P3782Frailty to predict unplanned hospitalizations, stroke, bleeding and death in atrial fibrillation
Bibliographic record
Abstract
Abstract Aim We investigated the prevalence of frailty, and the relationships between frailty and the risk of adverse clinical outcomes in patients with atrial fibrillation (AF). Methods Patients with known AF were enrolled in a nation-wide observational cohort study in Switzerland. Information on medical history, medication, lifestyle factors and clinical measurements were obtained. The primary outcome was unplanned hospitalizations, secondary outcomes were all-cause mortality, bleeding and stroke. The frailty index (FI) was measured using a cumulative deficit approach according to previously published criteria. Participants were divided into three groups (non-frail, pre-frail and frail) according to their FI at study entry. The association between frailty and clinical outcomes was assessed using multivariable adjusted Cox proportional hazard models. Results We included 2369 patients with a mean age of 73±8 years (27.3% female). The prevalence of frailty and pre-frailty was 10.6% and 60.7%, respectively. Frailty was associated with unplanned hospitalization (adjusted hazard ratio [HR] 3.59; 95% confidence interval [95% CI], 2.78–4.63; p<0.001), all-cause mortality (adjusted HR 16.72; 95% CI 7.75–36.05; p<0.001), bleeding (adjusted HR 2.46; 95% CI 1.61–3.77; p<0.001), and stroke (adjusted HR 3.29; 95% CI 1.29–8.39; p=0.01) (Figure). Similarly, pre-frailty was significantly associated with unplanned hospitalization (adjusted HR 1.82; 95% CI 1.49–2.22; p<0.001), all-cause mortality (adjusted HR 5.07; 95% CI 2.43–10.59; p<0.001) and bleeding (adjusted HR 1.53; 95% CI 1.11–2.13; p=0.01), but not with stroke. Cumulative incidence of adverse events Conclusion In our cohort, more than two thirds of AF patients were either pre-frail or frail. These patients have a high risk of unplanned hospitalizations and other adverse outcomes, indicating that frailty is a powerful tool to predict adverse clinical outcomes in AF patients. Acknowledgement/Funding Swiss National Science Foundation; Foundation for Cardiovascular Research Basel; University of Basel
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".