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Icosapent ethyl by baseline small dense low-density lipoprotein cholesterol: an analysis of REDUCE-IT

2024· article· en· W4403806406 on OpenAlexaff
Rahul Aggarwal, Deepti Bhatt, Philippe Gabríel Steg, Michael Miller, Eliot A. Brinton, Richard L. Dunbar, Fabrice M A C Martens, Steven Ketchum, Jean‐Claude Tardif, Preston Mason, Christie M. Ballantyne

Bibliographic record

VenueEuropean Heart Journal · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Lipids, and Metabolism
Canadian institutionsUniversité de Montréal
FundersAmarin PharmaAmarin Corporation
KeywordsMedicineBaseline (sea)Internal medicineCholesterolLow-density lipoproteinCardiologyOceanography

Abstract

fetched live from OpenAlex

Abstract Background Small dense low-density lipoprotein cholesterol (sdLDL-C) is associated with cardiovascular (CV) risk. Purpose We assessed if baseline sdLDL-C modified the effect of icosapent ethyl among patients in REDUCE-IT. Methods REDUCE-IT randomized patients to icosapent ethyl vs placebo. In this post hoc analysis, patients ≥45 years with CV disease or ≥50 years with diabetes and CV risk factors were included. Patients were required to have triglycerides of 135-499 mg/dL and low-density lipoprotein cholesterol (LDL-C) of 41-100 mg/dL. Exclusion criteria included severe heart failure, acute severe liver disease, or hemoglobin A1c >10%. Patients were stratified by baseline calculated sdLDL-C. A threshold of 46 mg/dL was chosen because sdLDL-C greater than this cutoff has predicted CV risk despite well-controlled LDL-C. The primary outcome included a composite of CV death, nonfatal myocardial infarction (MI), nonfatal stroke, coronary revascularization, or unstable angina. The key secondary composite outcome included CV death, nonfatal MI, or nonfatal stroke. Outcomes were assessed with Cox proportional hazard models and interaction analyses were conducted to assess heterogeneity in treatment effect by baseline sdLDL-C. Results REDUCE-IT included 8179 patients, with 8157 (99.7%) patients having sdLDL-C data. Among these patients, 7698 (94.4%) had sdLDL-C <46 mg/dL and 459 (5.6%) had sdLDL-C ≥46 mg/dL. Median sdLDL-C was 34.2 mg/dL (interquartile range [IQR]: 30.3, 38.4 mg/dL) in the lower sdLDL-C group and 48.4 mg/dL (IQR: 47.1, 50.6 mg/dL) in the higher sdLDL-C group (Table). The placebo group had a greater event rate in the higher sdLDL-C group compared to the lower sdLDL-C group (72.0 vs 56.4 events per 1000 p-y), though event rates in the icosapent ethyl group were similar by sdLDL-C group (44.1 vs 43.3 events per 1000 p-y, respectively). Icosapent ethyl reduced the rate of the primary outcome compared with placebo (17.2% vs 22.0%; hazard ratio [HR]: 0.75 [95% CI: 0.68, 0.83]; P<0.0001). The number needed to treat [NNT] to avoid one primary endpoint event was 21. In the lower sdLDL-C group, icosapent ethyl decreased rates of the primary outcome (43.3 events per 1000 patient-years [p-y]) compared with the placebo group (56.4 events per 1000 p-y) (17.3% vs 21.7%; HR: 0.77 [95% CI: 0.69, 0.85]; NNT: 22). Similarly, in the higher sdLDL-C group, icosapent ethyl decreased the rate of the primary outcome (44.1 events per 1000 p-y) compared with the placebo group (72.0 events per 1000 p-y) (16.6% vs 26.4%; HR: 0.58 [95% CI: 0.38, 0.88]; NNT: 10). There was no significant interaction between treatment benefit and baseline sdLDL-C for the primary (Pinteraction = 0.22) and key secondary outcome (Pinteraction = 0.82). Findings were overall similar for the other secondary outcomes (Figure). Conclusion Icosapent ethyl reduced CV outcomes in patients with high CV risk and elevated triglycerides irrespective of low or high sdLDL-C.

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)0.006
metaresearch head score (Gemma)0.007
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 score0.006
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.007
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.011
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.024
GPT teacher head0.292
Teacher spread0.268 · 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
Published2024
Admission routes1
Has abstractyes

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