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Record W3110571006 · doi:10.1093/ehjci/ehaa946.1412

Antiplatelet therapy prescription patterns for acute coronary syndrome: a decade analysed

2020· article· en· W3110571006 on OpenAlexaffabout
Saurabh Gupta, Emilie P. Belley‐Côté, Ameen Basha, Charlotte McEwen, Nicole Wu, Shamir R. Mehta, Jon-David Schwalm, Richard Whitlock

Bibliographic record

VenueEuropean Heart Journal · 2020
Typearticle
Languageen
FieldMedicine
TopicAntiplatelet Therapy and Cardiovascular Diseases
Canadian institutionsPopulation Health Research InstituteMcMaster University
Fundersnot available
KeywordsMedicineTicagrelorClopidogrelAcute coronary syndromePercutaneous coronary interventionConventional PCIMedical prescriptionP2Y12Internal medicineCardiologyGuidelineAspirinEmergency medicineMyocardial infarctionPharmacology

Abstract

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Abstract Background/Introduction Guidelines recommend dual antiplatelet therapy (DAPT) with acetylsalicylic acid (ASA) and ticagrelor following acute coronary syndrome (ACS) regardless of management strategy. Despite this, prescription practices lag and appropriate DAPT is not utilized. Purpose We aimed to trend differences in P2Y12 inhibitor prescriptions between ACS patients managed with percutaneous coronary intervention (PCI) or coronary artery bypass grafting (CABG). As well, we wanted to analyze the impact practice-changing trial publications, national guideline updates, and publicly funded drug coverage plans may have on prescription patterns. Methods From national databases, we obtained data for ACS patients in the province of Ontario, Canada between 2008 and 2018. Using an interrupted-time series with data aggregated monthly, we evaluated types of P2Y12 inhibitor prescribed at hospital discharge and changes to antiplatelet prescription patterns following publication of Ticagrelor versus Clopidogrel in Patients with Acute Coronary Syndrome (PLATO), Canadian Cardiovascular Society (CCS) antiplatelet therapy guidelines, and ticagrelor coverage by a publicly funded medication plan. Results We included 114,142 ACS patients; 49% underwent PCI and 8% required CABG. Between October 2008 and March 2018, the proportion of patients discharged on P2Y12 inhibitors increased from 73.4% to 87% (p<0.0001) for PCI patients and 11.4% to 31.4% (p<0.0001) for CABG patients. PLATO publication was associated with a 1.3% (p=0.002) monthly decline in clopidogrel prescriptions amongst PCI patients. The 2010 CCS antiplatelet therapy guidelines were associated with a 0.7% (p<0.0001) monthly decline in clopidogrel prescriptions amongst PCI patients. The approval of ticagrelor by publicly funded medication plan was associated with an increase in ticagrelor prescriptions within the first month (24.5%; p<0.0001) and a continued monthly increase (0.4%; p<0.0001) in PCI patients. The approval was also associated with an increase in monthly ticagrelor prescriptions (0.2%; p<0.0001) amongst CABG patients. The 2012 CCS antiplatelet therapy guidelines were associated with a decline in clopidogrel prescriptions within the first month (6.1%; p=0.003) and a monthly increase in ticagrelor prescriptions (0.3%; p=0.05) amongst PCI patients. Conclusion Drug coverage by a publicly funded medication plan and guideline updates had significant impact on P2Y12 inhibitor prescription practices. Despite improvements, P2Y12 inhibitor prescriptions for CABG patients are far behind PCI patients. Further research is necessary to address barriers to appropriate antiplatelet therapy in the ACS population. Antiplatelet Prescription Patterns Funding Acknowledgement Type of funding source: Public hospital(s). Main funding source(s): New Investigator Fund - Hamilton Health Sciences Foundation, Hamilton, Canada

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)0.001
metaresearch head score (Gemma)0.005
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 score0.601
Threshold uncertainty score0.802

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0040.012
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.053
GPT teacher head0.310
Teacher spread0.256 · 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
Published2020
Admission routes2
Has abstractyes

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