Changes in the rate of cardiometabolic and pulmonary events during the COVID-19 pandemic

  1. Alex J Walker
  2. John Tazare
  3. George Hickman
  4. Christopher T Rentsch
  5. Elizabeth J Williamson
  6. Krishnan Bhaskaran
  7. David Evans
  8. Kevin Wing
  9. Rohini Mathur
  10. Angel YS Wong
  11. Anna Schultze
  12. Sebastian CJ Bacon
  13. Chris Bates
  14. Caroline E Morton
  15. Helen J Curtis
  16. Emily Nightingale
  17. Helen I McDonald
  18. Amir Mehrkar
  19. Peter Inglesby
  20. Brian MacKenna
  21. Jonathan Cockburn
  22. William J Hulme
  23. Harriet Forbes
  24. Caroline Minassian
  25. Richard Croker
  26. John Parry
  27. Frank Hester
  28. Sam Harper
  29. Rosalind M Eggo
  30. Stephen JW Evans
  31. Liam Smeeth
  32. Ian J Douglas
  33. Laurie Tomlinson
  34. Ben Goldacre

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Abstract

Background

There has been extensive speculation about the relationship between COVID-19 and various cardiometabolic and pulmonary conditions. This a complex question: COVID-19 may cause a cardiometabolic or respiratory event; admission for a clinical event may result in hospital-acquired SARS-CoV-2 infection; both may contribute to a patient surpassing the threshold for presenting to services; and the presence of a pandemic may change whether patients present to services at all. To inform analysis of these questions, we set out to describe the overall rate of various key clinical events over time, and their relationship with COVID-19.

Methods

Working on behalf of NHS England, we used data from the OpenSAFELY platform containing data from approximately 40% of the population of England. We selected the whole adult population of 17m patients and within this identified two further mutually exclusive groups: patients who tested positive for SARS-CoV-2 in the community; and patients hospitalised with COVID-19. We report counts of death, DVT, PE, ischaemic stroke, MI, heart failure, AKI and diabetic ketoacidosis in each month between February 2019 and October 2020 within each of: the general population, community SARS-CoV-2 cases, and hospitalised patients with COVID-19. Outcome events were defined using hospitalisations, GP records and cause of death data.

Results

For all outcomes except death there was a lower count of events in April 2020 compared to April 2019. For most outcomes the minimum count of events was in April 2020, where the decrease compared to April 2019 in events ranged from 5.9% (PE) to 40.0% (heart failure). Despite hospitalised COVID-19 patients making up just 0.14% of the population in April 2020, these patients accounted for an extremely high proportion of cardiometabolic and respiratory events in that month (range of proportions 10.3% (DVT) to 33.5% (AKI)).

Interpretation

We observed a substantial drop in the incidence of cardiometabolic and pulmonary events in the non-COVID-19 general population, but high occurrence of COVID-19 among patients with these events. Shortcomings in routine NHS secondary care data, especially around the timing and order of events, make causal interpretations challenging. We caution that the intermediate findings reported here should be used to inform the design and interpretation of any studies using a general population comparator to evaluate the relationship between COVID-19 and other clinical events.

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  1. ScreenIT

    SciScore for 10.1101/2021.02.17.21251812: (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 Python 3.8.
    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: A key strength of our analysis is size: our analysis is based on the detailed primary care and summary hospital records of over 17 million people. Our aim was to give a high level overview of how the volume of events changed during the pandemic, and the co-occurrence of COVID-19 and each event: although we observed a high proportion of events within the COVID-19 population, we did not design the study to demonstrate a causal link between the two. Such relationships are likely to be complex: for example, some patients may be admitted with a cardiometabolic or respiratory event, and then acquire COVID-19 within hospital; some patients may develop COVID-19 and then experience a cardiometabolic or respiratory event as a consequence; some patients may have both COVID-19 and a cardiometabolic or respiratory event in the community, with both contributing to their presentation to services. We did not aim to determine the order that events occurred in: however in many cases the order of events cannot be reliably ascertained from the data available at national level, such as SUS or HES data, combined with SGSS test date data. For example it is not possible to reliably determine the time course or order of events within a hospital spell. Findings in context: A previous study using in-hospital records reported a 40% reduction in the incidence of acute coronary syndromes during the peak of Wave 1 of the pandemic in England, consistent with our findings, but did ...

    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.
    • No protocol registration statement was detected.

    About SciScore

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  2. Version published to 10.1101/2021.02.17.21251812v1 on medRxiv