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Record W3107325099 · doi:10.1093/ehjci/ehaa946.3011

REDUCE-IT: total ischemic events reduced across the full range of baseline LDL cholesterol and other key subgroups

2020· article· en· W3107325099 on OpenAlexaff
Deepak L. Bhatt, Michael Miller, Philippe Gabríel Steg, Eliot A. Brinton, Terry A. Jacobson, Steven Ketchum, Ralph T. Doyle, Rebecca A. Juliano, L Jiao, Craig Granowitz, John Gregson, Stuart Pocock, J.‐C. Tardif, Christie M. Ballantyne

Bibliographic record

VenueEuropean Heart Journal · 2020
Typearticle
Languageen
FieldMedicine
TopicLipoproteins and Cardiovascular Health
Canadian institutionsUniversité de MontréalMontreal Heart Institute
Fundersnot available
KeywordsMedicineMyocardial infarctionInternal medicineClinical endpointHazard ratioStroke (engine)Unstable anginaCardiologyProportional hazards modelConfoundingCoronary artery diseaseConfidence intervalRandomized controlled trial

Abstract

fetched live from OpenAlex

Abstract Background REDUCE-IT (Reduction of Cardiovascular Events with Icosapent Ethyl-Intervention Trial), a study of 8,179 randomized statin-treated patients with elevated triglycerides (TG) and increased cardiovascular (CV) risk followed for a median of 4.9 years, demonstrated robust results. Icosapent ethyl (IPE), a pure and stable prescription form of eicosapentaenoic acid, 4g/day reduced both time-to-first and total primary endpoint ischemic events (CV death, nonfatal myocardial infarction [MI], nonfatal stroke, coronary revascularization, or hospitalization for unstable angina) by 25% (HR 0.75; 95% CI 0.68–0.83; p<0.0001) and 30% (rate ratio 0.70; 95% CI 0.62–0.78; p<0.0001), respectively. Similar substantial reductions in first and total key secondary endpoint ischemic events (composite of CV death, nonfatal MI, or nonfatal stroke) were also observed. Demographic and baseline disease characteristics were generally balanced across treatment groups. Time-to-first event analyses showed robust and generally consistent benefit across subgroups. Previous total event analyses by baseline TG demonstrated large, consistent, statistically significant reductions across tertiles, suggesting the CV benefit of IPE is tied primarily to non-TG factors. Purpose Further explore the extent to which IPE reduced total primary and key secondary events across prespecified baseline demographic, disease, treatment, and lipid/lipoprotein/inflammatory biomarker subgroups. Methods Total events across subgroups were assessed with the prespecified negative binomial regression method. Main outcomes were total (first and subsequent) primary and key secondary composite endpoint events. Results Median baseline LDL-C levels in ascending tertiles were 58, 76, and 96 mg/dL; there were large, significant relative reductions in total primary endpoint events with IPE across tertiles (35%, 28%, and 27%, respectively; interaction p=0.62), with parallel substantial absolute risk reductions. Similar, significant relative reductions of 33%, 28%, and 24% in total key secondary endpoint events were observed, along with substantial absolute risk reductions. Total events analyses of prespecified subgroups also demonstrated robust and generally consistent findings for the primary and key secondary composite endpoints. Conclusion REDUCE-IT demonstrated substantial reductions in first and total primary and key secondary endpoint ischemic events, with robust and generally consistent results across baseline TG and LDL-C levels, as well as other prespecified baseline biomarker, demographic, disease, and treatment subgroups. These analyses provide useful insights for clinicians considering the range of patients who may benefit from IPE therapy and suggest that mechanisms beyond the lipid/lipoprotein/inflammatory pathways tested, including mechanisms beyond the LDL receptor pathways, may contribute to the observed substantial reductions in total ischemic burden with IPE therapy. Funding Acknowledgement Type of funding source: Other. Main funding source(s): The study was funded by Amarin Pharma, Inc.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.003
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)0.0030.000

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 head0.048
GPT teacher head0.310
Teacher spread0.263 · 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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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