Evidence map›Paper›PMID 41062957›Full record

ArticleBMC cardiovascular disorders2025

The value of cardiac CT based inflammatory risk assessment in predicting cardiovascular events: a case report.

Michail C Mavrogiannis, Nerea Sanfeliu Garces, Lucas Costa, Ahmed Alsinbili, Attila Kardos

Abstract readCase Reports
In one paragraph

Article in BMC cardiovascular disorders, 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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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Michail C MavrogiannisCardiovascular Medicine Division, Radcliffe Department of Medicine, University of Oxford, Oxford, UK. michail.mavrogiannis@wolfson.ox.ac.uk.
Nerea Sanfeliu GarcesDepartment of Cardiology, Translational Cardiovascular Research Group, Milton Keynes University Hospital NHS Foundation Trust, Milton Keynes, UK.
Lucas CostaDepartment of Cardiology, Translational Cardiovascular Research Group, Milton Keynes University Hospital NHS Foundation Trust, Milton Keynes, UK.
Ahmed AlsinbiliDepartment of Cardiology, Translational Cardiovascular Research Group, Milton Keynes University Hospital NHS Foundation Trust, Milton Keynes, UK.
Attila KardosDepartment of Cardiology, Translational Cardiovascular Research Group, Milton Keynes University Hospital NHS Foundation Trust, Milton Keynes, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundVascular inflammation plays a critical role in the development of coronary artery disease (CAD). Measurement of coronary inflammation from coronary computed tomography angiography (CCTA) using the perivascular fat attenuation index (FAI) Score could provide unique prognostic information and guide the clinical management of patients. In this context, we also refer to an artificial intelligence-based risk prediction tool (AI-Risk algorithm), which integrates FAI Score with clinical risk factors and plaque burden to estimate the long-term probability of a fatal cardiac event. CASE PRESENTATION: A 69-year-old male presented with symptoms of new onset angina. Past medical history included coronary artery bypass grafting (CABG) in 2001. Initial evaluation with CCTA showed patent arterial graft to left anterior descending (LAD) artery and two occluded venous grafts to obtuse marginal and diagonal branches, respectively, were identified. The non-grafted right coronary artery (RCA) was non-obstructive with moderate mid-vessel stenosis and the patient was discharged on optimal medical therapy. However, the patient was intolerant to statin. Eight years later, the patient was admitted to the hospital with a non-ST segment elevation myocardial infarction (NSTEMI) and the invasive coronary angiography showed occlusion of the non-grafted RCA. After few months of guidelines directed medical therapy, the patient developed progressive heart failure due to ischaemic cardiomyopathy and mitral regurgitation that led to his death. Retrospective perivascular FAI measurement of the non-grafted RCA captured the significantly elevated residual inflammatory risk.

conclusionsThe utilization of perivascular FAI Score and AI-Risk algorithm to capture inflammatory risk and predict future events beyond the current clinical risk stratification and CCTA interpretation, especially in the absence of obstructive CAD, could offer an important adjunct to current strategies in preventive cardiology, pending further validation. In this case report, our patient's management plan could have been adjusted had these technologies been available during initial evaluation, and the high inflammatory burden of the non-grafted RCA was timely captured.

Indexed as

Computed Tomography AngiographyCoronary AngiographyCoronary Artery DiseaseDecision Support TechniquesInflammationNon-ST Elevated Myocardial InfarctionAgedHumansMalePredictive Value of TestsRisk AssessmentRisk FactorsArtificial intelligenceComputed tomographyCoronary artery diseaseInflammation

Identifiers

PMID41062957
PMCPMC12505771

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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.