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

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Stickels, CP 
Nadarajah, R 
Gale, CP 
Sharkey, KJ 


Objectives. To 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. 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 following strategies 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 aortic stenosis patients 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 whilst 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 whilst 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 whilst waiting.


Funder: Wellcome Trust


COVID-19, cardiology, valvular heart disease, Aortic Valve Stenosis, COVID-19, Humans, Models, Theoretical, State Medicine, Waiting Lists

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BMJ Open

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BMJ Journals
EPSRC (EP/T017961/1)
Engineering and Physical Sciences Research Council (EP/N014588/1)
BG is supported by the NIHR Bristol Biomedical Research Centre at the University of Bristol and University Hospitals Bristol and Weston NHS Foundation Trust. JHFR is part-supported by the NIHR Cambridge Biomedical Research Centre, the British Heart Foundation, HEFCE, the EPSRC Cambridge Centre for Mathematics of Information in Healthcare and the Wellcome Trust.