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Record W2981647783 · doi:10.1093/eurheartj/ehz746.0787

P6181The association of coronary lumen volume to left ventricle mass ratio with myocardial blood flow and fractional flow reserve

2019· article· en· W2981647783 on OpenAlexaff
Pepijn A. van Diemen, S P Schumacher, MJ Bom, R S Driessen, Henk Everaars, W J Stuijfzand, P. G. Raijmakers, Peter M. van de Ven, James K. Min, Jonathon Leipsic, Juhani Knuuti, P.R. Boellaard, Albert C. van Rossum, Ibrahim Danad, Paul Knaapen

Bibliographic record

VenueEuropean Heart Journal · 2019
Typearticle
Languageen
FieldMedicine
TopicCardiac Imaging and Diagnostics
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsFractional flow reserveMedicineCardiologyInterquartile rangeCoronary artery diseaseVentricleInternal medicineCoronary flow reserveLumen (anatomy)StenosisBlood flowPopulationCoronary arteriesArteryNuclear medicineCoronary angiographyMyocardial infarction

Abstract

fetched live from OpenAlex

Abstract Background A low coronary lumen volume to left ventricle mass ratio (V/M) derived from coronary computed tomography angiography (CCTA) has been proposed as a factor contributing to impaired myocardial blood flow (MBF) even in the absence of obstructive coronary artery disease (CAD). Objective To elucidate the association of V/M with non-invasively obtained MBF parameters by means of [15O]H2O positron emission tomography (PET), as well as its correlations with invasively measured fractional flow reserve (FFR), overall and specifically in vessel with non-obstructive CAD. Methods This is a substudy of the PACIFIC trial, in which 208 patients underwent CCTA, and [15O]H2O PET prior to invasive coronary angiography (ICA) in conjunction with 3 vessel FFR measurements. Patient specific V/M was calculated for 152 patients. Matched vessel specific hyperemic MBF (hMBF), coronary flow reserve (CFR), FFR, and patient specific V/M were available for 431 vessels. The median V/M (20.71 mm3/g) was used to divide the study population into a group with a low V/M (<20.71 mm3/g) and a high V/M (≥20.71 mm3/g). Non-obstructive CAD was defined as a ≤50% stenosis grade on ICA. Results Overall, a higher percentage of vessels with an abnormal hMBF (34% vs. 19%, p=0.009), lower FFR values (0.93 [interquartile range: 0.85–0.97] vs. 0.95 [0.89–0.98], p=0.016), and a higher number of positive FFR values (20% vs. 9%, p=0.004) were observed among vessels in the low V/M group. Furthermore, a weak correlation between V/M, global hMBF (R=0.179, p=0.027), and global CFR (R=0.163, p=0.045) as well as a weak significant association with vessel specific hMBF (p=0.027), and FFR (p<0.001) was observed (figure 1). V/M was not independently predictive of vessels specific MBF parameters or FFR. Among vessels with non-obstructive CAD (361 vessels), an abnormal hMBF tended to be more frequently observed in vessels with a low patient specific V/M (21% vs. 13%, p=0.056). Globally, there was no correlation between V/M and hMBF nor CFR. Patient specific V/M tended to be weakly associated with vessel specific hMBF (p=0.083) and was associated with FFR (p=0.027) (figure 1). Lastly, patient specific V/M tended to be independently predictive of FFR in this specific group. Conclusion Overall, vessels with an abnormal hMBF, and positive FFR measurements were more frequently observed in patients with a low V/M compared to those with a high V/M. Furthermore, V/M weakly correlated with global hMBF as well as with CFR and was associated with vessel specific hMBF and FFR. However, there was no correlation between V/M and global nor vessel specific blood flow parameters in the absence of obstructive CAD, notwithstanding a weak association of V/M with FFR within this group was noted.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.010
GPT teacher head0.244
Teacher spread0.234 · 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 source (direct Gemma or distilled Codex), 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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Citations1
Published2019
Admission routes1
Has abstractyes

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