Impact of atrial fibrillation on the risk of major adverse cardiac events following coronary revascularisation
Bibliographic record
Abstract
OBJECTIVE: Atrial fibrillation (AF) remains a highly prevalent arrhythmia with significant burden on morbidity and mortality. The impact of AF in the revascularised population remains incompletely described. Given the high prevalence of AF in the revascularised population, we sought to evaluate the incidence and prognosis in patients with pre-existing and new-onset AF following revascularisation. METHODS: We used the University of Ottawa Heart Institute Revascularisation Registry to identify patients who underwent revascularisation between August 2015 and March 2020, who were prospectively followed for an average of one year. We conducted a retrospective cohort study analysing the association between AF and clinical outcomes. The primary outcome of interest was 1-year major adverse cardiac events (MACE) defined as a composite of death, myocardial infarction, unplanned revascularisation and cerebrovascular accidents. Moreover, secondary outcomes include the individual components of MACE and bleeding. RESULTS: A total of 6704 patients underwent revascularisation and completed 1-year clinical follow-up. Median time to follow-up was 12.8 (IQR 11.2-15.9) months. One-year MACE occurred in 166 (21.8%) and 683 (11.5%) patients in AF and non-AF groups, respectively (adjusted HR, 1.61; 95% CI 1.29 to 2.01; p<0.0001). AF was independently predictive of 1-year mortality, myocardial infarction, unplanned revascularisation, cerebrovascular accident and bleeding. Within 1 year, 299 (4.5%) episodes of new-onset AF was observed. New-onset AF following revascularisation was also associated with 1-year MACE, mortality, myocardial infarction, cerebrovascular accident and unplanned revascularisation. CONCLUSIONS: Preprocedural and new-onset AF following revascularisation remains highly predictive 1-year MACE. AF should be considered in addition to traditional risk factors for adverse outcomes following revascularisation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".