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Pressure drop-derived index associated with plaque vulnerability of coronary atherosclerosis

2023· article· en· W4388600081 on OpenAlexaff
Jae-Wook Chung, Sun Young Yang, Krista Lesina, J H Doh, Andrejs Ērglis, Jonathon Leipsic, Eun Ju Chun, Goh Eun Choi, Michiel Schaap, Christopher K. Zarins, Charles A. Taylor, William F. Fearon, Jagat Narula, Bon‐Kwon Koo

Bibliographic record

VenueEuropean Heart Journal · 2023
Typearticle
Languageen
FieldMedicine
TopicCoronary Interventions and Diagnostics
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineFractional flow reserveCardiologyHemodynamicsLesionAtheromaInternal medicineVulnerable plaqueRadiologyCoronary artery diseaseNuclear medicinePathologyMyocardial infarctionCoronary angiography

Abstract

fetched live from OpenAlex

Abstract Background The clinical importance of physiological distribution of coronary atherosclerosis is emerging. However, this concept has limited clinical application because of complex formulas and technical difficulties in measurement. Purpose We aimed to evaluate the utility of a simple physiological metric using pressure drop across the lesion, through the analysis of association with hemodynamic and plaque characteristics. Methods The analysis was conducted on a total 246 lesions in 136 vessels from DISCOVER-FLOW study, which was the first-in-human, prospective, international, multi-center study demonstrating the diagnostic accuracy of coronary computational tomographic angiography (CCTA)-derived fractional flow reserve (FFRCT) for invasive FFR. All vessel- and lesion-level hemodynamic as well as plaque parameters were analyzed by independent core laboratories using CCTA and computational fluid dynamic techniques. Functional significance (FS) was defined as CTA-derived FFR (FFRCT) ≤ 0.80, and physiological focal disease (PFD) was defined as change in FFRCT across the lesion (ΔFFRCT) ≥ 0.06 and FFR drop across the lesion ≥ 0.0015/mm. The lesions were categorized into four groups based on the existence of FS and/or PFD and denoted as follows: FS(-) PFD(-) for group A, FS(-) PFD(+) for group B, FS(+) PFD(-) for group C, FS(+) PFD(+) for group D; Statistics of hemodynamic and plaque characteristics among the four groups were compared. Results 48.8% and 54.5% of total lesions were designated as FS and PFD, respectively. Vessel-level characteristics such as total plaque volume (TPV) of vessel and percent atheroma volume (PAV) of vessel were positively associated with FS (all p<0.001 for A vs C and B vs D). In the lesion level, wall shear stress (WSS) was higher when PFD was present, regardless of FS (136.5 vs 260.8, p<0.001 with A vs B; 125.6 vs 299.9, p<0.001 with C vs D, Figure panel a). Plaque burden at minimal lumen area (PB) was significantly greater with PFD in both negative FS and positive FS (52.3 vs 68.5, p<0.001 with A vs B; 49.5 vs 81.1, p<0.001 with C vs D, Figure panel b). Other volumetric plaque factors such as plaque volume (PV) and PAV, were also significantly greater with PFD in both negative FS and positive FS (all p<0.01 for A vs B and C vs D, Figure panel c and d). Adverse plaque characteristics (APC) were found to be frequently present in conjunction with PFD, regardless of FS (30.0% vs 53.6%, p=0.013 with A vs B; 26.2% vs 68.0%, p<0.001 with C vs D, Figure panel e). PFD without FS showed significantly higher in all hemodynamic and plaque characteristics compared to FS without PFD (B vs C - p<0.001 for WSS, PV, PAV; p=0.002 for PB; p=0.012 for APC, Figure). Conclusions Hemodynamic and morphological characteristics related to plaque vulnerability were associated with a pressure drop-derived index, PFD, independent of functional significance of the vessel.

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)0.002
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.056
GPT teacher head0.305
Teacher spread0.249 · 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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