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Risk Categorization Using New American College of Cardiology/American Heart Association Guidelines for Cholesterol Management and Its Relation to Alirocumab Treatment Following Acute Coronary Syndromes

2019· article· en· W2971575888 on OpenAlexaff
Matthew T. Roe, Qian H Li, Deepak L. Bhatt, Vera Bittner, Rafael Díaz, Shaun G. Goodman, Robert A. Harrington, J. Wouter Jukema, Patricio López‐Jaramillo, Renato D. Lópes, Michael J. Louie, Patrick M. Moriarty, Michael Szarek, Robert A. Vogel, Harvey D. White, Andreas M. Zeiher, Marie T. Baccara‐Dinet, Philippe Gabríel Steg, Gregory G. Schwartz

Bibliographic record

VenueCirculation · 2019
Typearticle
Languageen
FieldMedicine
TopicLipoproteins and Cardiovascular Health
Canadian institutionsSt. Michael's Hospital
FundersUniversité de ParisAssistance publique-Hôpitaux de ParisImperial College LondonInstitut National de la Santé et de la Recherche MédicaleState University of New YorkSanofi
KeywordsMedicineAlirocumabCardiologyInternal medicineAcute coronary syndromeCategorizationAssociation (psychology)Intensive care medicineCholesterolMyocardial infarctionLipoprotein

Abstract

fetched live from OpenAlex

Background: The 2018 US cholesterol management guidelines recommend additional lipid-lowering therapies for secondary prevention in patients with low-density lipoprotein cholesterol ≥70 mg/dL or non−high-density lipoprotein cholesterol ≥100 mg/dL despite maximum tolerated statin therapy. Such patients are considered at very high risk (VHR) based on a history of >1 major atherosclerotic cardiovascular disease (ASCVD) event or a single ASCVD event and multiple high-risk conditions. We investigated the association of US guideline-defined risk categories with the occurrence of ischemic events after acute coronary syndrome and reduction of those events by alirocumab, a PCSK9 (proprotein convertase subtilisin/kexin type 9) inhibitor. Methods: In the ODYSSEY OUTCOMES trial (Evaluation of Cardiovascular Outcomes After an Acute Coronary Syndrome During Treatment With Alirocumab), patients with recent acute coronary syndrome and residual dyslipidemia despite optimal statin therapy were randomly assigned to alirocumab or placebo. The primary trial outcome (major adverse cardiovascular events, ie, coronary heart disease death, nonfatal myocardial infarction, ischemic stroke, or hospitalization for unstable angina) was examined according to American College of Cardiology/American Heart Association risk category. Results: Of 18 924 participants followed for a median of 2.8 years, 11 935 (63.1%) were classified as VHR: 4450 (37.3%) had multiple prior ASCVD events and 7485 (62.7%) had 1 major ASCVD event and multiple high-risk conditions. Major adverse cardiovascular events occurred in 14.4% of placebo-treated patients at VHR versus 5.6% of those not at VHR. In the VHR category, major adverse cardiovascular events occurred in 20.4% with multiple prior ASCVD events versus 10.7% with 1 ASCVD event and multiple high-risk conditions. Alirocumab was associated with consistent relative risk reductions in both risk categories (hazard ratio=0.84 for VHR; hazard ratio=0.86 for not VHR; P interaction =0.820) and by stratification within the VHR group (hazard ratio=0.86 for multiple prior ASCVD events; hazard ratio=0.82 for 1 major ASCVD event and multiple high-risk conditions; P interaction =0.672). The absolute risk reduction for major adverse cardiovascular events with alirocumab was numerically greater (but not statistically different) in the VHR group versus those not at VHR (2.1% versus 0.8%; P interaction =0.095) and among patients at VHR with multiple prior ASCVD events versus a single prior ASCVD event (2.4% versus 1.8%; P interaction =0.661). Conclusions: The US guideline criteria identify patients with recent acute coronary syndrome and dyslipidemia who are at VHR for recurrent ischemic events and who may derive a larger absolute benefit from treatment with alirocumab. Clinical Trial Registration: URL: https://www.clinicaltrials.gov . Unique identifier: NCT01663402.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.550

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.037
GPT teacher head0.322
Teacher spread0.285 · 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.

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

Citations52
Published2019
Admission routes1
Has abstractyes

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