Evidence map›Paper›PMID 35710248›Full record

ArticleBMJ open2022

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

Christian Philip Stickels, Ramesh Nadarajah, Chris P Gale, Houyuan Jiang, Kieran J Sharkey, Ben Gibbison, Nick Holliman, Sara Lombardo, Lars Schewe, Matteo Sommacal and 6 more

Abstract read
In one paragraph

Article in BMJ open, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. The challenge of early discharge after transcatheter aortic valve implantation.EuroIntervention : journal of EuroPCR in collaboration with the Working Group on Interventional Cardiology of the European Society of Cardiology · 2025
    Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors.

Christian Philip StickelsDepartment of Mathematical Sciences, University of Liverpool, Liverpool, UK.ORCID 0000-0002-5286-9379
Ramesh NadarajahLeeds Institute for Data Analytics, University of Leeds, Leeds, UK.ORCID 0000-0001-9895-9356
Chris P GaleLeeds Institute for Data Analytics, University of Leeds, Leeds, UK.
Houyuan JiangJudge Business School, University of Cambridge, Cambridge, UK.ORCID 0000-0002-2466-6929
Kieran J SharkeyDepartment of Mathematical Sciences, University of Liverpool, Liverpool, UK.ORCID 0000-0002-7210-9246
Ben GibbisonCardiac Anaesthesia and Intensive Care, Bristol Medical School, Bristol, UK.
Nick HollimanDepartment of Informatics, King's College London, London, UK.ORCID 0000-0003-4418-4908
Sara LombardoDepartment of Mathematical Sciences, Loughborough University, Loughborough, UK.ORCID 0000-0003-3545-163X
Lars ScheweSchool of Mathematics and Maxwell Institute for Mathematical Sciences, University of Edinburgh, Edinburgh, UK.ORCID 0000-0002-3778-262X
Matteo SommacalDepartment of Mathematics, Physics and Electrical Engineering, Northumbria University, Newcastle upon Tyne, UK.ORCID 0000-0003-2820-2117
Louise SunDivision of Cardiac Anesthesiology, University of Ottawa Heart Institute, Ottawa, Ontario, Canada.ORCID 0000-0003-3381-3115
Jonathan Weir-McCallDepartment of Radiology, University of Cambridge, Cambridge, UK.
Katherine CheemaHealth Intelligence, British Heart Foundation, London, UK.
James H F RuddDepartment of Medicine, University of Cambridge, Cambridge, UK.
Mamas MamasKeele Cardiovascular Research Group, Keele University, Keele, UK.ORCID 0000-0001-9241-8890
Feryal ErhunJudge Business School, University of Cambridge, Cambridge, UK f.erhun@jbs.cam.ac.uk.ORCID 0000-0003-0339-7085

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo 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.

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 strategies listed under Interventions may affect the time to clear the backlog of patients waiting for treatment and the associated waiting list mortality.

settingThe NHS in England.

participantsEstimated 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.

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 (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.

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 to treatment and many hundreds dying while waiting.

Indexed as

Aortic Valve StenosisCOVID-19HumansModels, TheoreticalState MedicineWaiting ListscardiologyCOVID-19valvular heart disease

Identifiers

PMID35710248
PMCPMC9207579

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.