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Record W4388595766 · doi:10.1093/eurheartj/ehad655.565

Year-to-year change of antithrombotic strategy and clinical outcomes of atrial fibrillation patients with vascular disease in this decade: The Fushimi AF Registry

2023· article· en· W4388595766 on OpenAlexaboutno aff
Nobutoyo Masunaga, Kenjiro Ishigami, Syuhei Ikeda, K Doi, Takashi Yoshizawa, Yasuhiro Hamatani, Kaori Ide, A Fijino, Mitsuru Ishii, Moritake Iguchi, Masahiro Esato, Hiromichi Wada, K Hasesgawa, Mitsuru Abe, Masaharu Akao

Bibliographic record

VenueEuropean Heart Journal · 2023
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsnot available
FundersJapan Agency for Medical Research and Development
KeywordsMedicineAtrial fibrillationAntithromboticGuidelineInternal medicineStroke (engine)Coronary artery diseaseMedical prescriptionCanadian Cardiovascular SocietyConcomitantDiseaseCardiologyMyocardial infarction

Abstract

fetched live from OpenAlex

Abstract Background Many atrial fibrillation (AF) patients have concomitant vascular diseases, and therefore, antithrombotic therapy is crucial for prevention of cardiovascular events in AF patients. In the last decade, antithrombotic therapy of AF patients has undergone major change due to launch of direct oral anticoagulants (DOAC). After the release of DOAC, several national guidelines for the management of AF were updated. The Japanese Circulation Society published the new guideline for the management of AF incorporating the recommendation of DOAC in 2014, which made a great impact on clinical practice in Japan. Purpose In this study, we investigated the year-to-year change of antithrombotic therapy of AF patients with vascular disease from 2011 to 2021 and compared the clinical characteristics and outcomes of those patients enrolled before and after the guideline (2011-2013 vs. 2014-2021) by using data of the Fushimi AF Registry. Methods The Fushimi AF Registry, a community-based prospective survey, was designed to enroll all of the AF patients who visited the participating medical institutions in Fushimi-ku, Kyoto, Japan. We started to enroll patients from March 2011 and follow up data including prescription status were available in 4,464 patients from March 2011 to August 2021. We defined vascular disease as coronary artery disease, peripheral artery disease, ischemic stroke or transient ischemic attack. Results In 2011, 61% of AF patients with vascular disease received OAC and 53% received antiplatelet drug (APD). Proportion of the patients receiving OAC increased year by year and that receiving APD decreased year by year. In 2021, 76% of the patients received OAC and 31% received APD. Proportion of the patients with combination of OAC and APD was 27% in 2011 and decreased year by year. In 2021, 18% received the combination therapy. In 2011, when the first DOAC was launched, 2% of the patients received DOAC and 59% received warfarin. After that, proportion of the patients receiving DOAC increased progressively. In 2021, 55% of patients received DOAC and 21% of patients received warfarin. Of 4,464 patients, 1,386 patients (31.0% of the entire cohort) had history of vascular disease at enrollment. Of 1,386 patients, 1,120 patients were enrolled before 2013 and 266 patients were enrolled after 2014. The incidence of composite of cardiac death, stroke or myocardial infarction was similar between the two groups (patients enrolled before 2013 vs. patients enrolled after 2014: 5.0 vs. 4.1 per 100 person-years; log-rank p=0.27). The incidence of major bleeding tended to be lower in patients enrolled after 2014 than those enrolled before 2013 although it was not statistically significant (2.8 vs. 2.0 per 100 person-years; log-rank p=0.15). Conclusion Antithrombotic strategy of AF patient with vascular disease has undergone major change in this decade, but the incidences of cardiovascular events of those patients have not significantly changed.

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)0.003
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 score0.018
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.125
GPT teacher head0.376
Teacher spread0.251 · 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
Published2023
Admission routes1
Has abstractyes

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