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Record W3080108494 · doi:10.1016/j.jcmg.2020.07.008

Sex Differences in Coronary Computed Tomography Angiography–Derived Fractional Flow Reserve

2020· article· en· W3080108494 on OpenAlexaff
Timothy Fairbairn, Rebecca Dobson, Lyne Hurwitz-Koweek, Hitoshi Matsuo, Bjarne Linde Nørgaard, Niels Peter Rønnow Sand, Koen Nieman, Jeroen J. Bax, Gianluca Pontone, Gilbert Raff, Kavitha M. Chinnaiyan, Mark Rabbat, Tetsuya Amano, Tomohiro Kawasaki, Takashi Akasaka, Hironori Kitabata, Sukumaran Binukrishnan, Campbell Rogers, Daniel S. Berman, Manesh R. Patel, Pamela S. Douglas, Jonathon Leipsic

Bibliographic record

VenueJACC. Cardiovascular imaging · 2020
Typearticle
Languageen
FieldMedicine
TopicCardiac Imaging and Diagnostics
Canadian institutionsUniversity of British Columbia
FundersSiemensBayer
KeywordsMedicineFractional flow reserveCoronary artery diseaseStenosisRevascularizationInternal medicineCardiologyComputed tomography angiographyDemographicsRadiologyIncidence (geometry)AngiographyCoronary angiographyMyocardial infarction

Abstract

fetched live from OpenAlex

This study is to determine the management and clinical outcomes of patients investigated with coronary computed tomography angiography (CCTA)–derived fractional flow reserve (FFRCT) according to sex. Women are underdiagnosed with conventional ischemia testing, have lower rates of obstructive coronary artery disease (CAD) at invasive coronary angiography (ICA), yet higher mortality compared to men. Whether FFRCT improves sex-based patient management decisions compared to CCTA alone is unknown. Subjects with symptoms and CAD on CCTA were enrolled (2015 to 2017). Demographics, symptom status, CCTA anatomy, coronary volume to myocardial mass ratio (V/M), lowest FFRCT values, and management plans were captured. Endpoints included reclassification rate between CCTA and FFRCT management plans, incidence of ICA demonstrating obstructive CAD (≥50% stenosis) and revascularization rates. A total of 4,737 patients (n = 1,603 females, 33.8%) underwent CCTA and FFRCT. Women were older (age 68 ± 10 years vs. 65 ± 10 years; p < 0.0001) with more atypical symptoms (41.5% vs. 33.9%; p < 0.0001). Women had less obstructive CAD (65.4% vs. 74.7%; p < 0.0001) at CCTA, higher FFRCT (0.76 ± 0.10 vs. 0.73 ± 0.10; p < 0.0001), and lower likelihood of positive FFRCT ≤ 0.80 for the same degree stenosis (p < 0.0001). A positive FFRCT ≤0.80 resulted in equal referral to ICA (n = 510 [54.5%] vs. n = 1,249 [56.5%]; p = 0.31), but more nonobstructive CAD (n = 208 [32.1%] vs. n = 354 [24.5%]; p = 0.0003) and less revascularization (n = 294 [31.4%] vs. n = 800 [36.2%]; p < 0.0001) in women, unless the FFRCT was ≤0.75 where revascularization rates were similar (n = 253 [41.9%] vs. n = 715 [46.4%]; p = 0.06). Women have a higher V/M ratio (26.17 ± 7.58 mm3/g vs. 24.76 ± 7.22 mm3/g; p < 0.0001) that is associated with higher FFRCT independent of degree stenosis (p < 0.001). Predictors of revascularization included stenosis severity, FFRCT, symptoms, and V/M ratio (p < 0.001) but not female sex (p = 0.284). FFRCT differs between the sexes, as women have a higher FFRCT for the same degree of stenosis. In FFRCT-positive CAD, women have less obstructive CAD at ICA and less revascularization, which is associated with higher V/M ratio. The findings suggest that CAD and FFRCT variations by sex need specific interpretation as these differences may affect therapeutic decision making and clinical outcomes. (Assessing Diagnostic Value of Non-invasive FFRCT in Coronary Care [ADVANCE]; NCT02499679)

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.114
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.020
GPT teacher head0.235
Teacher spread0.215 · 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.

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

Citations75
Published2020
Admission routes1
Has abstractyes

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