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

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 1:6 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.

Automated summary

Patients with active cancer have a threefold increase in risk of hospitalisation with COVID-19, and higher age, urea, and CRP levels are associated with increased mortality. Machine learning algorithm (MLA) shows potential in predicting outcomes of COVID-19 infection in cancer patients.