predicting covid 19 related death using the opensafely platform CORD-Papers-2022-06-02 (Version 1)

Title: Predicting COVID-19 related death using the OpenSAFELY platform
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 1st March 2020 with recorded sex postcode and ethnicity. 11972947 individuals were included and 7999 participants experienced a COVID-19 related death. The study period lasted 100 days ending 8th 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.
Published: 2021-03-01
DOI: 10.1101/2021.02.25.21252433
DOI_URL: http://doi.org/10.1101/2021.02.25.21252433
Author Name: Williamson E J
Author link: https://covid19-data.nist.gov/pid/rest/local/author/williamson_e_j
Author Name: Tazare J
Author link: https://covid19-data.nist.gov/pid/rest/local/author/tazare_j
Author Name: Bhaskaran K
Author link: https://covid19-data.nist.gov/pid/rest/local/author/bhaskaran_k
Author Name: McDonald H I
Author link: https://covid19-data.nist.gov/pid/rest/local/author/mcdonald_h_i
Author Name: Walker A J
Author link: https://covid19-data.nist.gov/pid/rest/local/author/walker_a_j
Author Name: Tomlinson L
Author link: https://covid19-data.nist.gov/pid/rest/local/author/tomlinson_l
Author Name: Wing K
Author link: https://covid19-data.nist.gov/pid/rest/local/author/wing_k
Author Name: Bacon S
Author link: https://covid19-data.nist.gov/pid/rest/local/author/bacon_s
Author Name: Bates C
Author link: https://covid19-data.nist.gov/pid/rest/local/author/bates_c
Author Name: Curtis H J
Author link: https://covid19-data.nist.gov/pid/rest/local/author/curtis_h_j
Author Name: Forbes H
Author link: https://covid19-data.nist.gov/pid/rest/local/author/forbes_h
Author Name: Minassian C
Author link: https://covid19-data.nist.gov/pid/rest/local/author/minassian_c
Author Name: Morton C E
Author link: https://covid19-data.nist.gov/pid/rest/local/author/morton_c_e
Author Name: Nightingale E
Author link: https://covid19-data.nist.gov/pid/rest/local/author/nightingale_e
Author Name: Mehrkar A
Author link: https://covid19-data.nist.gov/pid/rest/local/author/mehrkar_a
Author Name: Evans D
Author link: https://covid19-data.nist.gov/pid/rest/local/author/evans_d
Author Name: Nicholson B D
Author link: https://covid19-data.nist.gov/pid/rest/local/author/nicholson_b_d
Author Name: Leon D
Author link: https://covid19-data.nist.gov/pid/rest/local/author/leon_d
Author Name: Inglesby P
Author link: https://covid19-data.nist.gov/pid/rest/local/author/inglesby_p
Author Name: MacKenna B
Author link: https://covid19-data.nist.gov/pid/rest/local/author/mackenna_b
Author Name: Davies N G
Author link: https://covid19-data.nist.gov/pid/rest/local/author/davies_n_g
Author Name: DeVito N J
Author link: https://covid19-data.nist.gov/pid/rest/local/author/devito_n_j
Author Name: Drysdale H
Author link: https://covid19-data.nist.gov/pid/rest/local/author/drysdale_h
Author Name: Cockburn J
Author link: https://covid19-data.nist.gov/pid/rest/local/author/cockburn_j
Author Name: Hulme W J
Author link: https://covid19-data.nist.gov/pid/rest/local/author/hulme_w_j
Author Name: Morley J
Author link: https://covid19-data.nist.gov/pid/rest/local/author/morley_j
Author Name: Douglas I
Author link: https://covid19-data.nist.gov/pid/rest/local/author/douglas_i
Author Name: Rentsch C T
Author link: https://covid19-data.nist.gov/pid/rest/local/author/rentsch_c_t
Author Name: Mathur R
Author link: https://covid19-data.nist.gov/pid/rest/local/author/mathur_r
Author Name: Wong A
Author link: https://covid19-data.nist.gov/pid/rest/local/author/wong_a
Author Name: Schultze A
Author link: https://covid19-data.nist.gov/pid/rest/local/author/schultze_a
Author Name: Croker R
Author link: https://covid19-data.nist.gov/pid/rest/local/author/croker_r
Author Name: Parry J
Author link: https://covid19-data.nist.gov/pid/rest/local/author/parry_j
Author Name: Hester F
Author link: https://covid19-data.nist.gov/pid/rest/local/author/hester_f
Author Name: Harper S
Author link: https://covid19-data.nist.gov/pid/rest/local/author/harper_s
Author Name: Grieve R
Author link: https://covid19-data.nist.gov/pid/rest/local/author/grieve_r
Author Name: Harrison D A
Author link: https://covid19-data.nist.gov/pid/rest/local/author/harrison_d_a
Author Name: Steyerberg E W
Author link: https://covid19-data.nist.gov/pid/rest/local/author/steyerberg_e_w
Author Name: Eggo R M
Author link: https://covid19-data.nist.gov/pid/rest/local/author/eggo_r_m
Author Name: Diaz Ordaz K
Author link: https://covid19-data.nist.gov/pid/rest/local/author/diaz_ordaz_k
Author Name: Keogh R
Author link: https://covid19-data.nist.gov/pid/rest/local/author/keogh_r
Author Name: Evans S J
Author link: https://covid19-data.nist.gov/pid/rest/local/author/evans_s_j
Author Name: Smeeth L
Author link: https://covid19-data.nist.gov/pid/rest/local/author/smeeth_l
Author Name: Goldacre B
Author link: https://covid19-data.nist.gov/pid/rest/local/author/goldacre_b
license: medrxiv
source_x: MedRxiv; WHO
source_x_url: https://www.who.int/
url: https://doi.org/10.1101/2021.02.25.21252433 http://medrxiv.org/cgi/content/short/2021.02.25.21252433v1?rss=1
has_full_text: FALSE
G_ID: predicting_covid_19_related_death_using_the_opensafely_platform