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Record W4403818025 · doi:10.1093/eurheartj/ehae666.482

Anticoagulation and stroke in hospitalized patients newly diagnosed with secondary atrial fibrillation: a population-based cohort study

2024· article· en· W4403818025 on OpenAlexafffund
Husam Abdel‐Qadir, Madison Gunn, Jiming Fang, TinuolaO Odugbemi, Peter Austin, Paul Dorian, Cynthia A. Jackevicius, Douglas S. Lee, Sheldon M. Singh, Karen Tu, Dennis T. Ko

Bibliographic record

VenueEuropean Heart Journal · 2024
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsThe Scarborough HospitalSunnybrook Health Science CentreInstitute for Clinical Evaluative SciencesHealth Sciences CentreUniversity of TorontoUniversity Health NetworkWomen's College Hospital
FundersCanadian Cardiovascular Society
KeywordsMedicineAtrial fibrillationStroke (engine)Internal medicineCohortCardiologyCohort studyPopulation

Abstract

fetched live from OpenAlex

Abstract Background Atrial fibrillation (AF) can be triggered by acute precipitants, with reversion to sinus rhythm after the stressor resolves. This has been described as "secondary AF", to differentiate it from AF without acute provocation ("primary AF"). Objective To describe anticoagulation patterns and the risk of stroke following hospitalization with a new diagnosis of secondary AF, with comparison to a first hospitalization for primary AF. Methods We created a cohort of adults aged ≥66 years who were discharged alive from hospital after a first diagnosis of AF between April 1, 2013, and March 31, 2019. The main exposure was "secondary" AF, with "primary" AF serving as the comparator group. This was based on a validated approach utilizing the AF discharge diagnosis type. Patients were followed for 1 year. We used drug dispensation records to determine the proportion of people anticoagulated after discharge from hospital. We also identified hospitalizations for stroke. We used the cumulative incidence function to estimate the risk of stroke while not anticoagulated, while censoring on dispensation of anticoagulation and treating death as a competing risk. Inverse probability of censoring weights were used to reduce bias from informative censoring. Cause-specific hazards regression was used to estimate the hazard ratio (HR) for stroke associated with secondary AF (relative to primary AF) while accounting for differences in baseline characteristics and time-varying anticoagulation status. Results We identified 13,011 people with secondary AF and 11,065 with primary AF. People with primary AF were older (mean 79.5 yrs vs. 77.1 yrs for secondary AF), more likely to be female (58.6% vs. 42.3% for secondary AF), and more likely to have prior HF. Secondary AF was positively associated with prior bleeding, diabetes, ischemic heart disease, and peripheral vascular disease. Mortality was higher for people with secondary vs. primary AF: 1.7% (95%CI 1.5%-2.0%) vs. 0.7% (95%CI 0.6%-0.9%) respectively at 7 days, 4.2% (95%CI 3.9%-4.6%) vs. 2.5% (95%CI 2.2%-2.8%) at 30 days, and 16.1% (95%CI 15.5%-16.7%) vs. 14.6% (13.9%-15.2%) at 1-year post-discharge. The proportion of people who were dispensed anticoagulants within 7 days of discharge was lower for secondary than primary AF (26.9% vs. 68%), and this continued to be lower at one year (41.7% vs. 82.3%, p<0.001 for both comparisons). At 1 year, the risk of stroke in people who were not anticoagulated was 2.2% (95% CI 1.6-2.8%) after primary AF and 1.2% (95% CI 1.0-1.4%) after secondary AF (p< 0.001). Relative to patients with primary AF, the hazard of stroke (adjusted for baseline characteristics and time-varying anticoagulation status) was lower in patients with secondary AF (HR 0.74; CI 0.57-0.97; p= 0.03). Conclusion Secondary AF is associated with higher mortality, less anticoagulation, and a lower risk/ hazard of stroke than primary AF. These data can inform post-discharge care in people with secondary AF.Proportion anticoagulated over timeRisk of stroke without anticoagulation

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 score0.019
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.029
GPT teacher head0.311
Teacher spread0.281 · 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
Published2024
Admission routes2
Has abstractyes

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