Evidence map›Paper›PMID 35729425›Full record

ArticleEuropean radiology2022

Diagnostic accuracy of coronary computed tomography angiography-derived fractional flow reserve (CT-FFR) in patients before liver transplantation using CT-FFR machine learning algorithm.

Maximilian Schuessler, Fuat Saner, Fadi Al-Rashid, Thomas Schlosser

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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 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
3.7field-weighted citation impact, top 6% 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

8 citing papers in PubMed, 19 citations in OpenAlex.

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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

4 authors at 2 institutions in 1 country.

Maximilian SchuesslerInstitute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany. maximilian.schuessler@uk-essen.de.ORCID http://orcid.org/0000-0002-8416-3960
Fuat SanerDepartment of General-, Visceral- and Transplantation Surgery, University Hospital Essen, Essen, Germany.ORCID http://orcid.org/0000-0002-9157-4573
Fadi Al-RashidDepartment of Cardiology and Vascular Medicine, West German Heart and Vascular Center, University Hospital Essen, Essen, Germany.ORCID http://orcid.org/0000-0001-5229-9612
Thomas SchlosserInstitute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany.ORCID http://orcid.org/0000-0001-5212-3262
Essen University Hospital · DEWest German Heart and Vascular Center Essen · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesLiver transplantation (LT) is associated with high stress on the cardiovascular system. Ruling out coronary artery disease (CAD) is an important part of evaluation for LT. The aim of our study was to assess whether CT-derived fractional flow reserve (CT-FFR) allows for differentiation of hemodynamically significant and non-significant coronary stenosis in patients evaluated for LT.

methodsIn total, 201 patients undergoing LT evaluation were included in the study. The patients received coronary computed tomography angiography (CCTA) to rule out CAD and invasive coronary angiography (ICA) to further evaluate coronary lesions found in CCTA if a significant (≥ 50 % on CCTA) stenosis was suspected. CT-FFR was computed from CCTA datasets using a machine learning-based algorithm and compared to ICA as a standard of reference. Coronary lesions with CT-FFR ≤ 0.80 were defined as hemodynamically significant.

resultsIn 127 of 201 patients (63%), an obstructive CAD was ruled out by CCTA. In the remaining 74 patients (37%), at least one significant stenosis was suspected in CCTA. Compared to ICA, sensitivity, specificity, PPV, and NPV of the CT-FFR measurements were 71% (49-92%), 90% (82-98%), 67% (45-88%), and 91% (84-99%), respectively. The diagnostic accuracy was 85% (85-86%). In 69% of cases (52 of 75 lesions), additional analysis by CT-FFR correctly excluded the hemodynamic significance of the stenosis.

conclusionsMachine learning-based CT-FFR seems to be a very promising noninvasive approach for exclusion of hemodynamic significant coronary stenoses in patients undergoing evaluation for LT and could help to reduce the rate of invasive coronary angiography in this high-risk population. KEY POINTS: • Machine learning-based computed tomography-derived fractional flow reserve (CT-FFR) seems to be a very promising noninvasive approach for exclusion of hemodynamic significance of coronary stenoses in patients undergoing evaluation for liver transplantation and could help to reduce the rate of invasive coronary angiography in this high-risk population.

Indexed as

Coronary Artery DiseaseCoronary StenosisFractional Flow Reserve, MyocardialLiver TransplantationComputed Tomography AngiographyConstriction, PathologicCoronary AngiographyHumansMachine LearningPredictive Value of TestsROC CurveTomography, X-Ray ComputedComputed tomographyCoronary angiographyFractional flow reserveLiver transplantationMachine learning

Identifiers

PMID35729425
PMCPMC9705488
OpenAlexW4283329616

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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.