Evidence map›Paper›PMID 41583839›Full record

ArticleFrontiers in surgery2025

Predictive modelling of vascular surgery trends using machine learning: a comparative study of Irish public and private tertiary referral centres.

Sherif Sultan, Yogesh Acharya, Mohamed S Sultan, Omnia Zayed, Osama Soliman

Abstract read
In one paragraph

Article in Frontiers in surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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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

5 authors.

Sherif Sultan *Department of Vascular and Endovascular Surgery, Western Vascular Institute, University Hospital Galway, University of Galway, Galway, Ireland.
Yogesh Acharya *Department of Vascular and Endovascular Surgery, Western Vascular Institute, University Hospital Galway, University of Galway, Galway, Ireland.
Mohamed S SultanDepartment of Vascular and Endovascular Surgery, Western Vascular Institute, University Hospital Galway, University of Galway, Galway, Ireland.
Omnia ZayedInsight Research Ireland Center for Data Analytics, University of Galway, Galway, Ireland.
Osama SolimanCardiovascular Research Institute (CVRI) & PRECISE Core Labs, Mater Private Network, Dublin, Ireland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Vascular diseases are increasing in Ireland as well as worldwide alongside an ageing society, posing a growing demand for trained and qualified healthcare professionals. In this study, we have analysed current practices of vascular interventions by using the data from the vascular tertiary centre to predict the future size and capacity of the vascular surgery workforce through artificial intelligence (AI)-powered predictive models. Methods: We employed supervised machine learning (ML) regression model to predict trends in the landscape of complex vascular and endovascular surgery over the next 22 years, utilising data from a high-volume public and private tertiary referral vascular centre spanning two decades (2002 to 2023) in the West of Ireland. Results: We conducted 1,653 aortic interventions, 1,185 carotid interventions, and 3,069 peripheral vascular interventions, with conversion rates from referral to surgery of 5%, 7.4%, and 9.8%, respectively. The private sector experienced a dramatic 73-fold increase in abdominal aortic aneurysm interventions, contrasted with a modest 1.25-fold increase in the public sector. Our model predicts a shortage of vascular surgeons, with the workforce potentially meeting demand by 2050. By 2030, each surgeon would need to increase yearly wRVU production by 22%-31% and by 2040 by 8%-11% to accommodate the workload. Conclusions: Our model predicts a shortage of the vascular surgery workforce over the next two decades. We can speculate that addressing future needs in vascular surgery requires either training more specialists or increasing the efficiency and wRUV through strategic planning and integration of AI/ML systems to ensure adequate compensation and the sustainability of the workforce. By focusing on these areas, we can navigate the evolving landscape of vascular surgery and continue providing high-quality patient care.

Indexed as

machine learningpredictive modelspublic-private hospitalstrend analysisvascular surgery

Identifiers

PMID41583839
PMCPMC12823813

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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.