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Record W7128081926 · doi:10.1093/eurheartj/ehaf784.528

Regional differences in clinical outcomes in patients of asian race with atrial fibrillation randomized to direct oral anticoagulants vs warfarin: a patient-level meta-analyses from COMBINE AF

2025· article· en· W7128081926 on OpenAlexaff
T F Chao, E Braunwald, M G Palazzolo, E M Antman, A P Carnicelli, J W Eikelboom, C B Granger, S Goto, E T Kato, G Y H Lip, M R Patel, C T Ruff, L Wallentin, R P Giugliano

Bibliographic record

VenueEuropean Heart Journal · 2025
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsMcMaster University
Fundersnot available
KeywordsAtrial fibrillationEast AsiaWarfarinMajor bleedingRace (biology)Randomized controlled trialStroke (engine)Far East

Abstract

fetched live from OpenAlex

Abstract Background A previous report from the COMBINE AF database (A Collaboration Between Multiple Institutions to Better Investigate Non-Vitamin K Antagonist Oral Anticoagulant Use in Atrial Fibrillation), which includes data from RE-LY, ROCKET AF, ARISTOTLE, and ENGAGE AF-TIMI 48, demonstrated that the advantages of direct oral anticoagulants (DOACs) over warfarin were more evident in Asian patients with atrial fibrillation (AF) than in non-Asians. However, Asia is a vast region, and randomized data comparing clinical outcomes among AF patients of Asian race across different regions within Asia remain limited. Methods Using the COMBINE AF database, we performed an individual patient-level meta-analysis to compare clinical outcomes in patients of Asian race from 4 regions: East Asia (China, Hong Kong, Taiwan), Northeast (NE) Asia (Japan, South Korea), Southeast (SE) Asia (Malaysia, Philippines, Singapore, Thailand) and South Asia (India). Results There were 9,943 pts (64% males) of Asian race enrolled from 4 Asian regions (East: 3,388, NE: 2,751, SE: 1,673, South: 2,131) randomized to warfarin or DOACs. Patients in East Asia were older (median age: East 71 yrs, NE 70 yrs, SE 67, South 65; p<0.001), heavier (mean body weight: East 68.5kg, NE 65.7kg, SE 65.9kg, South 63.1kg; p<0.001) and had a higher prevalence of past history of stroke or transient ischemic attack (East 44.2%, NE 31%, SE 42%, South 31.1%; p<0.001) than those of other regions. The median times in therapeutic range with warfarin varied widely by regions in Asia (NE 62.5%, SE 60.1%, East 57.1%, South 48.6%; p<0.001). Compared to East Asia, regional differences for several clinical outcomes were observed (Fig 1). NE Asia had a lower risk of stroke/systemic embolic events (SEE)(adjusted hazard ratio [aHR] 0.70), ischemic stroke (0.73), intracranial hemorrhage (ICH)(0.62) and net clinical outcome (NCO: stroke/SEE, major bleeding [MB], or death)(0.79) compared to East Asia (all p values <0.05). SE Asia had a higher risk of NCO (aHR 1.27), while South Asia had a lower risk of stroke/SEE (0.73), ischemic stroke (0.68) but a higher risk of NCO (1.57) compared to East Asia (all p values <0.05). Compared to warfarin, standard-dose (SD) DOACs significantly reduced the risks of stroke/SEE, MB, ICH, CV death and the NCO to a similar degree across Asian regions (each Pint >0.1, Fig 2). SD DOACs significantly reduced the risk of ischemic stroke in SE and South Asia, but not in East and NE Asia (Pint 0.013). The risk of GI bleeding with SD DOAC and warfarin were similar in Asia regions, with no heterogeneity (Pint 0.13). Conclusions Although the adjusted risks of clinical events in AF patients of Asian race differed significantly between Asian regions, the reductions in stroke/SEE, MB, ICH, and CV death with SD DOACs compared to warfarin were consistent across Asian regions.

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)0.013
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.990
Threshold uncertainty score0.069

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.020
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0100.048
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.294
GPT teacher head0.426
Teacher spread0.132 · 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.

Study designMeta-analysis
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
Published2025
Admission routes1
Has abstractyes

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