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Record W4388594871 · doi:10.1093/eurheartj/ehad655.547

Trends in newly diagnosed atrial fibrillation and oral anticoagulant use after cardiac surgery: a population-based analysis from Ontario, Canada

2023· article· en· W4388594871 on OpenAlexafffundabout
J G Lee, Andrew C.T. Ha, Il Gyu Jeong, Jiming Fang, Maral Ouzounian, Peter C. Austin

Bibliographic record

VenueEuropean Heart Journal · 2023
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsInstitute for Clinical Evaluative SciencesUniversity of Toronto
FundersCanadian Cardiovascular Society
KeywordsMedicineAtrial fibrillationCardiac surgeryWarfarinInternal medicineCardiologyCohortDialysisPopulationSurgeryCoronary artery bypass surgeryCoronary artery diseaseArtery

Abstract

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Abstract Background Post-operative atrial fibrillation after cardiac surgery is associated with an increased risk of death and ischemic cardiovascular outcomes. In the non-surgical setting, oral anticoagulation (OAC) is beneficial among patients with AF in reducing their risk of thromboembolism. The use of OAC for patients with post-operative AF after cardiac surgery is not well-defined in contemporary clinical practice - at a time when direct oral anticoagulants (DOAC) are commonly prescribed in the non-surgical setting. Purpose To examine trends in OAC use among patients with post-operative atrial fibrillation after cardiac surgery at a time when DOAC is commonly prescribed in clinical practice. Methods The study cohort consisted of consecutive patients (>65 years) who experienced post-operative AF within 90 days after coronary artery bypass grafting (CABG), isolated left-sided non-mechanical heart valve surgery (iValve), and combined CABG + valve surgery between 2008 and 2021, identified by a mandatory provincial registry of all patients undergoing cardiac surgery in Ontario, Canada. Key exclusion criteria included: patients with pre-existing AF, end-stage renal disease requiring dialysis, mechanical heart valves, left atrial appendage ligation, and those who required anticoagulation for non-AF indications. Prescription of OAC (warfarin or DOAC) within 90 days after discharge from surgery was collected via linkage with the Ontario Drug Benefits Plan. Results The overall cohort consisted of 45,571 patients, in whom 14,572 (32.0%) developed POAF after cardiac surgery. Rates of POAF were highest among patients who underwent combined CABG + valve surgery (44.3% in 2008 to 34.2% in 2021), followed by iValve (42.6% in 2008 to 37.3% in 2021), then followed by isolated CABG (27.6% in 2008 to 24.2% in 2021). Over the study period, there was a decrease in the rates of POAF among patients who underwent all three types of cardiac surgery (P[trend] <0.01). In 2021, rates of oral anticoagulation were highest among patients who underwent combined CABG + valve surgery (61.1%), followed by iValve (59.2%) and isolated CABG (42.0%). Among patients who underwent isolated CABG, there was a 9% increase in the rates of OAC use during the study period (P[trend] <0.001). On the other hand, rates of OAC use remained largely unchanged (Figure A) among patients who underwent iValve and combined CABG + valve surgery (P[trend] 0.24 and 0.26, respectively). Since 2018, DOAC accounted for 60-70% of all OAC prescriptions (Figure B). Conclusions Despite advances in surgical techniques and post-operative care, the rates of post-operative atrial fibrillation after cardiac surgery remain substantial. Prescription rates for OAC remain modest over the past decade, but DOAC is increasingly more commonly prescribed in this patient population. Randomized trials are needed to assess the risks and benefits of OAC, specifically DOAC, in this high-risk patient population.Figure AFigure B

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.142

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.006
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.070
GPT teacher head0.307
Teacher spread0.238 · 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

Citations0
Published2023
Admission routes3
Has abstractyes

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