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Bleeding and New Malignancy Diagnoses After Anticoagulation for Atrial Fibrillation: A Population-Based Cohort Study

2025· article· en· W4407789750 on OpenAlexaffabout
Kavi Grewal, Xuesong Wang, Peter C. Austin, Cynthia A. Jackevicius, Inbar Nardi-Admon, Dennis T. Ko, Douglas S. Lee, Paaladinesh Thavendiranathan, Michael G. Fradley, Paul Dorian, Husam Abdel‐Qadir

Bibliographic record

VenueCirculation · 2025
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsPublic Health OntarioToronto Public HealthUniversity of TorontoUniversity Health NetworkInstitute for Clinical Evaluative SciencesSunnybrook HospitalWomen's College Hospital
Fundersnot available
KeywordsMedicineMalignancyAtrial fibrillationWarfarinHazard ratioPopulationCohortRivaroxabanCancer registryCancerInternal medicineEmergency departmentSurgeryConfidence interval

Abstract

fetched live from OpenAlex

BACKGROUND: Bleeding after starting anticoagulation for atrial fibrillation (AF) may be the first sign of malignancy, especially in elderly individuals. There are no recommendations to guide investigations for malignancy after new-onset bleeding after anticoagulation for AF. Our objective was to determine the association of bleeding after starting oral anticoagulation for AF with new diagnoses of malignancy in a population-wide sample. METHODS: We conducted a population-based cohort study using linked administrative data sets of people ≥66 years of age who newly initiated warfarin or direct oral anticoagulants after diagnosis with AF between 2008 and 2022. Follow-up was 2 years after starting anticoagulation. We excluded patients with valvular disease, chronic dialysis, venous thromboembolism, previous cancer, or previously documented bleeding. Bleeding was identified from hospital/emergency department discharge records and physician billings, then handled as a time-varying covariate in cause-specific regression models while adjusting for baseline characteristics. The primary outcome was incident malignancy. We also determined the site of origin of the malignancy and the stage at diagnosis if indicated in the Ontario Cancer Registry. Analyses were repeated while limiting the exposure to specific bleeding sites. RESULTS: Among 119 480 people (mean age, 77.4 years; 52% men) who started anticoagulants, 26 037 (21.8%) had documented bleeding, and 5800 (4.9%) were diagnosed with malignancy within the next 2 years. Bleeding was associated with a higher hazard of cancer diagnosis with a hazard ratio (HR) of 4.0 (95% CI, 3.8–4.3). The HRs for any malignancy were 5.0 (95% CI, 4.6–5.5) for gastrointestinal, 5.0 (95% CI, 4.4–5.7) for genitourinary, 4.0 (95% CI, 3.5–4.6) for respiratory, 1.8 (95% CI, 1.4–2.2) for intracranial, and 1.5 (95% CI, 1.2–2.0) for nasopharyngeal bleeds. The HRs were substantially higher for cancers concordant with the bleeding site (gastrointestinal, 15.4; genitourinary, 11.8; respiratory, 10.1). Cancers were diagnosed at an earlier stage after bleeding (27.6% stage 4 after bleeding versus 31.3% without bleeding; P =0.029). CONCLUSIONS: In anticoagulated patients with AF, bleeding was strongly associated with new cancer diagnoses. Antecedent bleeding was associated with cancer diagnosis at an earlier stage. This highlights the importance of timely investigations in patients with bleeding after anticoagulation for AF, rather than attributing bleeding as an expected adverse effect.

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)0.002
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 score0.060
Threshold uncertainty score0.118

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
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 head0.048
GPT teacher head0.341
Teacher spread0.293 · 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

Citations12
Published2025
Admission routes2
Has abstractyes

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