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Record W4307344820 · doi:10.1093/eurheartj/ehac587

Bleeding and ischaemic events after first bleed in anticoagulated atrial fibrillation patients: risk and timing

2022· article· en· W4307344820 on OpenAlexaff
Pascal Meyre, Steffen Blum, Elisa Hennings, Stefanie Aeschbacher, Tobias Reichlin, Nicolas Rodondi, Jürg H. Beer, Annina Stauber, Andreas Müller, Tim Sinnecker, Elisavet Moutzouri, Rebecca E. Paladini, Giorgio Moschovitis, Giulio Conte, Angelo Auricchio, Alexandra Ramadani, Matthias Schwenkglenks, Leo H. Bonati, Michael Kühne, Stefan Osswald, David Conen

Bibliographic record

VenueEuropean Heart Journal · 2022
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsMcMaster UniversityPopulation Health Research Institute
FundersPfizerDaiichi-SankyoSchweizerische HerzstiftungUniversität BaselSanofiSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsMedicineInterquartile rangeAtrial fibrillationHazard ratioBleedMyocardial infarctionStroke (engine)Internal medicineIncidence (geometry)Confidence intervalProspective cohort studySurgeryCardiology

Abstract

fetched live from OpenAlex

AIMS: To determine the risk of subsequent adverse clinical outcomes in anticoagulated patients with atrial fibrillation (AF) who experienced a new bleeding event. METHODS AND RESULTS: Anticoagulated AF patients were followed in two prospective cohort studies. Information on incident bleeding was systematically collected during yearly follow-up visits and events were adjudicated as major bleeding or clinically relevant non-major bleeding (CRNMB) according to the International Society on Thrombosis and Haemostasis guidelines. The primary outcome was a composite of stroke, myocardial infarction (MI), or all-cause death. Time-updated multivariable Cox proportional-hazards models were used to compare outcomes in patients with and without incident bleeding. Median follow-up was 4.08 years [interquartile range (IQR): 2.93-5.98]. Of the 3277 patients included (mean age 72 years, 28.5% women), 646 (19.7%) developed a new bleeding, 297 (9.1%) a major bleeding and 418 (12.8%) a CRNMB. The incidence of the primary outcome was 7.08 and 4.04 per 100 patient-years in patients with and without any bleeding [adjusted hazard ratio (aHR): 1.36, 95% confidence interval (CI): 1.16-1.61; P < 0.001; median time between a new bleeding and a primary outcome 306 days (IQR: 23-832)]. Recurrent bleeding occurred in 126 patients [incidence, 8.65 per 100 patient-years (95% CI: 7.26-10.30)]. In patients with and without a major bleeding, the incidence of the primary outcome was 11.00 and 4.06 per 100 patient-years [aHR: 2.04, 95% CI: 1.69-2.46; P < 0.001; median time to a primary outcome 142 days (IQR: 9-518)], and 59 had recurrent bleeding [11.61 per 100 patient-years (95% CI: 8.99-14.98)]. The incidence of the primary outcome was 5.29 and 4.55 in patients with and without CRNMB [aHR: 0.94, 95% CI: 0.76-1.15; P = 0.53; median time to a composite outcome 505 days (IQR: 153-1079)], and 87 had recurrent bleeding [8.43 per 100 patient-years (95% CI: 6.83-10.40)]. Patients who had their oral anticoagulation (OAC) discontinued after their first bleeding episode had a higher incidence of the primary composite than those who continued OAC (63/89 vs. 159/557 patients; aHR: 4.46, 95% CI: 3.16-6.31; P < 0.001). CONCLUSION: In anticoagulated AF patients, major bleeding but not CRNMB was associated with a high risk of adverse outcomes, part of which may be explained by OAC discontinuation. Most events occurred late after the bleeding episode, emphasizing the importance of long-term follow-up in these patients.

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)0.008
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
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 head0.301
Teacher spread0.253 · 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

Citations28
Published2022
Admission routes1
Has abstractyes

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