Frailty phenotype as a predictor of bleeding and mortality in ambulatory patients receiving direct oral anticoagulants
Bibliographic record
Abstract
BACKGROUND: Limited prospective data exist about the clinical relevance of frailty in patients with atrial fibrillation (AF) or venous thromboembolism (VTE) receiving direct oral anticoagulants (DOACs). The aim of this study was to evaluate whether frailty phenotype identifies DOAC-treated patients at higher risk of adverse clinical outcomes. METHODS: Consecutive, adult outpatients treated with DOACs for AF or VTE were prospectively enrolled. Patients were classified as frail, pre-frail, or non-frail according to frailty phenotype. Study outcomes were clinically relevant bleeding, including major and clinically relevant non-major bleeding, arterial and venous thromboembolism, and all-cause mortality. RESULTS: 236 patients (median age 78 years, 44% females) were included, of whom 156 (66%) had AF and 80 (34%) VTE. Ninety-eight (41%) patients were frail, 115 (49%) pre-frail, and 23 (10%) non-frail. Inappropriately high or low dose DOAC was used in 33% of frail and in 20% of non-frail or pre-frail patients. Over a median follow-up of 304 days, the incidence of clinically relevant bleeding, thromboembolism, and mortality were 20%, 4%, 9% in frail, and 10%, 3%, and 2% in pre-frail, respectively, while no study outcome occurred among non-frail patients. Risk ratios (95% confidence intervals) for these outcomes in frail versus pre-frail and non-frail patients were respectively 2.5 (1.8, 3.7), 1.9 (0.9, 4.0), and 6.3 (2.9, 13.6). CONCLUSION: In a prospective cohort of ambulatory patients receiving DOAC treatment for AF or VTE, frailty phenotype identified patients at higher risk of bleeding and all-cause mortality. Frailty assessment could be valuable to guide targeted interventions potentially improving patient prognosis.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".