Predicting COVID-19 related death using the OpenSAFELY platform

  1. The OpenSAFELY Collaborative
  2. Elizabeth J Williamson
  3. John Tazare
  4. Krishnan Bhaskaran
  5. Helen I McDonald
  6. Alex J Walker
  7. Laurie Tomlinson
  8. Kevin Wing
  9. Sebastian Bacon
  10. Chris Bates
  11. Helen J Curtis
  12. Harriet Forbes
  13. Caroline Minassian
  14. Caroline E Morton
  15. Emily Nightingale
  16. Amir Mehrkar
  17. Dave Evans
  18. Brian D Nicholson
  19. Dave Leon
  20. Peter Inglesby
  21. Brian MacKenna
  22. Nicholas G Davies
  23. Nicholas J DeVito
  24. Henry Drysdale
  25. Jonathan Cockburn
  26. Will Hulme
  27. Jess Morley
  28. Ian Douglas
  29. Christopher T Rentsch
  30. Rohini Mathur
  31. Angel Wong
  32. Anna Schultze
  33. Richard Croker
  34. John Parry
  35. Frank Hester
  36. Sam Harper
  37. Richard Grieve
  38. David A Harrison
  39. Ewout W. Steyerberg
  40. Rosalind M Eggo
  41. Karla Diaz-Ordaz
  42. Ruth Keogh
  43. Stephen JW Evans
  44. Liam Smeeth
  45. Ben Goldacre
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Abstract

Objectives

To compare approaches for obtaining relative and absolute estimates of risk of 28-day COVID-19 mortality for adults in the general population of England in the context of changing levels of circulating infection.

Design

Three designs were compared. (A) case-cohort which does not explicitly account for the time-changing prevalence of COVID-19 infection, (B) 28-day landmarking, a series of sequential overlapping sub-studies incorporating time-updating proxy measures of the prevalence of infection, and (C) daily landmarking. Regression models were fitted to predict 28-day COVID-19 mortality.

Setting

Working on behalf of NHS England, we used clinical data from adult patients from all regions of England held in the TPP SystmOne electronic health record system, linked to Office for National Statistics (ONS) mortality data, using the OpenSAFELY platform.

Participants

Eligible participants were adults aged 18 or over, registered at a general practice using TPP software on 1 st March 2020 with recorded sex, postcode and ethnicity. 11,972,947 individuals were included, and 7,999 participants experienced a COVID-19 related death. The study period lasted 100 days, ending 8 th June 2020.

Predictors

A range of demographic characteristics and comorbidities were used as potential predictors. Local infection prevalence was estimated with three proxies: modelled based on local prevalence and other key factors; rate of A&E COVID-19 related attendances; and rate of suspected COVID-19 cases in primary care.

Main outcome measures

COVID-19 related death.

Results

All models discriminated well between patients who did and did not experience COVID-19 related death, with C-statistics ranging from 0.92-0.94. Accurate estimates of absolute risk required data on local infection prevalence, with modelled estimates providing the best performance.

Conclusions

Reliable estimates of absolute risk need to incorporate changing local prevalence of infection. Simple models can provide very good discrimination and may simplify implementation of risk prediction tools in practice.

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