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Record W4384568096 · doi:10.1136/heartjnl-2023-bcs.41

41 Insights from positron emission tomography into the mechanism of the coronary sinus reducer

2023· article· en· W4384568096 on OpenAlexaboutno aff
Kevin Cheng, Ranil de Silva, Georgia Keramida

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicCardiac Imaging and Diagnostics
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineCoronary sinusCardiologyPositron emission tomographyInternal medicineCoronary artery diseasePerfusionNuclear medicineMyocardial perfusion imagingAnginaMyocardial infarction

Abstract

fetched live from OpenAlex

Aims Despite evidence suggesting angina improvement after Coronary Sinus Reducer (CSR) implantation, the underlying mechanism remains to be confirmed. In a pilot study, we sought to assess changes in quantitative myocardial perfusion after CSR implantation in patients with refractory angina secondary to advanced coronary artery disease (CAD) using Rubidium-82 (Rb-82) Positron Emission Tomography (PET). Methods Data was collected prospectively for patients undergoing clinically indicated CSR implantation. Rb-82 PET was performed at rest and stress at baseline and median 6 months follow-up. QPET software was used for automatic segmentation into a 17 segment American Heart Association model and quantification of rest and stress myocardial blood flow (MBF, ml/min/g) and myocardial perfusion reserve (MPR). Results Five patients (3 male; 2 female) with mean age 69±16 years were recruited. Median Canadian Cardiovascular Society class was 3. Median number of anti-anginal medications was 3 and was unchanged during the follow-up period. 85 myocardial segments were analysed. Rest and stress MBF were corrected for rate-pressure product. Wilcoxon signed-rank tests were used to compare paired global and segmental perfusion values. A linear mixed-effects model with random slopes and intercepts was used to assess the relationship between baseline segmental MPR and associated change in MPR (delta MPR) after CSR implantation. Globally, there was no significant change in rest (P>0.99), stress MBF (P=0.63) or MPR (P=0.81) from baseline to follow-up. At a segmental level, there was no significant difference in MPR (P>0.57). However, the magnitude of change in MPR was related to the degree of baseline ischaemia (Figure 1). More ischaemic segments (baseline MPR<1.67) saw a greater increase in MPR after CSR whereas segments with higher baseline MPR values (MPR≥1.67) experienced a decrease (P=0.03, conditional R2 = 0.675). The median change in MPR in segments with a baseline MPR<1.67 (n=59) was 8.9% (delta MPR: +0.08; P=0.001) driven by a greater increase in stress MBF (15.6%, IQR: -2.1-42.2) compared to rest MBF (8.2%, IQR: -8.3-23.5; median of differences: 9.1%, P=0.0006) (Figure 2). In segments with baseline MPR≥1.67 (n=26) a 13.8% decrease in MPR was observed (delta MPR: -0.27; P=0.0003) driven by a greater decrease in stress MBF (-23.8%, IQR: -32.7-76.2) compared to rest MBF (-0.4%, IQR -24.33-72.9; median of differences: -11.5%, P= 0.0008). These changes in MPR remained significant when stratified by segments of the left (MPR<1.67: +0.08 [P=0.01]; MPR≥1.67: -0.20 [P=0.004]) and right (MPR<1.67: +0.11 [P=0.03]; MPR≥1.67: -0.27 [P=0.02]) coronary artery distributions. Conclusions To our knowledge, this is the first study showing changes in quantitative myocardial perfusion by PET after CSR implantation. Our segmental analysis demonstrates a relationship between baseline MPR and change in MPR after CSR implantation. In segments with MPR <1.67, the significant increase in MPR associated with CSR implantation was driven predominantly by greater increases in stress than rest MBF. By contrast, in segments with MPR≥1.67, follow-up MPR fell, driven by a reduction in stress MBF. Global values were unchanged. These quantitative preliminary data suggest redistribution of perfusion during stress as an effect of CSR implantation in patients with refractory angina secondary to advanced CAD. These pilot data require confirmation in adequately powered, double-blinded, randomised, sham-controlled studies. Conflict of Interest Nil

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
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)0.0030.000

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 head0.009
GPT teacher head0.247
Teacher spread0.238 · 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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Citations0
Published2023
Admission routes1
Has abstractyes

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