P Hart
  27. Emily Heiden
  28. Michael Henry
  29. Ling-Pei Ho
  30. Rachel K Hoyles
  31. John Hutchinson
  32. Killian Hurley
  33. Mark Jones
  34. Steve Jones
  35. Maria Kokosi
  36. Michael Kreuter
  37. Laura MacKay
  38. Siva Mahendran
  39. George Margaritopoulos
  40. Maria Molina-Molina
  41. Philip L Molyneaux
  42. Aiden O’Brien
  43. Katherine O’Reilly
  44. Alice Packham
  45. Helen Parfrey
  46. Venerino Poletti
  47. Joanna Porter
  48. Elisabetta Renzoni
  49. Pilar Rivera-Ortega
  50. Anne-Marie Russell
  51. Gauri Saini
  52. Lisa G Spencer
  53. Giulia M. Stella
  54. Helen Stone
  55. Sharon Sturney
  56. David Thickett
  57. Muhunthan Thillai
  58. Tim Wallis
  59. Katie Ward
  60. Athol U Wells
  61. Alex West
  62. Melissa Wickremasinghe
  63. Felix Woodhead
  64. Glenn Hearson
  65. Lucy Howard
  66. J Kenneth Baillie
  67. Peter J.M. Openshaw
  68. Malcolm G Semple
  69. Iain Stewart
  70. ISARIC4C Investigators
  71. R Gisli Jenkins

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Abstract

Rationale

The impact of COVID-19 on patients with Interstitial Lung Disease (ILD) has not been established.

Objectives

To assess outcomes following COVID-19 in patients with ILD versus those without in a contemporaneous age, sex and comorbidity matched population.

Methods

An international multicentre audit of patients with a prior diagnosis of ILD admitted to hospital with COVID-19 between 1 March and 1 May 2020 was undertaken and compared with patients, without ILD obtained from the ISARIC 4C cohort, admitted with COVID-19 over the same period. The primary outcome was survival. Secondary analysis distinguished IPF from non-IPF ILD and used lung function to determine the greatest risks of death.

Measurements and Main Results

Data from 349 patients with ILD across Europe were included, of whom 161 were admitted to hospital with laboratory or clinical evidence of COVID-19 and eligible for propensity-score matching. Overall mortality was 49% (79/161) in patients with ILD with COVID-19. After matching ILD patients with COVID-19 had higher mortality (HR 1.60, Confidence Intervals 1.17-2.18 p=0.003) compared with age, sex and comorbidity matched controls without ILD. Patients with a Forced Vital Capacity (FVC) of <80% had an increased risk of death versus patients with FVC ≥80% (HR 1.72, 1.05-2.83). Furthermore, obese patients with ILD had an elevated risk of death (HR 1.98, 1.13−3.46).

Conclusions

Patients with ILD are at increased risk of death from COVID-19, particularly those with poor lung function and obesity. Stringent precautions should be taken to avoid COVID-19 in patients with ILD.

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  1. ScreenIT

    SciScore for 10.1101/2020.07.15.20152967: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: ISARIC4C CCP-UK received ethical approval from the South Central – Oxford C Research Ethics Committee in England (Ref: 13/SC/0149), and by the Scotland A Research Ethics Committee (Ref: 20/SS/0028).
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: Thank you for sharing your data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    This study also has some potential weaknesses. Due to the retrospective nature of the data collection recall bias might have led to over-selection of severe cases of COVID-19 in patients with ILD, however, this is mitigated by the large number of centres participating in the audit. Similarly, because only hospitalised patients could be included, it is possible that a large number of patients with ILD and COVID-19 were omitted and, therefore, the risk of COVID-19 could be over-stated. However, given the demographic associated with ILD we think this is unlikely. Also, the propensity-score matching included some younger patients who would be expected to have less severe disease, although there was insufficient detail in the audit data collection to match for COVID-19 severity based on admission severity scores. The effect of obesity was assessed in the matched population suggesting an increased risk in patients without ILD, however in contrast with prior reports (9,24) no effect of obesity was observed in the control population. This probably reflects the relatively small numbers of patients used in the matching and the large amount of missing data relating to weight. Therefore, the data relating to obesity must be interpreted accordingly, but we believe as a modifiable risk factor it is important to highlight the risk of obesity in patients with ILD who might develop COVID-19. Finally, it was not possible to evaluate specific treatment effects, such as the use of anti-fibrotics...

    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. ScreenIT

    SciScore for 10.1101/2020.07.15.20152967: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementISARIC4C CCP-UK received ethical approval from the South Central - Oxford C Research Ethics Committee in England (Ref: 13/SC/0149), and by the Scotland A Research Ethics Committee (Ref: 20/SS/0028).Randomizationnot detected.Blindingnot detected.Power Analysisnot detected.Sex as a biological variableOf these, 110 (68%) were male and the mean age was 73.2 (±11.5) years.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    , Northumbria Way, Northumbria Healthcare NHS Foundation Trust, Cramlington, NE23 6NZ 21 Respiratory Research Group, Hull York Medical School, Castle Hill Hospital, Cottingham, UK, HU16 5JQ 22 University Hospitals Southampton NHS Foundation Trust, Southampton, UK 23 NIHR Southampton Biomedical Research Centre & Clinical and Experimental Sciences, University of Southampton, Southampton, UK 24 Cork University Hospital, Cork, Ireland 25 Oxford Interstitial Lung Disease Service, Oxford University Hospitals NHS Foundation Trust, Oxford, OX3 7LE, UK 26 King’s Mill Hospital, Sutton-in-Ashfield
    Northumbria Healthcare
    suggested: None

    Data from additional tools added to each annotation on a weekly basis.

    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 is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.

  3. Version published to 10.1101/2020.07.15.20152967v1 on medRxiv