Evidence map›Paper›PMID 38188259›Full record

ArticleFrontiers in cardiovascular medicine2023

Interrater variability of ML-based CT-FFR during TAVR-planning: influence of image quality and coronary artery calcifications.

Robin F Gohmann, Adrian Schug, Konrad Pawelka, Patrick Seitz, Nicolas Majunke, Hamza El Hadi, Linda Heiser, Katharina Renatus, Steffen Desch, Sergey Leontyev and 11 more

Abstract read
In one paragraph

Article in Frontiers in cardiovascular medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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

2 citing papers in PubMed.

  1. 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

21 authors.

Robin F Gohmann *Department of Diagnostic and Interventional Radiology, Heart Center Leipzig, Leipzig, Germany.
Adrian Schug *Department of Diagnostic and Interventional Radiology, Heart Center Leipzig, Leipzig, Germany.
Konrad PawelkaDepartment of Diagnostic and Interventional Radiology, Heart Center Leipzig, Leipzig, Germany.
Patrick SeitzDepartment of Diagnostic and Interventional Radiology, Heart Center Leipzig, Leipzig, Germany.
Nicolas MajunkeDepartment of Cardiology, Heart Center Leipzig, University of Leipzig, Leipzig, Germany.
Hamza El HadiDepartment of Cardiology, Heart Center Leipzig, University of Leipzig, Leipzig, Germany.
Linda HeiserDepartment of Diagnostic and Interventional Radiology, Heart Center Leipzig, Leipzig, Germany.
Katharina RenatusDepartment of Diagnostic and Interventional Radiology, Heart Center Leipzig, Leipzig, Germany.
Steffen DeschDepartment of Cardiology, Heart Center Leipzig, University of Leipzig, Leipzig, Germany.
Sergey LeontyevDepartment of Cardiac Surgery, Heart Center Leipzig, University of Leipzig, Leipzig, Germany.
Thilo NoackDepartment of Cardiac Surgery, Heart Center Leipzig, University of Leipzig, Leipzig, Germany.
Philipp KieferDepartment of Cardiac Surgery, Heart Center Leipzig, University of Leipzig, Leipzig, Germany.
Christian KrieghoffMedical Faculty, University of Leipzig, Leipzig, Germany.
Christian LückeMedical Faculty, University of Leipzig, Leipzig, Germany.
Sebastian EbelDepartment of Diagnostic and Interventional Radiology, Heart Center Leipzig, Leipzig, Germany.
Michael A BorgerDepartment of Cardiac Surgery, Heart Center Leipzig, University of Leipzig, Leipzig, Germany.
Holger ThieleDepartment of Cardiology, Heart Center Leipzig, University of Leipzig, Leipzig, Germany.
Christoph PankninSiemens Healthcare GmbH, Erlangen, Germany.
Mohamed Abdel-WahabDepartment of Cardiology, Heart Center Leipzig, University of Leipzig, Leipzig, Germany.
Matthias HornInstitute for Medical Informatics, Statistics and Epidemiology (IMISE), University of Leipzig, Leipzig, Germany.
Matthias GutberletDepartment of Diagnostic and Interventional Radiology, Heart Center Leipzig, Leipzig, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To compare machine learning (ML)-based CT-derived fractional flow reserve (CT-FFR) in patients before transcatheter aortic valve replacement (TAVR) by observers with differing training and to assess influencing factors. Background: Coronary computed tomography angiography (cCTA) can effectively exclude CAD, e.g. prior to TAVR, but remains limited by its specificity. CT-FFR may mitigate this limitation also in patients prior to TAVR. While a high reliability of CT-FFR is presumed, little is known about the reproducibility of ML-based CT-FFR. Methods: Consecutive patients with obstructive CAD on cCTA were evaluated with ML-based CT-FFR by two observers. Categorization into hemodynamically significant CAD was compared against invasive coronary angiography. The influence of image quality and coronary artery calcium score (CAC) was examined. Results: CT-FFR was successfully performed on 214/272 examinations by both observers. The median difference of CT-FFR between both observers was -0.05(-0.12-0.02) ( Conclusion: Differences between CT-FFR values increased in values below the cut-off, having little clinical impact. Categorization into CAD differed in several patients, but ultimately only had a moderate influence on diagnostic accuracy. This was independent of image quality or CAC.

Indexed as

aortic stenosiscomputed tomography coronary angiographycomputed tomography fractional flow reservecoronary angiographycoronary artery diseasediagnostic accuracymachine learningtranscatheter aortic valve implantation

Identifiers

PMID38188259
PMCPMC10768187

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