Evidence map›Paper›PMID 35359166›Full record

ArticleEuropean radiology2022

Impact of machine-learning-based coronary computed tomography angiography-derived fractional flow reserve on decision-making in patients with severe aortic stenosis undergoing transcatheter aortic valve replacement.

Verena Brandt, U Joseph Schoepf, Gilberto J Aquino, Raffi Bekeredjian, Akos Varga-Szemes, Tilman Emrich, Richard R Bayer, Florian Schwarz, Thomas J Kroencke, Christian Tesche and 1 more

Abstract read
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In one paragraph

Article in European radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed, 2 pooled it
4.8field-weighted citation impact, top 4% of its field
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

25 citing papers in PubMed, 2 syntheses or guidelines pooled it, 30 citations in OpenAlex.

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  10. Harnessing Artificial Intelligence for Innovation in Interventional Cardiovascular Care.Journal of the Society for Cardiovascular Angiography & Interventions · 2025
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  19. Fractional Flow Reserve-Based Patient Risk Classification.Diagnostics (Basel, Switzerland) · 2023
    Article
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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

11 authors at 3 institutions in 2 countries.

Verena BrandtDivision of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, 5 Courtenay Drive, Charleston, SC, 29425-2260, USA.
U Joseph SchoepfDivision of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, 5 Courtenay Drive, Charleston, SC, 29425-2260, USA. schoepf@musc.edu.ORCID http://orcid.org/0000-0002-6164-5641
Gilberto J AquinoDivision of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, 5 Courtenay Drive, Charleston, SC, 29425-2260, USA.
Raffi BekeredjianDepartment of Cardiology and Angiology, Robert-Bosch-Hospital, Stuttgart, Germany.
Akos Varga-SzemesDivision of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, 5 Courtenay Drive, Charleston, SC, 29425-2260, USA.
Tilman EmrichDivision of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, 5 Courtenay Drive, Charleston, SC, 29425-2260, USA.
Richard R BayerDivision of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, 5 Courtenay Drive, Charleston, SC, 29425-2260, USA.
Florian SchwarzDepartment of Diagnostic and Interventional Radiology, University Hospital Augsburg, Augsburg, Germany.
Thomas J KroenckeDepartment of Diagnostic and Interventional Radiology, University Hospital Augsburg, Augsburg, Germany.
Christian TescheDivision of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, 5 Courtenay Drive, Charleston, SC, 29425-2260, USA.
Josua A DeckerDivision of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, 5 Courtenay Drive, Charleston, SC, 29425-2260, USA.
Medical University of South Carolina · USUniversity Hospital Augsburg · DERobert Bosch Hospital · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo evaluate feasibility and diagnostic performance of coronary CT angiography (CCTA)-derived fractional flow reserve (CT-FFR) for detection of significant coronary artery disease (CAD) and decision-making in patients with severe aortic stenosis (AS) undergoing transcatheter aortic valve replacement (TAVR) to potentially avoid additional pre-TAVR invasive coronary angiography (ICA).

methodsConsecutive patients with severe AS (n = 95, 78.6 ± 8.8 years, 53% female) undergoing pre-procedural TAVR-CT followed by ICA with quantitative coronary angiography were retrospectively analyzed. CCTA datasets were evaluated using CAD Reporting and Data System (CAD-RADS) classification. CT-FFR measurements were computed using an on-site machine-learning algorithm. A combined algorithm was developed for decision-making to determine if ICA is needed based on pre-TAVR CCTA: [1] all patients with CAD-RADS ≥ 4 are referred for ICA; [2] patients with CAD-RADS 2 and 3 are evaluated utilizing CT-FFR and sent to ICA if CT-FFR ≤ 0.80; [3] patients with CAD-RADS < 2 or CAD-RADS 2-3 and normal CT-FFR are not referred for ICA.

resultsTwelve patients (13%) had significant CAD (≥ 70% stenosis) on ICA and were treated with PCI. Twenty-eight patients (30%) showed CT-FFR ≤ 0.80 and 24 (86%) of those were reported to have a maximum stenosis ≥ 50% during ICA. Using the proposed algorithm, significant CAD could be identified with a sensitivity, specificity, and positive and negative predictive value of 100%, 78%, 40%, and 100%, respectively, potentially decreasing the number of necessary ICAs by 65 (68%).

conclusionCombination of CT-FFR and CAD-RADS is able to identify significant CAD pre-TAVR and bears potential to significantly reduce the number of needed ICAs. KEY POINTS: • Coronary CT angiography-derived fractional flow reserve (CT-FFR) using machine learning together with the CAD Reporting and Data System (CAD-RADS) classification safely identifies significant coronary artery disease based on quantitative coronary angiography in patients prior to transcatheter aortic valve replacement. • The combination of CT-FFR and CAD-RADS enables decision-making and bears the potential to significantly reduce the number of needed invasive coronary angiographies.

Indexed as

Aortic Valve StenosisCoronary Artery DiseaseCoronary StenosisFractional Flow Reserve, MyocardialPercutaneous Coronary InterventionTranscatheter Aortic Valve ReplacementComputed Tomography AngiographyConstriction, PathologicCoronary AngiographyFemaleHumansMachine LearningMalePredictive Value of TestsRetrospective StudiesTomography, X-Ray ComputedAngiographyCoronary artery diseaseFractional flow reserveMultidetector computed tomographyTranscatheter aortic valve replacement

Identifiers

PMID35359166
OpenAlexW4220652022

What OpenQuestion holds

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Read underepoch 390

Registered trials

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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.