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Record W4301585575 · doi:10.1093/ehjcr/ytac405

Improved quantification of mitral regurgitant volume after MitraClip implantation using four-dimensional flow cardiac magnetic resonance imaging with dynamic valve tracking

2022· article· en· W4301585575 on OpenAlexaboutno aff
Seiko Ide, Isamu Mizote, Yasushi Sakata

Bibliographic record

VenueEuropean Heart Journal - Case Reports · 2022
Typearticle
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMitraClipMagnetic resonance imagingMedicineMitral valveCardiac magnetic resonanceCardiologyCardiac magnetic resonance imagingMitral regurgitationInternal medicineRadiology

Abstract

fetched live from OpenAlex

A 78-year-old man was referred to our hospital for MitraClip implantation after treatment for decompensated heart failure. Transthoracic echocardiography (TTE) revealed severe mitral regurgitation (MR) with eccentric swirling jets reaching the posterior left atrium due to mitral valve prolapse (Panel A). However, TTE performed suboptimal quantification of MR due to the jet eccentricity; thus, cardiovascular magnetic resonance (CMR) was used instead. Standard CMR methods indirectly quantify MR by subtracting aortic forward flow from the left-ventricular stroke volume. Four-dimensional flow CMR with dynamic valve tracking (4D-DVT) quantifies MR directly with frame-by-frame correction of the annular valve plane motion. The preoperative MR volume was measured at 73 mL using 4D-DVT and at 84 mL using the standard method (Panel B, see Supplementary material online, Video S1, Video Still Image 1). At the 1 year follow up, MR volume quantification was hindered by metal artefacts and multiple jets from the two-clip implantation on TTE (Panel C). However, cine CMR demonstrated a significant reduction in the left-ventricle end-diastolic volume index from 137 to 43 mL/m2 (Panels D and E), and 4D-DVT showed that the MR volume was reduced to 24 mL (Panel F, see Supplementary material online, Video S2, Video Still Image 2). The standard CMR method showed an illogical MR volume reduction of −13 mL. The optimal region of interest for 4D-DVT was just above the annules to avoid artefacts from the clips. Serial 4D-DVT assessment is potentially more versatile and accurate than the standard CMR method or TTE to quantify MR, especially in patients with metal artefacts or eccentric jets. Panel A: Preoperative TTE showed mitral valve prolapse, which caused severe MR with eccentric swirling jets in the left atrium. Panel B: Eccentric MR jets were also observed in the left atrium (arrowheads) on 4D-DVT pathline imaging, from which a preoperative MR volume of 73 mL was derived. CMR data analysis was performed using cmr42 (Circle Cardiovascular Imaging, Canada). Panel C: Repeat TTE at 1 year showed mitral valves repaired by implantation of two clips. Colour Doppler imaging qualitatively revealed mild MR with multiple jets due to clips. Panel D: Cine CMR revealed left-ventricular dilatation (LVEDVi = 137 mL/m2) before implantation of clips with a preserved ejection fraction (LVEF = 54%). Panel E: Follow-up cine CMR indicated that cardiac morphology had markedly improved (LVEDVi = 43 mL/m2) 1 year after clip implantation. Panel F: Serial 4D-DVT imaging demonstrated a reduction in MR volume of 24 mL (arrow) 1 year after clip implantation. 4D-DVT pathline imaging showing eccentric swirling mitral regurgitation jets reaching the posterior left atrium due to mitral valve prolapse. Follow-up cardiovascular magnetic resonance at 1 year. Reduced mitral regurgitation volume was observed by 4D-DVT imaging, which allowed volumetric quantification above the mitral annulus even after MitraClip implantation. Supplementary material is available at European Heart Journal – Case Reports online. Consent: The study was approved by the local research ethics board and written informed consent was obtained from the parent. Funding: This work was supported in part by Japan Society for Promotion of Science (grant number 18K08106).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.054
Threshold uncertainty score0.859

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.021
GPT teacher head0.301
Teacher spread0.280 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2022
Admission routes1
Has abstractyes

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