Development and validation of an algorithm to estimate the risk of severe complications of COVID-19: a retrospective cohort study in primary care in the Netherlands

OBJECTIVE: To develop an algorithm (sCOVID) to predict the risk of severe complications of COVID-19 in a community-dwelling population to optimise vaccination scenarios. DESIGN: Population-based cohort study. SETTING: 264 Dutch general practices contributing to the NL-COVID database. PARTICIPANTS: 6074 people aged 0–99 diagnosed with COVID-19. MAIN OUTCOMES: Severe complications (hospitalisation, institutionalisation, death). The algorithm was developed from a training data set comprising 70% of the patients and validated in the remaining 30%. Potential predictor variables included age, sex, chronic comorbidity score (CCS) based on risk factors for COVID-19 complications, obesity, neighbourhood deprivation score (NDS), first or second COVID-19 wave and confirmation test. Six population vaccination scenarios were explored: (1) random (naive), (2) random for persons above 60 years (60plus), (3) oldest patients first in age band of 5 years (oldest first), (4) target population of the annual influenza vaccination programme (influenza), (5) those 25–65 years of age first (worker), and (6) risk based using the prediction algorithm (sCOVID). RESULTS: Severe complications were reported in 243 (4.8%) people with 59 (20.3%) nursing home admissions, 181 (62.2%) hospitalisations and 51 (17.5%) deaths. The algorithm included age, sex, CCS, NDS, wave and confirmation test (c-statistic=0.91, 95% CI 0.88 to 0.94) in the validation set. Applied to different vaccination scenarios, the proportion of people needed to be vaccinated to reach a 50% reduction of severe complications was 67.5%, 50.0%, 26.1%, 16.0%, 10.0% and 8.4% for the worker, naive, influenza, 60plus, oldest first and sCOVID scenarios, respectively. CONCLUSION: The sCOVID algorithm performed well to predict the risk of severe complications of COVID-19 in the first and second waves of COVID-19 infections in this Dutch population. The regression estimates can and need to be adjusted for future predictions. The algorithm can be applied to identify persons with highest risks from data in the electronic health records of general practitioners (GPs).