Frailty to predict unplanned hospitalization, stroke, bleeding, and death in atrial fibrillation
Bibliographic record
Abstract
AIMS: Atrial fibrillation (AF) and frailty are common, and the prevalence is expected to rise further. We aimed to investigate the prevalence of frailty and the ability of a frailty index (FI) to predict unplanned hospitalizations, stroke, bleeding, and death in patients with AF. METHODS AND RESULTS: Patients with known AF were enrolled in a prospective cohort study in Switzerland. Information on medical history, lifestyle factors, and clinical measurements were obtained. The primary outcome was unplanned hospitalization; secondary outcomes were all-cause mortality, bleeding, and stroke. The FI was measured using a cumulative deficit approach, constructed according to previously published criteria and divided into three groups (non-frail, pre-frail, and frail). The association between frailty and outcomes was assessed using multivariable-adjusted Cox regression models. Of the 2369 included patients, prevalence of pre-frailty and frailty was 60.7% and 10.6%, respectively. Pre-frailty and frailty were associated with a higher risk of unplanned hospitalizations [adjusted hazard ratio (aHR) 1.82, 95% confidence interval (CI) 1.49-2.22; P < 0.001; and aHR 3.59, 95% CI 2.78-4.63, P < 0.001], all-cause mortality (aHR 5.07, 95% CI 2.43-10.59; P < 0.001; and aHR 16.72, 95% CI 7.75-36.05; P < 0.001), and bleeding (aHR 1.53, 95% CI 1.11-2.13; P = 0.01; and aHR 2.46, 95% CI 1.61-3.77; P < 0.001). Frailty, but not pre-frailty, was associated with a higher risk of stroke (aHR 3.29, 95% CI 1.2-8.39; P = 0.01). CONCLUSION: Over two-thirds of patients with AF are pre-frail or frail. These patients have a high risk for unplanned hospitalizations and other adverse events. These findings emphasize the need to carefully evaluate these patients. However, whether screening for pre-frailty and frailty and targeted prevention strategies improve outcomes needs to be shown in future studies. CLINICAL TRIAL REGISTRATION: Clinicaltrials.gov identifier number: NCT02105844.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".