Evidence map›Paper›PMID 30324382›Full record

ArticleEuropean radiology2019

Noninvasive CT-based hemodynamic assessment of coronary lesions derived from fast computational analysis: a comparison against fractional flow reserve.

Panagiotis K Siogkas, Constantinos D Anagnostopoulos, Riccardo Liga, Themis P Exarchos, Antonis I Sakellarios, George Rigas, Arthur J H A Scholte, M I Papafaklis, Dimitra Loggitsi, Gualtiero Pelosi and 6 more

Abstract read
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In one paragraph

Article in European radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing 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

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

  1. Pooled it
  2. Review
  3. Coronary physiology in the cath lab: is "virtual FFR" ready for the prime time?Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2023
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  12. Cardiac CT perfusion and FFRCardiovascular diagnosis and therapy · 2020
    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

16 authors.

Panagiotis K SiogkasUnit of Medical Technology and Intelligent Information Systems, Dept. of Materials Science and Engineering, University of Ioannina, Ioannina, Greece.
Constantinos D AnagnostopoulosCenter for Experimental Surgery, Clinical and Translational Research, Biomedical Research Foundation, Academy of Athens, 4 Soranou Ephessiou St., 115 27, Athens, Greece. cdanagnostopoulos@bioacademy.gr.
Riccardo LigaCardio-Thoracic and Vascular Department, University Hospital of Pisa, Pisa, Italy.
Themis P ExarchosBiomedical Research Institute - FORTH, GR 45110 Ioannina, Ioannina, Greece.
Antonis I SakellariosBiomedical Research Institute - FORTH, GR 45110 Ioannina, Ioannina, Greece.
George RigasBiomedical Research Institute - FORTH, GR 45110 Ioannina, Ioannina, Greece.
Arthur J H A ScholteDepartment of Cardiology, Leiden University Medical Center, Leiden, The Netherlands.
M I PapafaklisMichaelideion Cardiac Center, Dept. of Cardiology in Medical School, University of Ioannina, 451 10, Ioannina, Greece.
Dimitra LoggitsiCT & MRI Department Hygeia-Mitera Hospitals, Athens, Greece.
Gualtiero PelosiFondazione Toscana G. Monasterio and CNR Institute of Clinical Physiology, Pisa, Italy.
Oberdan ParodiFondazione Toscana G. Monasterio and CNR Institute of Clinical Physiology, Pisa, Italy.
Teemu MaaniittyTurku PET Centre, University of Turku and Turku University Hospital, Turku, Finland.
Lampros K MichalisMichaelideion Cardiac Center, Dept. of Cardiology in Medical School, University of Ioannina, 451 10, Ioannina, Greece.
Juhani KnuutiTurku PET Centre, University of Turku and Turku University Hospital, Turku, Finland.
Danilo NegliaFondazione Toscana G. Monasterio and CNR Institute of Clinical Physiology, Pisa, Italy.
Dimitrios I FotiadisUnit of Medical Technology and Intelligent Information Systems, Dept. of Materials Science and Engineering, University of Ioannina, Ioannina, Greece.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesApplication of computational fluid dynamics (CFD) to three-dimensional CTCA datasets has been shown to provide accurate assessment of the hemodynamic significance of a coronary lesion. We aim to test the feasibility of calculating a novel CTCA-based virtual functional assessment index (vFAI) of coronary stenoses > 30% and ≤ 90% by using an automated in-house-developed software and to evaluate its efficacy as compared to the invasively measured fractional flow reserve (FFR). METHODS AND

resultsIn 63 patients with chest pain symptoms and intermediate (20-90%) pre-test likelihood of coronary artery disease undergoing CTCA and invasive coronary angiography with FFR measurement, vFAI calculations were performed after 3D reconstruction of the coronary vessels and flow simulations using the finite element method. A total of 74 vessels were analyzed. Mean CTCA processing time was 25(± 10) min. There was a strong correlation between vFAI and FFR, (R = 0.93, p < 0.001) and a very good agreement between the two parameters by the Bland-Altman method of analysis. The mean difference of measurements from the two methods was 0.03 (SD = 0.033), indicating a small systematic overestimation of the FFR by vFAI. Using a receiver-operating characteristic curve analysis, the optimal vFAI cutoff value for identifying an FFR threshold of ≤ 0.8 was ≤ 0.82 (95% CI 0.81 to 0.88).

conclusionsvFAI can be effectively derived from the application of computational fluid dynamics to three-dimensional CTCA datasets. In patients with coronary stenosis severity > 30% and ≤ 90%, vFAI performs well against FFR and may efficiently distinguish between hemodynamically significant from non-significant lesions. KEY POINTS: Virtual functional assessment index (vFAI) can be effectively derived from 3D CTCA datasets. In patients with coronary stenoses severity > 30% and ≤ 90%, vFAI performs well against FFR. vFAI may efficiently distinguish between functionally significant from non-significant lesions.

Indexed as

Imaging, Three-DimensionalAgedCoronary AngiographyCoronary Artery DiseaseCoronary VesselsFemaleFractional Flow Reserve, MyocardialHemodynamicsHumansMaleMiddle AgedROC CurveTomography, X-Ray ComputedComputed tomography angiographyCoronary artery diseaseMyocardial fractional flow reserve

Identifiers

PMID30324382

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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.