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Record W2982138431 · doi:10.1093/eurheartj/ehz745.0059

3294Frequency, management and outcomes of patients with stable coronary artery disease eligible for COMPASS. An analysis of the CLARIFY registry

2019· article· en· W2982138431 on OpenAlexaff
Arthur Darmon, Grégory Ducrocq, Adam Jasilek, Jean‐Michel Juliard, Emmanuel Sorbets, Roberto Ferrari, Ian Ford, Jean‐Claude Tardif, Kim Fox, Philippe Gabríel Steg

Bibliographic record

VenueEuropean Heart Journal · 2019
Typearticle
Languageen
FieldMedicine
TopicAntiplatelet Therapy and Cardiovascular Diseases
Canadian institutionsMontreal Heart Institute
Fundersnot available
KeywordsMedicineCoronary artery diseaseRivaroxabanInternal medicineRevascularizationPopulationStroke (engine)CompassAspirinDiabetes mellitusCardiologyObservational studyHazard ratioEjection fractionWarfarinHeart failureSurgeryMyocardial infarctionConfidence intervalAtrial fibrillation

Abstract

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Abstract Background The COMPASS trial demonstrated that a combination of rivaroxaban and aspirin improved cardiovascular (CV) outcomes in high-risk patients with either peripheral artery disease (PAD) or stable coronary artery disease (CAD) compared with aspirin alone, at the price of increased bleeding. A previous analysis of the REACH Registry reported an eligibility rate of 52.9% within a population with stable vascular disease. However, most of cardiologists actually treat patients with stable CAD, rather than PAD. Data regarding eligibility to COMPASS in CAD patients from real life practice are scarce. Purpose We aimed to describe the proportion of patients eligible to COMPASS within the CLARIFY Registry. Additionally, we aimed to describe their management and outcomes, comparing patients excluded from the trial (COMPASS Excluded), patients eligible for the trial (COMPASS Eligible), and patients who did not meet the “enrichment criteria” for enrolment (COMPASS Not Included). Methods We used the CLARIFY Registry, an international observational registry of more than 30.000 patients with stable CAD. In accordance with COMPASS exclusion criteria, patients with a REACH bleeding risk score >10, heart failure (HF), severe renal insufficiency, need for dual antiplatelet therapy (DAPT), or anticoagulant (AC) therapy were excluded. Then, COMPASS inclusion criteria were applied: CAD patients had to be 65 years or more, or, if younger, have documented atherosclerosis (PAD or revascularization involving at least two vascular beds) or at least two enrichment criteria (current smoker, diabetes mellitus, GFR <60 mL/min, or non lacunar ischemic stroke).The ischemic outcome was a composite of CV death, MI, or stroke and bleeding outcome was a composite of bleeding leading to either admission or transfusion, or haemorrhagic stroke. Results Among 15.185 patients with comprehensive data allowing precise assessment of eligibility, 43.1% (n=6.540) had at least one exclusion criteria (COMPASS-Excluded), 23.1% (n=3.503) did not have enrichment criteria (COMPASS-Not Included) and 33.9% (n=5.142) were eligible. The vast majority of excluded patients were excluded due to high bleeding risk (62.7% needing DAPT, and 52.7% for high REACH bleeding risk score). The rates (100 patients/year) of ischemic and bleeding outcome were 2.3 [2.1–2.5] and 0.5 [0.4–0.6] respectively for COMPASS-Eligible, 3.0 [2.8–3.2] and 0.6 [0.5–0.7] for COMPASS-Excluded and 1.2 [1.0–1.4] and 0.2 [0.2–0.3] for COMPASS-Not Included. Ischemic and bleeding events Conclusion In a large contemporary registry of stable CAD patients, approximately one of three patients was potentially eligible for adjunction of low-dose rivaroxaban to aspirin. This group is at particularly high risk of ischemic outcome. Patients with exclusion criteria for COMPASS had the worse ischemic and bleeding outcomes and represent a group in need of improved therapy. Acknowledgement/Funding None

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)0.003
metaresearch head score (Gemma)0.007
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 score0.005
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.015
GPT teacher head0.253
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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Citations1
Published2019
Admission routes1
Has abstractyes

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