Increased risk of COVID-19-related admissions in patients with active solid organ cancer in the West Midlands region of the UK: a retrospective cohort study.

ABSTRACT

OBJECTIVE: Susceptibility of patients with cancer to COVID-19 pneumonitis has been variable. We aim to quantify the risk of hospitalisation in patients with active cancer and use a machine learning algorithm (MLA) and traditional statistics to predict clinical outcomes and mortality. DESIGN: Retrospective cohort study. SETTING: A single UK district general hospital. PARTICIPANTS: Data on total hospital admissions between March 2018 and June 2020, all active cancer diagnoses between March 2019 and June 2020 and clinical parameters of COVID-19-positive admissions between March 2020 and June 2020 were collected. 526 COVID-19 admissions without an active cancer diagnosis were compared with 87 COVID-19 admissions with an active cancer diagnosis. PRIMARY AND SECONDARY OUTCOME MEASURES: 30-day and 90-day post-COVID-19 survival. RESULTS: In total, 613 patients were enrolled with male to female ratio of 16 and median age of 77 years. The estimated infection rate of COVID-19 was 87 of 22 729 (0.4%) in the patients with cancer and 526 of 404 379 (0.1%) in the population without cancer (OR of being hospitalised with COVID-19 if having cancer is 2.942671 (95% CI 2.344522 to 3.693425); p<0.001). Survival was reduced in patients with cancer with COVID-19 at 90 days. R-Studio software determined the association between cancer status, COVID-19 and 90-day survival against variables using MLA. Multivariate analysis showed increases in age (OR 1.039 (95% CI 1.020 to 1.057), p<0.001), urea (OR 1.005 (95% CI 1.002 to 1.007), p<0.001) and C reactive protein (CRP) (OR 1.065 (95% CI 1.016 to 1.116), p<0.008) are associated with greater 30-day and 90-day mortality. The MLA model examined the contribution of predictive variables for 90-day survival (area under the curve 0.749); with transplant patients, age, male gender and diabetes mellitus being predictors of greater mortality. CONCLUSIONS: Active cancer diagnosis has a threefold increase in risk of hospitalisation with COVID-19. Increased age, urea and CRP predict mortality in patients with cancer. MLA complements traditional statistical analysis in identifying prognostic variables for outcomes of COVID-19 infection in patients with cancer. This study provides proof of concept for MLA in risk prediction for COVID-19 in patients with cancer and should inform a redesign of cancer services to ensure safe delivery of cancer care.