Aortic stenosis post-COVID-19: A mathematical model on waiting lists and mortality (preprint)

ObjectivesTo provide estimates for how different treatment pathways for the management of severe aortic stenosis (AS) may affect NHS England waiting list duration and associated mortality. DesignWe constructed a mathematical model of the excess waiting list and found the closed-form analytic solution to that model. From published data, we calculated estimates for how the following strategies may affect the time to clear the backlog of patients waiting for treatment and the associated waiting list mortality. Interventions1) increasing the capacity for the treatment of severe AS, 2) converting proportions of cases from surgery to transcatheter aortic valve implantation, and 3) a combination of these two. ResultsIn a capacitated system, clearing the backlog by returning to pre-COVID-19 capacity is not possible. A conversion rate of 50% would clear the backlog within 666 (95% CI, 533-848) days with 1419 (95% CI, 597-2189) deaths whilst waiting during this time. A 20% capacity increase would require 535 (95% CI, 434-666) days, with an associated mortality of 1172 (95% CI, 466-1859). A combination of converting 40% cases and increasing capacity by 20% would clear the backlog within a year (343 (95% CI, 281-410) days) with 784 (95% CI, 292-1324) deaths whilst awaiting treatment. ConclusionA strategy change to the management of severe AS is required to reduce the NHS backlog and waiting list deaths during the post-COVID-19 recovery period. However, plausible adaptations will still incur a substantial wait and many hundreds dying without treatment.

A Comparison of Time-Series Predictions for Healthcare Emergency Department Indicators and the Impact of COVID-19

Across the world, healthcare systems are under stress and this has been hugely exacerbated by the COVID pandemic. Key Performance Indicators (KPIs), usually in the form of time-series data, are used to help manage that stress. Making reliable predictions of these indicators, particularly for emergency departments (ED), can facilitate acute unit planning, enhance quality of care and optimise resources. This motivates models that can forecast relevant KPIs and this paper addresses that need by comparing the Autoregressive Integrated Moving Average (ARIMA) method, a purely statistical model, to Prophet, a decomposable forecasting model based on trend, seasonality and holidays variables, and to the General Regression Neural Network (GRNN), a machine learning model. The dataset analysed is formed of four hourly valued indicators from a UK hospital: Patients in Department;Number of Attendances;Unallocated Patients with a DTA (Decision to Admit);Medically Fit for Discharge. Typically, the data exhibit regular patterns and seasonal trends and can be impacted by external factors such as the weather or major incidents. The COVID pandemic is an extreme instance of the latter and the behaviour of sample data changed dramatically. The capacity to quickly adapt to these changes is crucial and is a factor that shows better results for GRNN in both accuracy and reliability.