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Record W3092288248 · doi:10.1093/eurheartj/ehaa649

Lipoprotein(a) lowering by alirocumab reduces the total burden of cardiovascular events independent of low-density lipoprotein cholesterol lowering: ODYSSEY OUTCOMES trial

2020· article· en· W3092288248 on OpenAlexafffund
Michael Szarek, Vera Bittner, Philip E. Aylward, Marie T. Baccara‐Dinet, Deepak L. Bhatt, Rafael Díaz, Zlatko Fras, Shaun G. Goodman, Sigrun Halvorsen, Robert A. Harrington, J. Wouter Jukema, Patrick M. Moriarty, Robert Pordy, Kausik K. Ray, Peter Sinnaeve, Sotirios Tsimikas, Robert A. Vogel, Harvey D. White, Doron Zahger, Andreas M. Zeiher, Philippe Gabríel Steg, Gregory G. Schwartz

Bibliographic record

VenueEuropean Heart Journal · 2020
Typearticle
Languageen
FieldMedicine
TopicLipoproteins and Cardiovascular Health
Canadian institutionsCanadian VIGOUR CentreUniversity of AlbertaSt. Michael's Hospital
FundersNIHR Imperial Biomedical Research CentreAmerican RegentGenentechEsperion TherapeuticsSirtex MedicalHLS TherapeuticsIdorsia PharmaceuticalsNovo NordiskDuke Clinical Research InstituteAstellas PharmaEisaiDaiichi-SankyoEuropean CommissionMedicines CompanyRegado BiosciencesTenax TherapeuticsAstraZenecaAmarin CorporationIonis PharmaceuticalsLuitpold PharmaceuticalsF. Hoffmann-La RocheIronwood Pharmaceuticals, IncorporatedRegeneron PharmaceuticalsBoston VA Research InstituteBoston Scientific CorporationEli Lilly and CompanyCleveland ClinicBristol-Myers SquibbNational Institute for Health and Care ResearchCSL BehringBelvoir Media GroupServierGilead SciencesEuropean Society of CardiologyAmgenPfizerSt. Jude MedicalKowa CompanyNational Institute of Diabetes and Digestive and Kidney DiseasesSanofiAmerican Heart Association
KeywordsMedicineAlirocumabLdl cholesterolCholesterolLipoproteinAtherosclerotic cardiovascular diseaseLipoprotein(a)Internal medicineCardiologyDiseaseApolipoprotein A1

Abstract

fetched live from OpenAlex

AIMS: Lipoprotein(a) concentration is associated with first cardiovascular events in clinical trials. It is unknown if this relationship holds for total (first and subsequent) events. In the ODYSSEY OUTCOMES trial in patients with recent acute coronary syndrome (ACS), the proprotein convertase subtilisin/kexin type 9 inhibitor alirocumab reduced lipoprotein(a), low-density lipoprotein cholesterol (LDL-C), and cardiovascular events compared with placebo. This post hoc analysis determined whether baseline levels and alirocumab-induced changes in lipoprotein(a) and LDL-C [corrected for lipoprotein(a) cholesterol] independently predicted total cardiovascular events. METHODS AND RESULTS: Cardiovascular events included cardiovascular death, non-fatal myocardial infarction, stroke, hospitalization for unstable angina or heart failure, ischaemia-driven coronary revascularization, peripheral artery disease events, and venous thromboembolism. Proportional hazards models estimated relationships between baseline lipoprotein(a) and total cardiovascular events in the placebo group, effects of alirocumab treatment on total cardiovascular events by baseline lipoprotein(a), and relationships between lipoprotein(a) reduction with alirocumab and subsequent risk of total cardiovascular events. Baseline lipoprotein(a) predicted total cardiovascular events with placebo, while higher baseline lipoprotein(a) levels were associated with greater reduction in total cardiovascular events with alirocumab (hazard ratio Ptrend = 0.045). Alirocumab-induced reductions in lipoprotein(a) (median -5.0 [-13.6, 0] mg/dL) and corrected LDL-C (median -51.3 [-67.1, -34.0] mg/dL) independently predicted lower risk of total cardiovascular events. Each 5-mg/dL reduction in lipoprotein(a) predicted a 2.5% relative reduction in cardiovascular events. CONCLUSION: Baseline lipoprotein(a) predicted the risk of total cardiovascular events and risk reduction by alirocumab. Lipoprotein(a) lowering contributed independently to cardiovascular event reduction, supporting the concept of lipoprotein(a) as a treatment target after ACS.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.457
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.030
GPT teacher head0.271
Teacher spread0.241 · 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 teacher head, not a consensus.

Study designBench or experimental
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".

Quick stats

Citations209
Published2020
Admission routes2
Has abstractyes

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