Adverse outcomes in patients with atrial fibrillation and a pacemaker: a cohort study
Bibliographic record
Abstract
AIMS: Patients with atrial fibrillation (AF) are at a high risk of adverse cardiovascular outcomes. Little is known about the specific population of AF patients with implanted pacemaker (PM) and their prognosis. Therefore, we aimed to compare the risks of adverse outcomes in AF patients with and without PM. METHODS AND RESULTS: Data from two Swiss prospective, multicentre cohort studies (Swiss-AF, Beat-AF) (n = 3675) with yearly follow-ups (FUs) up to 8 years were analysed. The first main outcome was major adverse cardiovascular events (MACE), a composite of stroke or transient ischaemic attack, myocardial infarction, cardiovascular death, and systemic embolism. The second main outcome was hospitalization for heart failure (HF). Secondary outcomes were the individual components of MACE. We performed time-updated Cox regression analyses to investigate the association of PM and outcomes. Median age was 71.4 years, 28.8% female, 445 (12.1%) patients had a PM at baseline, and 238 additional patients (7.4%, 1.05%/year) received a PM over a median FU of 7 years. Patients with a PM had higher incidence rates for MACE and HF (5.97 and 5.08 per 100 patient-years, respectively), compared to patients without a PM (3.37 and 2.61 per 100 patient-years, respectively). After multivariable adjustment, we found no independent association of PM and MACE (aHR [95% CI] 1.12 [0.95-1.33; P = 0.183]) or HF (aHR [95% CI] 1.14 [0.94-1.37; P = 0.180]). We found consistent results for the individual components of MACE. CONCLUSION: Patients with AF and a PM experienced an increased rate of adverse cardiovascular outcomes. However, the PM itself was not independently associated with these outcomes.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".