Evidence map›Paper›PMID 36647000›Full record

ArticleBMC cardiovascular disorders2023

Automated 4D flow cardiac MRI pipeline to derive peak mitral inflow diastolic velocities using short-axis cine stack: two centre validation study against echocardiographic pulse-wave doppler.

Hosamadin Assadi, Rui Li, Ciaran Grafton-Clarke, Bhalraam Uthayachandran, Samer Alabed, Ahmed Maiter, Gareth Archer, Peter P Swoboda, Chris Sawh, Alisdair Ryding and 8 more

Open access · goldAbstract read
In one paragraph

Article in BMC cardiovascular disorders, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
2.1field-weighted citation impact, top 14% 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

7 citing papers in PubMed, 9 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

18 authors at 6 institutions in 3 countries.

Hosamadin AssadiNorwich Medical School, University of East Anglia, Norwich Research Park, Norwich, NR4 7UQ, UK.
Rui LiNorwich Medical School, University of East Anglia, Norwich Research Park, Norwich, NR4 7UQ, UK.
Ciaran Grafton-ClarkeNorwich Medical School, University of East Anglia, Norwich Research Park, Norwich, NR4 7UQ, UK.
Bhalraam UthayachandranNorwich Medical School, University of East Anglia, Norwich Research Park, Norwich, NR4 7UQ, UK.
Samer AlabedDepartment of Infection, Immunity and Cardiovascular Disease, University of Sheffield Medical School and Sheffield Teaching Hospitals NHS Trust, Sheffield, UK.
Ahmed MaiterDepartment of Infection, Immunity and Cardiovascular Disease, University of Sheffield Medical School and Sheffield Teaching Hospitals NHS Trust, Sheffield, UK.
Gareth ArcherDepartment of Infection, Immunity and Cardiovascular Disease, University of Sheffield Medical School and Sheffield Teaching Hospitals NHS Trust, Sheffield, UK.
Peter P SwobodaLeeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK.
Chris SawhNorfolk and Norwich University Hospitals NHS Foundation Trust, Norfolk, UK.
Alisdair RydingNorfolk and Norwich University Hospitals NHS Foundation Trust, Norfolk, UK.
Faye NelthorpeNorfolk and Norwich University Hospitals NHS Foundation Trust, Norfolk, UK.
Bahman KasmaiNorfolk and Norwich University Hospitals NHS Foundation Trust, Norfolk, UK.
Fabrizio RicciDepartment of Neuroscience, Imaging and Clinical Sciences, "G.d'Annunzio" University of Chieti-Pescara, Chieti, Italy.
Rob J van der GeestDepartment of Radiology, Division of Image Processing, Leiden University Medical Center, Leiden, The Netherlands.
Marcus FlatherNorwich Medical School, University of East Anglia, Norwich Research Park, Norwich, NR4 7UQ, UK.
Vassilios S VassiliouNorwich Medical School, University of East Anglia, Norwich Research Park, Norwich, NR4 7UQ, UK.
Andrew J SwiftDepartment of Infection, Immunity and Cardiovascular Disease, University of Sheffield Medical School and Sheffield Teaching Hospitals NHS Trust, Sheffield, UK.
Pankaj GargNorwich Medical School, University of East Anglia, Norwich Research Park, Norwich, NR4 7UQ, UK. p.garg@uea.ac.uk.ORCID 0000-0002-5483-169X
University of East Anglia · GBNorfolk and Norwich University Hospitals NHS Foundation Trust · GBSheffield Teaching Hospitals NHS Foundation Trust · GBLeiden University Medical Center · NLUniversity of Chieti-Pescara · ITUniversity of Leeds · GB

Funding

Wellcome Trust 205188/Z/16/ZWellcome Trust 220703/Z/20/Z
6 · The paper itself

Abstract

backgroundMeasurement of peak velocities is important in the evaluation of heart failure. This study compared the performance of automated 4D flow cardiac MRI (CMR) with traditional transthoracic Doppler echocardiography (TTE) for the measurement of mitral inflow peak diastolic velocities.

methodsPatients with Doppler echocardiography and 4D flow cardiac magnetic resonance data were included retrospectively. An established automated technique was used to segment the left ventricular transvalvular flow using short-axis cine stack of images. Peak mitral E-wave and peak mitral A-wave velocities were automatically derived using in-plane velocity maps of transvalvular flow. Additionally, we checked the agreement between peak mitral E-wave velocity derived by 4D flow CMR and Doppler echocardiography in patients with sinus rhythm and atrial fibrillation (AF) separately.

resultsForty-eight patients were included (median age 69 years, IQR 63 to 76; 46% female). Data were split into three groups according to heart rhythm. The median peak E-wave mitral inflow velocity by automated 4D flow CMR was comparable with Doppler echocardiography in all patients (0.90 ± 0.43 m/s vs 0.94 ± 0.48 m/s, P = 0.132), sinus rhythm-only group (0.88 ± 0.35 m/s vs 0.86 ± 0.38 m/s, P = 0.54) and in AF-only group (1.33 ± 0.56 m/s vs 1.18 ± 0.47 m/s, P = 0.06). Peak A-wave mitral inflow velocity results had no significant difference between Doppler TTE and automated 4D flow CMR (0.81 ± 0.44 m/s vs 0.81 ± 0.53 m/s, P = 0.09) in all patients and sinus rhythm-only groups. Automated 4D flow CMR showed a significant correlation with TTE for measurement of peak E-wave in all patients group (r = 0.73, P < 0.001) and peak A-wave velocities (r = 0.88, P < 0.001). Moreover, there was a significant correlation between automated 4D flow CMR and TTE for peak-E wave velocity in sinus rhythm-only patients (r = 0.68, P < 0.001) and AF-only patients (r = 0.81, P = 0.014). Excellent intra-and inter-observer variability was demonstrated for both parameters.

conclusionAutomated dynamic peak mitral inflow diastolic velocity tracing using 4D flow CMR is comparable to Doppler echocardiography and has excellent repeatability for clinical use. However, 4D flow CMR can potentially underestimate peak velocity in patients with AF.

Indexed as

Atrial FibrillationMitral ValveAgedBlood Flow VelocityEchocardiographyEchocardiography, DopplerFemaleHumansMagnetic Resonance ImagingMaleRetrospective Studies4D flow CMRArtificial intelligenceDoppler echocardiographyMitral valvePeak velocity

Identifiers

PMID36647000
PMCPMC9843884
OpenAlexW4316661431

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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.