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Record W2982282556 · doi:10.1093/eurheartj/ehz746.0771

P6165Sex differences in compositional plaque volume progression in patients with stable coronary artery disease: observations from a serial CCTA registry

2019· article· en· W2982282556 on OpenAlexaff
S E Lee, Daniele Andreini, Matthew J. Budoff, Filippo Cademartiri, Martin Hadamitzky, Hugo Marques, Jonathon Leipsic, Peter H. Stone, Habib Samady, Jagat Narula, Daniel S. Berman, Leslee J. Shaw, Jeroen J. Bax, James K. Min, Hyuk‐Jae Chang

Bibliographic record

VenueEuropean Heart Journal · 2019
Typearticle
Languageen
FieldMedicine
TopicCardiac Imaging and Diagnostics
Canadian institutionsSt. Paul's Hospital
Fundersnot available
KeywordsMedicineCoronary artery diseaseInternal medicineCardiologyDiabetes mellitusProspective cohort studyConfidence intervalMultivariate analysisEndocrinology

Abstract

fetched live from OpenAlex

Abstract Background It is unclear whether sex impacts the plaque volume (PV) progression in patients with stable coronary artery disease (CAD). Purpose To explore whether the total and compositional PV progression rate differ according to sex. Methods We performed a prospective multinational registry of consecutive patients who underwent serial CCTA at ≥2-year interval. Total and compositional PV at baseline and follow-up were quantitatively analysed and normalized using the analysed total vessel length. Multivariate linear regression models were constructed for each women and men. Results Of the 1,255 patients included (median CT interval 3.8 years), 543 were women and 712 were men. Women were older (62±9 years vs. 59±9 years, p<0.001) and had higher total cholesterol level (195±41mg/dL vs. 187±39mg/dL, p=0.002). Prevalence of hypertension, diabetes, and family history of CAD were not different (all p>0.05). At baseline, men possessed greater total PV (131.5±230.5mm3 vs. 97.7±193.6mm3, p=0.005) and a higher prevalence of high-risk plaques (HRP) than women (31% vs. 20%, p<0.001). Annual total PV progression rate was greater in men, driven by the greater non-calcified PV progression (TABLE). In multivariate analysis (TABLE), although total PV progression rate was not different, women were associated with greater calcified PV progression (β=2.83, p=0.004) but slower non-calcified PV progression (β=-3.39, p=0.008) and less development of HRP (β=-0.18, p=0.049) than men. CCTA findings according to sex Univariate analysis Female Sex in Multivariable Analysis Women (n=543) Men (n=712) P β SE P Agatston CACS, /year 0.44±0.7 0.4±0.7 0.332 0.106 0.04 0.006 Total PVnormalized, mm3/year 14.7±23.4 17.8±26.2 0.026 -0.56 1.33 0.677 Calcified PVnormalized, mm3/year 10.5±21.5 10.0±19.1 0.670 2.83 0.98 0.004 Non-calcified PVnormalized, mm3/year 4.2±17.3 7.8±21.2 0.001 -3.39 1.28 0.008 Development of high-risk plaque*, n (%) 86 (15.8) 139 (19.5) 0.092 -0.18 0.09 0.049 In linear multivariate regression analysis adjusted with age, race, HTN, DM, family history, smoking, LDL, statin, anti-platelets, beta-blockers, and PV at baseline, women were associated with greater calcified PV progression and slower non-calcified PV progression. (High-risk plaque was defined as ≥2 of low-attenuation plaque, spotty calcification, and positive remodelling.) Conclusion In this large CCTA cohort, we found that the compositional PV progression differs according to sex. These findings, which are hypothesis generating, suggest that comprehensive plaque evaluation may contribute to further refine risk stratification according to sex. Acknowledgement/Funding This work was supported by the National Research Foundation of Korea funded by the Ministry of Science and ICT (Grant No. 2012027176).

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 score0.003
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
Bibliometrics0.0010.001
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 head0.023
GPT teacher head0.252
Teacher spread0.229 · 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
Published2019
Admission routes1
Has abstractyes

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