Evidence map›Paper›PMID 39341792›Full record

ArticleEuropean heart journal. Quality of care & clinical outcomes2025

Cost-effectiveness of a novel AI technology to quantify coronary inflammation and cardiovascular risk in patients undergoing routine coronary computed tomography angiography.

Apostolos Tsiachristas, Kenneth Chan, Elizabeth Wahome, Ben Kearns, Parijat Patel, Maria Lyasheva, Nigar Syed, Sam Fry, Thomas Halborg, Henry West and 21 more

Abstract read
In one paragraph

Article in European heart journal. Quality of care & clinical outcomes, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 2 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

16 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
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  10. Article
  11. Using pericoronary fat attenuation to guide management after coronary interventions.EuroIntervention : journal of EuroPCR in collaboration with the Working Group on Interventional Cardiology of the European Society of Cardiology · 2025
    Article
  12. Article
  13. Cardiovascular imaging in 2024: review of current research and innovations.European heart journal. Imaging methods and practice · 2025
    Review
  14. Review
  15. Artificial intelligence for cardiology: from diagnosis to management.Therapeutic advances in cardiovascular disease
    Review
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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

31 authors.

Apostolos TsiachristasNuffield Department of Primary Care Health Sciences & Department of Psychiatry, University of Oxford, Oxford, OX2 6GG, UK.
Kenneth ChanAcute Multidisciplinary Imaging & Interventional Centre, British Heart Foundation (BHF) Centre of Research Excellence, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, Oxford NIHR Biomedical Research Centre, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford, OX3 9DU, UK.
Elizabeth WahomeAcute Multidisciplinary Imaging & Interventional Centre, British Heart Foundation (BHF) Centre of Research Excellence, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, Oxford NIHR Biomedical Research Centre, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford, OX3 9DU, UK.
Ben KearnsLumanity, Sheffield, S1 2GQ, UK.
Parijat PatelAcute Multidisciplinary Imaging & Interventional Centre, British Heart Foundation (BHF) Centre of Research Excellence, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, Oxford NIHR Biomedical Research Centre, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford, OX3 9DU, UK.
Maria LyashevaAcute Multidisciplinary Imaging & Interventional Centre, British Heart Foundation (BHF) Centre of Research Excellence, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, Oxford NIHR Biomedical Research Centre, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford, OX3 9DU, UK.
Nigar SyedCaristo Diagnostics, Oxford, OX2 0HP, UK.
Sam FryCaristo Diagnostics, Oxford, OX2 0HP, UK.
Thomas HalborgAcute Multidisciplinary Imaging & Interventional Centre, British Heart Foundation (BHF) Centre of Research Excellence, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, Oxford NIHR Biomedical Research Centre, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford, OX3 9DU, UK.
Henry WestAcute Multidisciplinary Imaging & Interventional Centre, British Heart Foundation (BHF) Centre of Research Excellence, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, Oxford NIHR Biomedical Research Centre, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford, OX3 9DU, UK.
Edward NicolDepartments of Cardiology and Radiology, Royal Brompton Hospital, London, SW3 6NP, UK.
David AdlamDepartment of Cardiovascular Sciences, University of Leicester, Leicester, LE1 7RH, UK.
Bhavik ModiDepartment of Cardiovascular Sciences, University of Leicester, Leicester, LE1 7RH, UK.
Attila KardosDepartment of Cardiology, Translational Cardiovascular Research Group, Milton Keynes University Hospital NHS Foundation Trust, Milton, MK6 5LD, UK.ORCID 0000-0002-0231-7605
John P GreenwoodLeeds Teaching Hospitals, Leeds, LS1 3EX, UK.
Nikant SabharwalAcute Multidisciplinary Imaging & Interventional Centre, British Heart Foundation (BHF) Centre of Research Excellence, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, Oxford NIHR Biomedical Research Centre, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford, OX3 9DU, UK.ORCID 0000-0002-1989-947X
Giovanni Luigi De MariaAcute Multidisciplinary Imaging & Interventional Centre, British Heart Foundation (BHF) Centre of Research Excellence, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, Oxford NIHR Biomedical Research Centre, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford, OX3 9DU, UK.
Shahzad MunirRoyal Wolverhampton NHS Trust, Wolverhampton, WV10 0QP, UK.
Elisa McAlindonRoyal Wolverhampton NHS Trust, Wolverhampton, WV10 0QP, UK.
Yogesh SohanCaristo Diagnostics, Oxford, OX2 0HP, UK.
Pete TomlinsCaristo Diagnostics, Oxford, OX2 0HP, UK.
Muhammad SiddiqueCaristo Diagnostics, Oxford, OX2 0HP, UK.
Cheerag ShirodariaAcute Multidisciplinary Imaging & Interventional Centre, British Heart Foundation (BHF) Centre of Research Excellence, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, Oxford NIHR Biomedical Research Centre, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford, OX3 9DU, UK.ORCID 0000-0001-6463-1838
Ron BlanksteinBrigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA.
Milind DesaiDepartment of Cardiovascular Medicine, Cleveland Clinic Heart Vascular and Thoracic Institute, Cleveland, OH, 44195, USA.ORCID 0000-0003-0010-9797
Stefan NeubauerAcute Multidisciplinary Imaging & Interventional Centre, British Heart Foundation (BHF) Centre of Research Excellence, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, Oxford NIHR Biomedical Research Centre, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford, OX3 9DU, UK.ORCID 0000-0001-9017-5645
Keith M ChannonAcute Multidisciplinary Imaging & Interventional Centre, British Heart Foundation (BHF) Centre of Research Excellence, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, Oxford NIHR Biomedical Research Centre, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford, OX3 9DU, UK.
John DeanfieldInstitute of Cardiovascular Science, University College London, London, WC1E 6DD, UK.
Ron AkehurstLumanity, Sheffield, S1 2GQ, UK.
Charalambos AntoniadesAcute Multidisciplinary Imaging & Interventional Centre, British Heart Foundation (BHF) Centre of Research Excellence, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, Oxford NIHR Biomedical Research Centre, University of Oxford, John Radcliffe Hospital, Headley Way, Oxford, OX3 9DU, UK.ORCID 0000-0002-6983-5423
ORFAN Consortium

Funding

British Heart Foundation CH/16/1/32013British Heart Foundation CH/F/21/90009British Heart Foundation RG/F/21/110040British Heart Foundation TG/19/2/34831British Heart Foundation Oxford Centre of Research Excellence RG/18/3/34214EU Research and Innovation Action MAESTRIA 965286Innovate UK 104472National Consortium of Intelligent Medical ImagingNHS-AI AI_AWARD02013NHS-AI AI_AWARD02443NIHR Oxford Biomedical Research CentreWellcome Trust 104688
6 · The paper itself

Abstract

aimsCoronary computed tomography angiography (CCTA) is a first-line investigation for chest pain in patients with suspected obstructive coronary artery disease (CAD). However, many acute cardiac events occur in the absence of obstructive CAD. We assessed the lifetime cost-effectiveness of integrating a novel artificial intelligence-enhanced image analysis algorithm (AI-Risk) that stratifies the risk of cardiac events by quantifying coronary inflammation, combined with the extent of coronary artery plaque and clinical risk factors, by analysing images from routine CCTA. METHODS AND

resultsA hybrid decision-tree with population cohort Markov model was developed from 3393 consecutive patients who underwent routine CCTA for suspected obstructive CAD and followed up for major adverse cardiac events over a median (interquartile range) of 7.7(6.4-9.1) years. In a prospective real-world evaluation survey of 744 consecutive patients undergoing CCTA for chest pain investigation, the availability of AI-Risk assessment led to treatment initiation or intensification in 45% of patients. In a further prospective study of 1214 consecutive patients with extensive guidelines recommended cardiovascular risk profiling, AI-Risk stratification led to treatment initiation or intensification in 39% of patients beyond the current clinical guideline recommendations. Treatment guided by AI-Risk modelled over a lifetime horizon could lead to fewer cardiac events (relative reductions of 11%, 4%, 4%, and 12% for myocardial infarction, ischaemic stroke, heart failure, and cardiac death, respectively). Implementing AI-Risk Classification in routine interpretation of CCTA is highly likely to be cost-effective (incremental cost-effectiveness ratio £1371-3244), both in scenarios of current guideline compliance, or when applied only to patients without obstructive CAD.

conclusionsCompared with standard care, the addition of AI-Risk assessment in routine CCTA interpretation is cost-effective, by refining risk-guided medical management.

Indexed as

Artificial IntelligenceComputed Tomography AngiographyCoronary AngiographyCoronary Artery DiseaseInflammationAgedCost-Benefit AnalysisFemaleHumansMaleMiddle AgedProspective StudiesRisk AssessmentCoronary artery diseaseCoronary CT angiographyCost-effectiveness analysisInflammation

Identifiers

PMID39341792
PMCPMC12187117

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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.