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

Stickels, Christian Philip, Nadarajah, Ramesh, Gale, Chris P, Jiang, Houyuan, Sharkey, Kieran J, Gibbison, Ben, Holliman, Nick, Lombardo, Sara, Schewe, Lars, Sommacal, Matteo, Sun, Louise, Weir-McCall, Jonathan, Cheema, Katherine, Rudd, James H F, Mamas, Mamas and Erhun, Feryal (2022) Aortic stenosis post-COVID-19: a mathematical model on waiting lists and mortality. BMJ Open, 12 (6). e059309. ISSN 2044-6055

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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.

Item Type: Article
Additional Information: Funding information: This study was part funded by EPSRC Cambridge Centre for Mathematics of Information in Healthcare, grant number EP/T017961/1.
Subjects: B900 Others in Subjects allied to Medicine
G900 Others in Mathematical and Computing Sciences
Depositing User: John Coen
Date Deposited: 17 Jun 2022 11:28
Last Modified: 17 Jun 2022 11:30

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