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Record W2982130206 · doi:10.1093/eurheartj/ehz746.0188

P5010Use of risk score to identify lower and higher risk subsets among COMPASS-Eligible patients with stable CAD. Insights from the CLARIFY Registry

2019· article· en· W2982130206 on OpenAlexaff
Arthur Darmon, Grégory Ducrocq, A Jasliek, Laurent J. Feldman, Emmanuel Sorbets, Roberto Ferrari, Ian Ford, Jean‐Claude Tardif, Michał Tendera, Kim Fox, Philippe Gabríel Steg

Bibliographic record

VenueEuropean Heart Journal · 2019
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsMontreal Heart Institute
Fundersnot available
KeywordsMedicineRivaroxabanCoronary artery diseaseAspirinPopulationCompassInternal medicineStroke (engine)WarfarinCardiologyAtrial fibrillation

Abstract

fetched live from OpenAlex

Abstract Background The COMPASS trial showed that a combination of rivaroxaban and aspirin improved cardiovascular (CV) outcomes in patients with stable coronary artery disease (CAD) compared with aspirin alone, at the expense of increased bleeding. An important issue is to identify in this broad population, patients who are likely to derive the greatest benefit without too great a bleeding risk. Purpose To evaluate the performance of the CHA2DS2VaSc (range from 0 to 9), the REACH Recurrent Ischemic Score (RIS) (range from 0 to ≥29) and the REACH Bleeding Risk Score (BRS) (range from 0 to 22) to identify patients with the most favourable trade-off between ischemic and bleeding events, among CAD patients eligible to COMPASS Methods We used the CLARIFY Registry, an international registry of >30.000 patients with stable CAD. COMPASS inclusion and exclusion criteria were applied to the CLARIFY population with complete data (n=15.185) to define the “COMPASS eligible population”. Patients at high bleeding risk (REACH BRS >10), were excluded in accordance to COMPASS exclusion criteria. Patients were categorized as low-intermediate (0–1) or high (≥2) CHA2DS2VaSc; low (0–12) or intermediate (13–19) REACH RIS, and low (0–6) or intermediate (7–10) REACH BRS. The ischemic outcome was a composite of CV death, MI or stroke, and the bleeding outcome was a composite of bleeding leading to either admission or transfusion, or haemorrhagic stroke. Results The COMPASS-eligible population comprised 5.142 patients (33.9%). Ischemic and bleeding outcome for this group were 2.3 [2.1–2.5] and 0.5 [0.4–0.6] events/100 patient-years, respectively. Patients with high CHA2DS2VaSc score, intermediate REACH BRS and RIS represented 95.5% (n=4.913), 83.8% (n=4.309) and 37.6% (n=1.934) of the population. Regarding ischemic risk, patients with intermediate REACH RIS had the higher ischemic risk (3.0 [2.6–3.4] vs 1.9 [1.7–2.1] for patients with low REACH RIS, p<0.001), followed by intermediate REACH BRS (2.5 [2.2–2.7] vs 1.5 [1.2–2.0] for patients with low REACH BRS, p=0.0003) and high CHA2DS2VaSc score (2.4 [2.2–2.6]), compared to the overall population. Patients with low CHA2DS2VaSc had the lowest ischemic risk (0.6 [0.3–1.3]) compared to the overall population. Regarding bleeding risk, there were no differences between patients categorized according to CHA2DS2VaSc (0.5 [0.2–1.15] vs 0.5 [0.4–0.6], p=0.95) REACH BRS (0.4 [0.3–0.7] vs 0.5 [0.4–0.6], p=0.80) or REACH RIS (0.4 [0.3–0.5] vs 0.5 [0.4–0.7], p=0.26). Ischemic (blue) and bleeding (red) event Conclusions Among a broad population of CAD patients eligible to COMPASS, low CHA2DS2VaSc score identify a small subset of patients with very low ischemic risk which is unlikely to benefit from the adjunction of low dose rivaroxaban to standard therapy. Patients with intermediate REACH Recurrent Ischemic Score had higher ischemic risk, without increased bleeding risk and may be optimal candidates from adjunction of low dose rivaroxaban. 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)0.002
metaresearch head score (Gemma)0.006
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 score0.004
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.039
GPT teacher head0.297
Teacher spread0.258 · 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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