Process modelling of NHS cardiovascular waiting lists in response to the COVID-19 pandemic.

OBJECTIVE: To model the referral, diagnostic and treatment pathway for cardiovascular disease (CVD) in the English National Health Service (NHS) to provide commissioners and managers with a methodology to optimise patient flow and reduce waiting lists. STUDY DESIGN: A systems dynamics approach modelling the CVD healthcare system in England. The model is designed to capture current and predict future states of waiting lists. SETTING: Routinely collected, publicly available data streams of primary and secondary care, sourced from NHS Digital, NHS England, the Office of National Statistics and StatsWales. DATA COLLECTION AND EXTRACTION METHODS: The data used to train and validate the model were routinely collected and publicly available data. It was extracted and implemented in the model using the PySD package in python. RESULTS: NHS cardiovascular waiting lists in England have increased by over 40% compared with pre- COVID-19 levels. The rise in waiting lists was primarily due to restrictions in referrals from primary care, creating a bottleneck postpandemic. Predictive models show increasing point capacities within the system may paradoxically worsen downstream flow. While there is no simple rate-limiting step, the intervention that would most improve patient flow would be to increase consultant outpatient appointments. CONCLUSIONS: The increase in NHS CVD waiting lists in England can be captured using a systems dynamics approach, as can the future state of waiting lists in the presence of further shocks/interventions. It is important for those planning services to use such a systems-oriented approach because the feed-forward and feedback nature of patient flow through referral, diagnostics and treatment leads to counterintuitive effects of interventions designed to reduce waiting lists.

Aortic stenosis post-COVID-19: a mathematical model on waiting lists and mortality.

OBJECTIVES: To provide estimates for how different treatment pathways for the management of severe aortic stenosis (AS) may affect National Health Service (NHS) England waiting list duration and associated mortality. DESIGN: We 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 strategies listed under Interventions may affect the time to clear the backlog of patients waiting for treatment and the associated waiting list mortality. SETTING: The NHS in England. PARTICIPANTS: Estimated patients with AS in England. INTERVENTIONS: (1) 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. RESULTS: In 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 (533-848) days with 1419 (597-2189) deaths while waiting during this time. A 20% capacity increase would require 535 (434-666) days, with an associated mortality of 1172 (466-1859). A combination of converting 40% cases and increasing capacity by 20% would clear the backlog within a year (343 (281-410) days) with 784 (292-1324) deaths while awaiting treatment. CONCLUSION: A 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 to treatment and many hundreds dying while waiting.

What is the relationship between free flow and pressure flow studies in women?

INTRODUCTION AND HYPOTHESIS: The relationship between free flow (FFS) and pressure flow (PFS) voiding studies remains uncertain and the effect of a urethral catheter on flow rates has not been determined. The relationship between residuals obtained at FF and PFS has yet to be established. METHODS: This was a prospective cohort study based on 474 consecutive women undergoing cystometry using different sized urethral catheters at different centres. FFS and PFS data were compared for different conditions and the relationship of residuals analysed for FFS and PFS. The null hypothesis was that urethral catheters do not produce an alteration in maximum flow rates for PFS and FF studies. RESULTS: Urethral catheterisation results in lower flow rates (p < 0.01) and this finding is confirmed when flows are corrected for voided volume (p < 0.01). FFS and PFS maximum flow rates are lower in women with DO than USI (p < 0.01). A 6-F urethral catheter does not have a significantly greater effect than a 4.5-F urethral catheter. A mathematical model can be applied to transform FFS to PFS flow rates and vice versa. There was no significant difference between the mean residuals of the two groups (FFS vs PFS-two-tailed t = 0.54, p = 0.59). Positive residuals in FFS showed a good association with positive residuals in the PFS (r = 0.53, p < 0.01) CONCLUSIONS: Urethral catheterisation results in lower maximum flow rates. The relationship can be compared mathematically. The null hypothesis can be rejected.