MétaCan
Menu
Back to cohort
Record W2604910616 · doi:10.1136/heartjnl-2016-310090

Predicting the risk of bleeding during dual antiplatelet therapy after acute coronary syndromes

2017· article· en· W2604910616 on OpenAlexaff
Joakim Alfredsson, Benjamin Neely, Megan L. Neely, Deepak L. Bhatt, Shaun G. Goodman, Pierluigi Tricoci, Kenneth W. Mahaffey, Jan H. Cornel, Harvey D. White, Keith A.A. Fox, Dorairaj Prabhakaran, Kenneth J. Winters, Paul W. Armstrong, E. Magnus Ohman, Matthew T. Roe

Bibliographic record

VenueHeart · 2017
Typearticle
Languageen
FieldMedicine
TopicAntiplatelet Therapy and Cardiovascular Diseases
Canadian institutionsCanadian VIGOUR CentreUniversity of AlbertaSt. Michael's Hospital
FundersDaiichi Sankyo CompanyEli Lilly and Company
KeywordsMedicineTIMICardiologyInternal medicineMyocardial infarctionAcute coronary syndromeAspirinUnstable anginaClopidogrelFramingham Risk ScoreCoronary artery diseaseCreatinineThrombolysisSurgeryDisease

Abstract

fetched live from OpenAlex

Objectives Dual antiplatelet therapy (DAPT) with aspirin + a P2Y12 inhibitor is recommended for at least 12 months for patients with acute coronary syndrome (ACS), with shorter durations considered for patients with increased bleeding risk. However, there are no decision support tools available to predict an individual patient’s bleeding risk during DAPT treatment in the post-ACS setting. Methods To develop a longitudinal bleeding risk prediction model, we analy sed 9240 patients with unstable angina/non-ST segment elevation myocardial infarction (NSTEMI) from the Targeted Platelet Inhibition to Clarify the Optimal Strategy to Medically Manage Acute Coronary Syndromes (TRILOGY ACS) trial, who were managed without revasculari sation and treated with DAPT for a median of 14.8 months. Results We identified 10 significant baseline predictors of non-coronary artery bypass grafting (CABG)-related Global Use of Strategies to Open Occluded Arteries (GUSTO) severe/life-threatening/moderate bleeding: age, sex, weight, NSTEMI (vs unstable angina), angiography performed before randomi sation, prior peptic ulcer disease, creatinine, systolic blood pressure, haemoglobin and treatment with beta-blocker. The five significant baseline predictors of Thrombolysis In Myocardial Infarction (TIMI) major or minor bleeding included age, sex, angiography performed before randomi sation, creatinine and haemoglobin. The models showed good predictive accuracy with Therneau’s C-indices: 0.78 (SE = 0.024) for the GUSTO model and 0.67 (SE = 0.023) for the TIMI model. Internal validation with bootstrapping gave similar C-indices of 0.77 and 0.65, respectively. External validation demonstrated an attenuated C-index for the GUSTO model (0.69) but not the TIMI model (0.68). Conclusions Longitudinal bleeding risks during treatment with DAPT in patients with ACS can be reliably predicted using selected baseline characteristics. The TRILOGY ACS bleeding models can inform risk –benefit considerations regarding the duration of DAPT following ACS. Trial registration ClinicalTrials.gov identifier: https://clinicaltrials.gov/ct2/show/NCT00699998

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)0.009
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.266
Teacher spread0.250 · 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

Citations47
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueHeartSame topicAntiplatelet Therapy and Cardiovascular DiseasesFrench-language works237,207