Rates of serious clinical outcomes in survivors of hospitalisation with COVID-19 in England: a descriptive cohort study within the OpenSAFELY platform

  1. The OpenSAFELY Collaborative
  2. John Tazare
  3. Alex J. Walker
  4. Laurie A. Tomlinson
  5. George Hickman
  6. Christopher T. Rentsch
  7. Elizabeth J. Williamson
  8. Krishnan Bhaskaran
  9. David Evans
  10. Kevin Wing
  11. Rohini Mathur
  12. Angel YS. Wong
  13. Anna Schultze
  14. Seb Bacon
  15. Chris Bates
  16. Caroline E. Morton
  17. Helen J. Curtis
  18. Emily Nightingale
  19. Helen I. McDonald
  20. Amir Mehrkar
  21. Peter Inglesby
  22. Simon Davy
  23. Brian MacKenna
  24. Jonathan Cockburn
  25. William J. Hulme
  26. Charlotte Warren-Gash
  27. Ketaki Bhate
  28. Dorothea Nitsch
  29. Emma Powell
  30. Amy Mulick
  31. Harriet Forbes
  32. Caroline Minassian
  33. Richard Croker
  34. John Parry
  35. Frank Hester
  36. Sam Harper
  37. Rosalind M. Eggo
  38. Stephen JW. Evans
  39. Liam Smeeth
  40. Ian J Douglas
  41. Ben Goldacre

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Abstract

Background: Patients surviving hospitalisation for COVID-19 are thought to be at high risk of cardiometabolic and pulmonary complications, but quantification of that risk is limited. We aimed to describe the overall burden of these complications in people after discharge from hospital with COVID-19.  

Methods:  Working on behalf of NHS England, we used linked primary care records, death certificate and hospital data from the OpenSAFELY platform. We constructed three cohorts: patients discharged following hospitalisation with COVID-19, patients discharged following pre-pandemic hospitalisation with pneumonia, and a frequency-matched cohort from the general population in 2019. We studied seven outcomes: deep vein thrombosis (DVT), pulmonary embolism (PE), ischaemic stroke, myocardial infarction (MI), heart failure, AKI and new type 2 diabetes mellitus (T2DM) diagnosis. Absolute rates were measured in each cohort and Fine and Gray models were used to estimate age/sex adjusted subdistribution hazard ratios comparing outcome risk between discharged COVID-19 patients and the two comparator cohorts.

Results:  Amongst the population of 77,347 patients discharged following hospitalisation with COVID-19, rates for the majority of outcomes peaked in the first month post-discharge, then declined over the following four months. Patients in the COVID-19 population had markedly higher risk of all outcomes compared to matched controls from the 2019 general population. Across the whole study period, the risk of outcomes was more similar when comparing patients discharged with COVID-19 to those discharged with pneumonia in 2019, although COVID-19 patients had higher risk of T2DM (15.2 versus 37.2 [rate per 1,000-person-years for COVID-19 versus pneumonia, respectively]; SHR, 1.46 [95% CI: 1.31 - 1.63]). 

Conclusions:  Risk of cardiometabolic and pulmonary adverse outcomes is markedly raised following discharge from hospitalisation with COVID-19 compared to the general population. However, excess risks were similar to those seen following discharge post-pneumonia. Overall, this suggests a large additional burden on healthcare resources.

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  1. Version published to 10.12688/wellcomeopenres.17735.1
  2. Version published to 10.1101/2021.01.22.21250304v3 on medRxiv
  3. ScreenIT

    SciScore for 10.1101/2021.01.22.21250304: (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
    Software and reproducibility: Data management was performed using the OpenSAFELY software, Python 3.8 and SQL, and analysis using Stata 16.1.
    Python
    suggested: (IPython, RRID:SCR_001658)

    Results from OddPub: Thank you for sharing your code.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Strengths and limitations: We were able to source our cohorts from the OpenSAFELY platform, which contains over 17m adults. This gave us a population of patients who were discharged following hospitalisation with COVID-19 of 31,569, allowing us to obtain precise estimates of the rate of each outcome. We were also able to draw on multiple linked data sources, including primary care records, hospitalisations and death certificates. This allows a more complete picture to be presented of the clinical activity surrounding each outcome. We believe that our use of an active control population of patients hospitalised with pneumonia in 2019 provides useful context for the rates of these outcomes in COVID-19 patients who survive hospitalisation. A comparison cohort could also have been attained by matching patients from the general population on various attributes such as age, sex and comorbidities. However, such a cohort would be lacking the exposure of an acute respiratory illness event requiring hospitalisation. We think presenting the rates in this context is more informative than within a general population. We note that our study aimed to describe clinical events that occurred after discharge from hospital, and therefore may not reflect the true additional morbidity burden of COVID-19 hospitalisation: specifically we did not set out to describe events that occurred during hospital admission with COVID-19 or pneumonia. However, in our view reliable analysis of in-hospital events ...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

    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

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  4. Version published to 10.1101/2021.01.22.21250304v1 on medRxiv
  5. Version published to 10.1101/2021.01.22.21250304v2 on medRxiv