MétaCan
Menu
Back to cohort

114 Detecting ischaemia in flow limiting multi-vessel disease – is 3d perfusion cmr where the money lies?

2017· article· en· W2737485096 on OpenAlexaff
Joy Shome, Mr Kerem.C Tezcan, Sohaib Nazir, Adriana Villa, David Snell, Kamran Baig, Simon Redwood, Brian Clapp, Antonis N. Pavlidis, Reza Razavi, Tevfik F. Ismail, Divaka Perera, Sebastian Kozerke, Sven Plien

Bibliographic record

VenueHeart · 2017
Typearticle
Languageen
FieldMedicine
TopicCardiac Imaging and Diagnostics
Canadian institutionsSt. Thomas Hospital
Fundersnot available
KeywordsMedicineFractional flow reserveCardiologyCoronary artery diseasePerfusionInternal medicinePerfusion scanningRadiologyStenosisCoronary circulationBlood flowCoronary angiographyMyocardial infarction

Abstract

fetched live from OpenAlex

Introduction Myocardial perfusion cardiovascular magnetic resonance (CMR) is a highly accurate non-invasive imaging modality in the diagnosis of coronary artery disease (CAD). High-resolution (hi-res) two-dimensional (2D) perfusion CMR can better detect sub-endocardial ischaemia although with the disadvantage of lesser myocardial coverage. Three-dimensional (3D) perfusion provides whole heart coverage but has a comparatively inferior resolution. The superiority of fractional flow reserve (FFR) over visual angiographic assessment in determining functional significance of a coronary stenosis is now well established. We studied the diagnostic agreement between hi-res 2D and 3D perfusion CMR in patients with significant multi-vessel flow limiting CAD as confirmed by FFR on a per patient and per vessel basis. Methods Patients with suspected stable CAD referred for invasive coronary angiography as part of their routine clinical care were prospectively recruited. Prior to revascularisation (if performed) all patients underwent both hi-res 2D adenosine vasodilator stress and 3D adenosine stress perfusion scans during the same sitting. Visually, coronary stenoses less than 50% were deemed not to be flow limiting, whereas those 80% or more were considered as flow limiting. For stenoses between 50%–80% FFR study was performed, with FFR of 0.8 or less considered functionally significant. Blinded, independent, qualitative visual analysis by two experienced readers was performed to confirm existence of true perfusion defects on both 2D and 3D perfusion CMR datasets. Vascular territories in relation to CMR perfusion defects were assigned as per AHA 16 segment classification. Results Prevalence of CAD was 62%. Of the 29 patients studied, 6 (21%) had single-vessel disease, 8 (27%) had two-vessel disease (2VD), 4 (14%) had three-vessel disease (3VD), and 11 (38%) had no significant CAD. In view of the small sample size qualitative analysis was undertaken to determine concordance between hi-res 2D and 3D perfusion CMR. Prevalence of perfusion defects relating to the three vascular territories have been detailed in Table 1. Conclusions Our study shows an excellent agreement for both modalities in detecting FFR positive CAD on a per patient basis. On a per vessel basis, between the two, agreement in the LAD territory appears to be best. Discrepancy appears to be most in the circumflex territory. 3D perfusion CMR appears to detect circumflex ischaemia more accurately possibly due to better coverage of the basal left ventricle. (Ref Fig 1 and Fig 2) The lateral wall is often the thinnest making it more difficult to detect perfusion abnormalities – better coverage with 3D may be beneficial here. However, in the circumflex vascular territory we also observe that, in the absence of flow limiting disease in the circumflex artery (comparatively with the LAD and RCA) perfusion defects appear to be more prevalent, reflecting a limitation of the AHA classification. Abstract 114 Figure 1 3D perfusion images showing perfusion defects in LAD (see blue arrows) and circumflex territory (see red arrows) Abstract 114 Table 1 Abstract 114 Figure 2 High resolution 2D perfusion images in the same patient showing perfusion defects in the LAD territory only (see blue arrows)

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)0.000
metaresearch head score (Gemma)0.003
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 score0.047
Threshold uncertainty score0.440

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.027
GPT teacher head0.306
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".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueHeartSame topicCardiac Imaging and DiagnosticsFrench-language works237,207