Prediction models for covid-19 outcomes
Matthew Sperrin, Brian McMillan · BMJ · 2020
Robust models that predict the prognosis of coronavirus 2019 (covid-19) are urgently needed to support decisions about shielding, hospital admission, treatment, and population level interventions.With cases increasing in the UK and elsewhere, and winter approaching, such models could have a rapid clinical impact.Two linked articles report on two newly developed covid-19 prediction models.QCOVID is a risk prediction model for covid-19 related mortality for use in the general population (doi:10.1136/bmj.m3731), 1 whereas the 4C mortality score is for use on admission to hospital (doi:10.1136/bmj.m3339). 2 Notably, these models are of higher quality than others published to date, 3 having been developed using ample sample sizes, 4 with generally appropriate modelling choices, and suitably internally validated and reported.5 6 Nevertheless, we sound a note of caution in their use.QCOVID predicts the risk of catching and dying from (or being admitted to hospital with) covid-19 in the general population. 1 The authors rightly emphasise the fact that predicting separately either the probability of catching covid-19 or the probability of dying from it is not possible, owing primarily to incomplete knowledge of who actually has the disease.However, this conflation causes limitations in the model's application.The risk of catching covid-19 depends on an individual's behaviour and the local dynamics of the disease, which are not modelled by QCOVID.These dynamics, such as local disease prevalence, change rapidly.Therefore, calibration of the model is likely to deteriorate rapidly.Moreover, recent data show a shift in the age distribution of cases towards younger people; discrimination of the model may also drop, therefore, as age is a strong predictor.QCOVID is, however, described as a "living" model 1 ; with regular updating, these problems can be mitigated.