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Record W2981877182 · doi:10.1093/eurheartj/ehz745.0118

4114Efficacy of alirocumab treatment after acute coronary syndrome according to new ACC/AHA guidelines for lipid-lowering therapy

2019· article· en· W2981877182 on OpenAlexaff
Matthew T. Roe, Michael Szarek, Q H Li, Deepak L. Bhatt, Vera Bittner, Shaun G. Goodman, Robert A. Harrington, Patricio López‐Jaramillo, Renato D. Lópes, Michael J. Louie, Patrick M. Moriarty, Robert A. Vogel, Marie T. Baccara‐Dinet, Philippe Gabríel Steg, Gregory G. Schwartz

Bibliographic record

VenueEuropean Heart Journal · 2019
Typearticle
Languageen
FieldMedicine
TopicLipoproteins and Cardiovascular Health
Canadian institutionsSt. Michael's Hospital
Fundersnot available
KeywordsMedicineMaceInternal medicineAcute coronary syndromeAlirocumabMyocardial infarctionUnstable anginaCardiologyDyslipidemiaCoronary artery diseasePercutaneous coronary interventionDiseaseCholesterolLipoprotein

Abstract

fetched live from OpenAlex

Abstract Background The 2018 ACC/AHA cholesterol management guidelines recommend additional lipid-lowering therapies for secondary prevention in patients with LDL-C ≥1.8 mmol/L despite maximally tolerated statin therapy who are considered “very high-risk” on the basis of history of multiple ischaemic events or an ischaemic event and multiple high-risk conditions. Purpose We examined the frequency of major adverse cardiovascular events (MACE) and efficacy of PCSK9 inhibition with alirocumab to reduce MACE in patients with recent acute coronary syndrome (ACS) categorized as very high-risk or not very high-risk by guideline criteria. Methods Patients in ODYSSEY OUTCOMES (n=18,924) with recent ACS and residual dyslipidaemia despite optimal statin therapy were randomized to alirocumab or placebo and followed for median 2.8 years. The primary MACE outcome was a composite of coronary heart disease death, non-fatal myocardial infarction (MI), ischaemic stroke, or hospitalization for unstable angina. Results Of 18,924 randomized patients, 11,935 (63.1%) were categorized as very high-risk and 6989 (36.9%) as not very high risk (per ACC/AHA guidelines criteria). In the very high-risk category, 4450 (37.3%) had a prior ischaemic event plus the trial-qualifying index ACS (MI, 3633; stroke, 524; peripheral artery disease, 759); 7485 (62.7%) had no ischaemic event before the index ACS but had ≥2 high-risk conditions (diabetes, 3319; age ≥65 years, 3087; current smoking, 2371; chronic kidney disease, 1583). In the placebo group, the incidence of MACE was higher among those in the very high-risk category (14.4%) vs those not at very high-risk (5.6%). Overall, alirocumab reduced the risk of MACE vs placebo (9.5% vs 11.1%, hazard ratio [HR] 0.85, 95% confidence interval [CI] 0.78–0.93; P=0.003), with consistent relative reductions in both risk categories (very high risk HR 0.84, 95% CI 0.76–0.92; not very high risk HR 0.86, 95% CI 0.70–1.06). However, the absolute reduction in MACE with alirocumab was greater among patients classified as very high-risk (2.1%) vs not very high risk (0.8%), and greater in particular among those classified as very high risk based on multiple ischaemic events (2.4%, Figure). Conclusions Application of 2018 ACC/AHA cholesterol guidelines criteria accurately identifies patients with ACS and dyslipidaemia who are at very high risk for recurrent MACE, and who derive a large absolute benefit from alirocumab treatment. Patients categorized as very high-risk based upon multiple ischaemic events derive a particularly large absolute benefit from treatment with alirocumab. Acknowledgement/Funding Supported by Sanofi and Regeneron Pharmaceuticals

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)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.079
GPT teacher head0.357
Teacher spread0.278 · 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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