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Comparison of methods for predicting COVID-19-related death in the general population using the OpenSAFELY platform.
Williamson, Elizabeth J; Tazare, John; Bhaskaran, Krishnan; McDonald, Helen I; Walker, Alex J; Tomlinson, Laurie; Wing, Kevin; Bacon, Sebastian; Bates, Chris; Curtis, Helen J; Forbes, Harriet J; Minassian, Caroline; Morton, Caroline E; Nightingale, Emily; Mehrkar, Amir; Evans, David; Nicholson, Brian D; Leon, David A; Inglesby, Peter; MacKenna, Brian; Davies, Nicholas G; DeVito, Nicholas J; Drysdale, Henry; Cockburn, Jonathan; Hulme, William J; Morley, Jessica; Douglas, Ian; Rentsch, Christopher T; Mathur, Rohini; Wong, Angel; Schultze, Anna; Croker, Richard; Parry, John; Hester, Frank; Harper, Sam; Grieve, Richard; Harrison, David A; Steyerberg, Ewout W; Eggo, Rosalind M; Diaz-Ordaz, Karla; Keogh, Ruth; Evans, Stephen J W; Smeeth, Liam; Goldacre, Ben.
Afiliación
  • Williamson EJ; London School of Hygiene and Tropical Medicine, Faculty of Epidemiology & Population Health, Keppel Street, London, WC1E 7HT, UK. Elizabeth.williamson@lshtm.ac.uk.
  • Tazare J; London School of Hygiene and Tropical Medicine, Faculty of Epidemiology & Population Health, Keppel Street, London, WC1E 7HT, UK.
  • Bhaskaran K; London School of Hygiene and Tropical Medicine, Faculty of Epidemiology & Population Health, Keppel Street, London, WC1E 7HT, UK.
  • McDonald HI; London School of Hygiene and Tropical Medicine, Faculty of Epidemiology & Population Health, Keppel Street, London, WC1E 7HT, UK.
  • Walker AJ; NIHR Health Protection Research Unit (HPRU) in Immunisation, London, UK.
  • Tomlinson L; The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX26GG, UK.
  • Wing K; London School of Hygiene and Tropical Medicine, Faculty of Epidemiology & Population Health, Keppel Street, London, WC1E 7HT, UK.
  • Bacon S; London School of Hygiene and Tropical Medicine, Faculty of Epidemiology & Population Health, Keppel Street, London, WC1E 7HT, UK.
  • Bates C; The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX26GG, UK.
  • Curtis HJ; TPP, TPP House, 129 Low Lane, Horsforth, Leeds, LS18 5PX, UK.
  • Forbes HJ; The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX26GG, UK.
  • Minassian C; University of Bristol, Beacon House, Queens Road, Bristol, BS8 1QU, UK.
  • Morton CE; London School of Hygiene and Tropical Medicine, Faculty of Epidemiology & Population Health, Keppel Street, London, WC1E 7HT, UK.
  • Nightingale E; The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX26GG, UK.
  • Mehrkar A; London School of Hygiene and Tropical Medicine, Faculty of Epidemiology & Population Health, Keppel Street, London, WC1E 7HT, UK.
  • Evans D; The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX26GG, UK.
  • Nicholson BD; The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX26GG, UK.
  • Leon DA; The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX26GG, UK.
  • Inglesby P; London School of Hygiene and Tropical Medicine, Faculty of Epidemiology & Population Health, Keppel Street, London, WC1E 7HT, UK.
  • MacKenna B; The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX26GG, UK.
  • Davies NG; The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX26GG, UK.
  • DeVito NJ; London School of Hygiene and Tropical Medicine, Faculty of Epidemiology & Population Health, Keppel Street, London, WC1E 7HT, UK.
  • Drysdale H; The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX26GG, UK.
  • Cockburn J; The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX26GG, UK.
  • Hulme WJ; TPP, TPP House, 129 Low Lane, Horsforth, Leeds, LS18 5PX, UK.
  • Morley J; The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX26GG, UK.
  • Douglas I; The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX26GG, UK.
  • Rentsch CT; London School of Hygiene and Tropical Medicine, Faculty of Epidemiology & Population Health, Keppel Street, London, WC1E 7HT, UK.
  • Mathur R; London School of Hygiene and Tropical Medicine, Faculty of Epidemiology & Population Health, Keppel Street, London, WC1E 7HT, UK.
  • Wong A; London School of Hygiene and Tropical Medicine, Faculty of Epidemiology & Population Health, Keppel Street, London, WC1E 7HT, UK.
  • Schultze A; London School of Hygiene and Tropical Medicine, Faculty of Epidemiology & Population Health, Keppel Street, London, WC1E 7HT, UK.
  • Croker R; London School of Hygiene and Tropical Medicine, Faculty of Epidemiology & Population Health, Keppel Street, London, WC1E 7HT, UK.
  • Parry J; The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, OX26GG, UK.
  • Hester F; TPP, TPP House, 129 Low Lane, Horsforth, Leeds, LS18 5PX, UK.
  • Harper S; TPP, TPP House, 129 Low Lane, Horsforth, Leeds, LS18 5PX, UK.
  • Grieve R; TPP, TPP House, 129 Low Lane, Horsforth, Leeds, LS18 5PX, UK.
  • Harrison DA; London School of Hygiene and Tropical Medicine, Faculty of Epidemiology & Population Health, Keppel Street, London, WC1E 7HT, UK.
  • Steyerberg EW; Intensive Care National Audit & Research Centre (ICNARC), 24 High Holborn, Holborn, London, WC1V 6AZ, UK.
  • Eggo RM; Leiden University Medical Center, Leiden, the Netherlands.
  • Diaz-Ordaz K; London School of Hygiene and Tropical Medicine, Faculty of Epidemiology & Population Health, Keppel Street, London, WC1E 7HT, UK.
  • Keogh R; London School of Hygiene and Tropical Medicine, Faculty of Epidemiology & Population Health, Keppel Street, London, WC1E 7HT, UK.
  • Evans SJW; London School of Hygiene and Tropical Medicine, Faculty of Epidemiology & Population Health, Keppel Street, London, WC1E 7HT, UK.
  • Smeeth L; London School of Hygiene and Tropical Medicine, Faculty of Epidemiology & Population Health, Keppel Street, London, WC1E 7HT, UK.
  • Goldacre B; London School of Hygiene and Tropical Medicine, Faculty of Epidemiology & Population Health, Keppel Street, London, WC1E 7HT, UK.
Diagn Progn Res ; 6(1): 6, 2022 Feb 24.
Article en En | MEDLINE | ID: mdl-35197114
ABSTRACT

BACKGROUND:

Obtaining accurate estimates of the risk of COVID-19-related death in the general population is challenging in the context of changing levels of circulating infection.

METHODS:

We propose a modelling approach to predict 28-day COVID-19-related death which explicitly accounts for COVID-19 infection prevalence using a series of sub-studies from new landmark times incorporating time-updating proxy measures of COVID-19 infection prevalence. This was compared with an approach ignoring infection prevalence. The target population was adults registered at a general practice in England in March 2020. The outcome was 28-day COVID-19-related death. Predictors included demographic characteristics and comorbidities. Three proxies of local infection prevalence were used model-based estimates, rate of COVID-19-related attendances in emergency care, and rate of suspected COVID-19 cases in primary care. We used data within the TPP SystmOne electronic health record system linked to Office for National Statistics mortality data, using the OpenSAFELY platform, working on behalf of NHS England. Prediction models were developed in case-cohort samples with a 100-day follow-up. Validation was undertaken in 28-day cohorts from the target population. We considered predictive performance (discrimination and calibration) in geographical and temporal subsets of data not used in developing the risk prediction models. Simple models were contrasted to models including a full range of predictors.

RESULTS:

Prediction models were developed on 11,972,947 individuals, of whom 7999 experienced COVID-19-related death. All models discriminated well between individuals who did and did not experience the outcome, including simple models adjusting only for basic demographics and number of comorbidities C-statistics 0.92-0.94. However, absolute risk estimates were substantially miscalibrated when infection prevalence was not explicitly modelled.

CONCLUSIONS:

Our proposed models allow absolute risk estimation in the context of changing infection prevalence but predictive performance is sensitive to the proxy for infection prevalence. Simple models can provide excellent discrimination and may simplify implementation of risk prediction tools.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Diagn Progn Res Año: 2022 Tipo del documento: Article País de afiliación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Diagn Progn Res Año: 2022 Tipo del documento: Article País de afiliación: Reino Unido