Comparison of methods for predicting COVID-19-related death in the general population using the OpenSAFELY platform
Authors:   The OpenSAFELY Collaborative, 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 
Published: February 2022

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.