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Low-Density Lipoprotein Cholesterol Lowering With Evolocumab and Outcomes in Patients With Peripheral Artery Disease

2017· article· en· W2768588165 on OpenAlexaff
Marc P. Bonaca, Patrice Nault, Robert P. Giugliano, Anthony Keech, Armando Lira Pineda, Estella Kanevsky, Julia Kuder, Sabina A. Murphy, J. Wouter Jukema, Basil S. Lewis, Lâle Tokgözoğlu, Ransi Somaratne, Peter Sever, Terje R. Pedersen, Marc S. Sabatine

Bibliographic record

VenueCirculation · 2017
Typearticle
Languageen
FieldMedicine
TopicPeripheral Artery Disease Management
Canadian institutionsMcGill UniversityCégep de l'Outaouais
FundersNational Institute for Health and Care Research
KeywordsMedicineEvolocumabArterial diseaseInternal medicinePeripheralCardiologyCholesterolDiseasePCSK9LipoproteinAtherosclerotic cardiovascular diseaseVascular diseaseLDL receptor

Abstract

fetched live from OpenAlex

Background: The PCSK9 (proprotein convertase subtilisin/kexin type 9) inhibitor evolocumab reduced low-density lipoprotein cholesterol and cardiovascular events in the FOURIER trial (Further Cardiovascular Outcomes Research With PCSK9 Inhibition in Subjects With Elevated Risk). We investigated the efficacy and safety of evolocumab in patients with peripheral artery disease (PAD) as well as the effect on major adverse limb events. Methods: FOURIER was a randomized trial of evolocumab versus placebo in 27 564 patients with atherosclerotic disease on statin therapy followed for a median of 2.2 years. Patients were identified as having PAD at baseline if they had intermittent claudication and an ankle brachial index of <0.85, or if they had a prior peripheral vascular procedure. The primary end point was a composite of cardiovascular death, myocardial infarction, stroke, hospital admission for unstable angina, or coronary revascularization. The key secondary end point was a composite of cardiovascular death, myocardial infarction, or stroke. An additional outcome of interest was major adverse limb events defined as acute limb ischemia, major amputation, or urgent peripheral revascularization for ischemia. Results: Three thousand six hundred forty-two patients (13.2%) had PAD (1505 with no prior myocardial infarction or stroke). Evolocumab significantly reduced the primary end point consistently in patients with PAD (hazard ratio [HR] 0.79; 95% confidence interval [CI], 0.66–0.94; P =0.0098) and without PAD (HR 0.86; 95% CI, 0.80–0.93; P =0.0003; P interaction =0.40). For the key secondary end point, the HRs were 0.73 (0.59–0.91; P =0.0040) for those with PAD and 0.81 (0.73–0.90; P <0.0001) for those without PAD ( P interaction =0.41). Because of their higher risk, patients with PAD had larger absolute risk reductions for the primary end point (3.5% with PAD, 1.6% without PAD) and the key secondary end point (3.5% with PAD, 1.4% without PAD). Evolocumab reduced the risk of major adverse limb events in all patients (HR, 0.58; 95% CI, 0.38–0.88; P =0.0093) with consistent effects in those with and without known PAD. There was a consistent relationship between lower achieved low-density lipoprotein cholesterol and lower risk of limb events ( P =0.026 for the beta coefficient) that extended down to <10 mg/dL. Conclusions: Patients with PAD are at high risk of cardiovascular events, and PCSK9 inhibition with evolocumab significantly reduced that risk with large absolute risk reductions. Moreover, lowering of low-density lipoprotein cholesterol with evolocumab reduced the risk of major adverse limb events. Clinical Trial Registration: URL: https://www.clinicaltrials.gov . Unique identifier: NCT01764633.

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)0.004
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 score0.002
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.008
GPT teacher head0.226
Teacher spread0.219 · 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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Citations754
Published2017
Admission routes1
Has abstractyes

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