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 c...
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| Format: | Article |
| Language: | English |
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BMJ Publishing Group
2022-06-01
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| Series: | BMJ Open |
| Online Access: | https://bmjopen.bmj.com/content/12/6/e059309.full |
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| author | Chris P Gale Jonathan Weir-McCall James H F Rudd Mamas Mamas Ramesh Nadarajah Ben Gibbison Louise Sun Christian Philip Stickels Houyuan Jiang Kieran J Sharkey Nick Holliman Sara Lombardo Lars Schewe Matteo Sommacal Katherine Cheema Feryal Erhun |
| author_facet | Chris P Gale Jonathan Weir-McCall James H F Rudd Mamas Mamas Ramesh Nadarajah Ben Gibbison Louise Sun Christian Philip Stickels Houyuan Jiang Kieran J Sharkey Nick Holliman Sara Lombardo Lars Schewe Matteo Sommacal Katherine Cheema Feryal Erhun |
| author_sort | Chris P Gale |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-daa5ac6b29444b0eada06a7b92e84b42 |
| institution | Kabale University |
| issn | 2044-6055 |
| language | English |
| publishDate | 2022-06-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Open |
| spelling | doaj-art-daa5ac6b29444b0eada06a7b92e84b422025-01-28T05:10:08ZengBMJ Publishing GroupBMJ Open2044-60552022-06-0112610.1136/bmjopen-2021-059309Aortic stenosis post-COVID-19: a mathematical model on waiting lists and mortalityChris P Gale0Jonathan Weir-McCall1James H F Rudd2Mamas Mamas3Ramesh Nadarajah4Ben Gibbison5Louise Sun6Christian Philip Stickels7Houyuan Jiang8Kieran J Sharkey9Nick Holliman10Sara Lombardo11Lars Schewe12Matteo Sommacal13Katherine Cheema14Feryal Erhun15Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UKRadiology, University of Cambridge School of Clinical Medicine, Cambridge, UK1 Section of Cardiorespiratory Medicine, University of Cambridge, Cambridge, UKKeele Cardiovascular Research Group, Keele University, Keele, UKLeeds Institute for Data Analytics, University of Leeds, Leeds, UK3 Cardiac Anaesthesia and Intensive Care, Bristol Medical School, University of Bristol, Bristol, UKDivision of Cardiac Anesthesiology, University of Ottawa Heart Institute, Ottawa, Ontario, CanadaDepartment of Mathematical Sciences, University of Liverpool, Liverpool, UKJudge Business School, University of Cambridge, Cambridge, UKDepartment of Mathematical Sciences, University of Liverpool, Liverpool, UKDepartment of Informatics, King`s College London, London, UKDepartment of Mathematical Sciences, Loughborough University, Loughborough, UKSchool of Mathematics and Maxwell Institute for Mathematical Sciences, University of Edinburgh, Edinburgh, UKDepartment of Mathematics, Physics and Electrical Engineering, Northumbria University, Newcastle upon Tyne, UKBritish Heart Foundation, London, UKJudge Business School, University of Cambridge, Cambridge, UKObjectives 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.https://bmjopen.bmj.com/content/12/6/e059309.full |
| spellingShingle | Chris P Gale Jonathan Weir-McCall James H F Rudd Mamas Mamas Ramesh Nadarajah Ben Gibbison Louise Sun Christian Philip Stickels Houyuan Jiang Kieran J Sharkey Nick Holliman Sara Lombardo Lars Schewe Matteo Sommacal Katherine Cheema Feryal Erhun Aortic stenosis post-COVID-19: a mathematical model on waiting lists and mortality BMJ Open |
| title | Aortic stenosis post-COVID-19: a mathematical model on waiting lists and mortality |
| title_full | Aortic stenosis post-COVID-19: a mathematical model on waiting lists and mortality |
| title_fullStr | Aortic stenosis post-COVID-19: a mathematical model on waiting lists and mortality |
| title_full_unstemmed | Aortic stenosis post-COVID-19: a mathematical model on waiting lists and mortality |
| title_short | Aortic stenosis post-COVID-19: a mathematical model on waiting lists and mortality |
| title_sort | aortic stenosis post covid 19 a mathematical model on waiting lists and mortality |
| url | https://bmjopen.bmj.com/content/12/6/e059309.full |
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