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Cardiovascular benefits outweigh risks in patients with atrial fibrillation in REDUCE-IT (Reduction of Cardiovascular Events with Icosapent Ethyl-Intervention Trial)

2021· article· en· W4386660575 on OpenAlexaff
Brian Olshansky, Deepak L. Bhatt, Michael Miller, Philippe Gabríel Steg, Eliot A. Brinton, Terry A. Jacobson, Steven Ketchum, Robert T. Doyle, Rebecca A. Juliano, Lixia Jiao, Peter R. Kowey, James A. Reiffel, J.‐C. Tardif, Christie M. Ballantyne, Mina K. Chung

Bibliographic record

VenueEuropean Heart Journal · 2021
Typearticle
Languageen
FieldMedicine
TopicLipoproteins and Cardiovascular Health
Canadian institutionsUniversité de MontréalMontreal Heart Institute
FundersAmarin PharmaAmarin Corporation
KeywordsMedicineInternal medicineAtrial fibrillationMyocardial infarctionStroke (engine)CardiologyClinical endpointUnstable anginaHazard ratioAdverse effectAnginaRandomized controlled trialConfidence interval

Abstract

fetched live from OpenAlex

Abstract Background/Introduction REDUCE-IT, a multinational, double-blind trial, randomized 8179 statin-treated patients with controlled low density lipoprotein cholesterol, elevated triglycerides, and cardiovascular (CV) risk, to icosapent ethyl (IPE) 4 grams/day or placebo. IPE reduced the primary (CV death, myocardial infarction [MI], stroke, coronary revascularization, hospitalization for unstable angina) and key secondary (CV death, MI, stroke) endpoints 25% and 26%, respectively (each p<0.0001), and individual components including stroke (28%), MI (31%), cardiac arrest (48%), and sudden cardiac death (31%) (all p≤0.01). With IPE, bleeding was greater (11.8% vs 9.9%; p=0.006), serious bleeding trended higher (2.7% vs 2.1%; p=0.06), and atrial fibrillation/flutter (AF/F) hospitalization endpoints increased (3.1% vs 2.1%; p=0.004). Purpose To evaluate the effects of IPE on the risk of CV events and safety measures in patients by either history of AF/F or in-study occurrence of positively adjudicated AF/F hospitalization. Methods Conduct post hoc efficacy and safety subgroup analyses of patients with or without either baseline history of AF/F or in-study adjudicated AF/F hospitalization, including hospitalization for ≥24 hours; AF/F not meeting endpoint criteria were reported as adverse events. Results Patients with (n=751; 9.2%) AF/F history at baseline (vs without; n=7428; 90.8%) (Figure 1), or those with (n=211; 2.6%) positively adjudicated in-study AF/F hospitalization endpoints (vs without; n=7968; 97.4%) (Figure 2), had higher event rates of primary, key secondary, and fatal or nonfatal stroke endpoints, but relative risk reductions with IPE were not significantly different (all interaction p-values [pint]=ns). Similar reductions were observed with IPE across the prespecified endpoint testing hierarchy in patients with or without AF/F history or in-study hospitalization endpoints. Patients with baseline AF/F history had similar relative risk for in-study occurrence of AF/F hospitalization with IPE versus placebo (pint=0.21) but had greater absolute risk (12.5% vs 6.3%, IPE vs placebo) vs patients without baseline AF/F history (2.2% vs 1.6%, IPE vs placebo); i.e., recurrent AF/F in those with a prior history of AF/F was more prevalent than de novo AF/F. Serious bleeding trended higher regardless of AF/F history or in-study AF/F hospitalization endpoints (all pint=ns); absolute risk of serious bleeding was greater in patients with AF/F history at baseline (7.3% vs 6.0%) vs those without a baseline history of AF/F (2.3% vs 1.7%), and serious bleeding also trended higher in patients with in-study AF/F hospitalization (8.7% vs 6.0%) vs without (2.5% vs 2.0%) [all IPE vs placebo]. Conclusion REDUCE-IT patients with AF/F history or in-study AF/F hospitalization endpoints had greater CV risk, but similar relative risk reduction in primary, key secondary, and fatal or nonfatal stroke endpoints with IPE. Funding Acknowledgement Type of funding sources: Private company. Main funding source(s): Amarin Pharma, Inc.

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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: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.045
GPT teacher head0.291
Teacher spread0.246 · 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 designNon-randomized trial
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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Citations3
Published2021
Admission routes1
Has abstractyes

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