Symptomatic atrial fibrillation and risk of cardiovascular events: data from the Euro Heart Survey
Bibliographic record
Abstract
AIMS: Atrial fibrillation (AF) is associated with a wide range of clinical presentations. Whether and how AF symptoms can affect prognosis is still unclear. Aims of the present analysis were to investigate potential predictors of symptomatic AF and to determine if symptoms are associated with higher incidence of cardiovascular (CV) events at 1-year follow-up. METHODS AND RESULTS: The Euro Heart Survey on Atrial Fibrillation included 3607 consecutive patients with documented AF and available follow-up regarding symptoms status. Patients found symptomatic at baseline were classified into still symptomatic (SS group; n = 896) and asymptomatic (SA; n = 1556) at 1 year. Similarly, asymptomatic patients at baseline were classified into still asymptomatic (AA group; n = 903) and symptomatic (AS group; n = 252) at 1 year. Demographics, as well as clinical variables and medical treatments, were tested as potential predictors of symptoms persistence/development at 1-year. We also compared CV events between SS and SA groups, and AS and AA groups at 1-year follow-up. Both persistence and development of AF symptoms were associated with an increased risk of CV hospitalization, stroke, heart failure worsening, and thrombo-embolism. AF type, hypothyroidism, chronic heart failure, and chronic obstructive pulmonary disease (COPD), were independently associated with an increased risk of symptomatic status at 1-year follow-up between SS and SA groups. CONCLUSION: Persistence or development of symptoms after medical treatment are associated with an increased risk of CV events during a 1-year follow-up. Type of AF, along with hypothyroidism, COPD and chronic heart failure are significantly associated with symptoms persistence despite medical treatment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".