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Record W2803515378 · doi:10.1093/ndt/gfy104.sp436

SP436DESIGN FEATURES OF THE BETONMACE CHRONIC KIDNEY DISEASE SUB-STUDY; EFFECTS OF THE SELECTIVE BET-INHIBITOR APABETALONE ON KIDNEY FUNCTION AND MACE IN POST-ACS PATIENTS WITH ESTIMATED GLOMERULAR FILTRATION RATE BELOW 60 AND DIABETES

2018· article· en· W2803515378 on OpenAlexaff
Kamyar Kalantar‐Zadeh, Jan Johansson, Ewelina Kulikowski, Christopher Halliday, Ken Lebioda, Mike Sweeney, Norman C.W. Wong, Stephen J. Nicholls, Gregory Schwartz, Kausik K. Ray

Bibliographic record

VenueNephrology Dialysis Transplantation · 2018
Typearticle
Languageen
FieldMedicine
TopicDiet and metabolism studies
Canadian institutionsResverlogix (Canada)
Fundersnot available
KeywordsMedicineRenal functionMaceKidney diseaseUrologyInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION AND AIMS: Cardio-metabolic disease often contributes to chronic kidney disease (CKD). CKD is an important risk factor for increased cardiovascular events in high risk vascular disease patients. Epigenetic dysregulation and bromodomain and extraterminal domain (BET) proteins are believed to be involved in cardiovascular disease (CVD) and CKD pathogenesis. Treatment with apabetalone, a selective BET inhibitor, over 6-months has illustrated a reduction in alkaline phosphatase (ALP) in phase 2 studies. Additionally, in these phase 2 studies a significant CVD event reduction was highlighted which was most pronounced in patients with diabetes. Therefore an event driven phase 3 trial in CVD patients - BETonMACE - has been initiated. METHODS: BETonMACE is a multinational, multi-center (≍200 sites) pivotal phase 3, double blind randomized (1:1), placebo controlled trial in post-Acute Coronary Syndrome (ACS) patient with diabetes mellitus and HDL-cholesterol <40 (females) and <45 (males) mg/dL investigating if apabetalone 100 mg b.i.d. vs. placebo in addition to standard of care including high intensity statins, beta blockers, ace inhibitors, and dual anti-platelet inhibition treatment, delays the time to major adverse cardiac events (MACE). The study is designed to randomize 2,400 patients and accrue 250 MACE defined as cardiovascular death, non-fatal MI and stroke. Estimated glomerular filtration rate (eGFR) is calculated using the Cockcroft Gault equation. In all patients, eGFR is evaluated at screening, 24 weeks, 52 weeks, 76 weeks, 100 weeks and at the termination of the trial. Evidence of severe renal impairment as determined by an eGFR less than 30 mL/min/1.7m2 at screening is an exclusion criteria in the BETonMACE study. The substudy will include the assessment of changes in kidney function in a patient population with eGFR below 60 mL/min/1.7m2 at screening. Therefore, these patients will be classified as stage 3 chronic kidney disease patients. Kidney function assessment is a pre-specified variable comparing change from baseline for active treatment vs. placebo applying standard adjustments including age and baseline eGFR. Markers of kidney disease risk such as alkaline phosphatase, serum chemistry markers and inflammatory markers will also be included in the analysis. RESULTS: To date, BETonMACE has randomized 2,224 patients of which 11% have screening eGFR below 60. At completion patients will have been treated from 6 to approximately 36 months. CONCLUSIONS: Kidney function assessment, using eGFR, and MACE reduction is being evaluated in BETonMACE, a phase 3 CVD event trial testing the efficacy of a novel first-in-class BET-inhibitor, apabetalone. The kidney population sub-study, in patients with eGFR below 60 mL/min/1.7m2 at screening, will provide further insights about epigenetics in kidney function changes and MACE effects of BET-inhibition in approximately 300 post-ACS patients with diabetes, low HDL and CKD.

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)0.003
metaresearch head score (Gemma)0.002
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 score0.015
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0150.002

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 head0.004
GPT teacher head0.207
Teacher spread0.204 · 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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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