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.

Evaluating service development in critical care: The impact of establishing a medical high dependency unit on intensive care unit workload, case mix, and mortality.

BACKGROUND: Critical care services underpin the delivery of many types of secondary care, and there is increasing focus on how to best deliver such services. The aim of this study was to investigate the impact of establishing a medical high dependency unit, in a tertiary referral center, on the workload, case mix, and mortality of the intensive care unit. METHODS: Single-center, 11-year retrospective study of patients admitted to the general intensive care unit, before and after the opening of the medical high dependency unit, using interrupted time series methodology. RESULTS: Over the duration of the study period, 3209 medical patients were admitted to the intensive care unit. There was a constant rate of medical admissions to the intensive care unit until the opening of the medical high dependency unit, followed by a statistically significant decline thereafter. There was a statistically significant decrease in the average severity of illness of medical patients prior to the opening of the medical high dependency unit, but there was no evidence of a change following the opening of the unit. There was no evidence of a statistically significant change in the estimated mean standardized mortality ratio for either medical or surgical admissions after the intervention. CONCLUSIONS: The opening of a medical high dependency unit had a minimal impact on the intensive care unit. There was, in all likelihood, an unmet need-of less seriously ill patients, who were previously looked after on a normal ward, but did not require intensive care unit admission-who are now cared for in the new medical high dependency unit. Interrupted time series analysis, although not without limitations, is a useful mean of evaluating changes in service delivery.