Frailty and oral anticoagulant prescription in adults with atrial fibrillation: A systematic review
Bibliographic record
Abstract
Abstract Objectives The objectives of this study were to determine the prevalence of frailty in the context of atrial fibrillation (AF); to identify the most commonly used frailty instruments in AF; and to describe the effect of frailty on non‐vitamin K oral anticoagulant (NOAC) prescription for stroke prevention in adults with AF. Methods A systematic search of databases, including Medline, Embase, Web of Science, Cochrane Library, Scopus, and CINAHL, was conducted using search terms including “atrial fibrillation,” “frailty,” and “anticoagulation.” A narrative synthesis was undertaken. Results A total of 92 articles were screened, and 12 articles were included. The mean age of the participants (n = 212,111) was 82 years (range = 77–85 years) with 56% of participants identified as frail and 44% identified non‐frail. A total of five different frailty instruments were identified: the Frailty Phenotype (FP; n = 5, 42%), the Clinical Frailty Scale (CFS; n = 4, 33%), Cumulative Deficit Model of Frailty (CDM; n = 1, 8%), Edmonton Frail Scale (n = 1, 8%) and the Resident Assessment Instrument – Minimum Data Set (RAI‐MDS 2.0; n = 1, 8%). Frailty was identified as an important barrier to anticoagulant therapy with 52% of the frail population anticoagulated vs 67% non‐frail. Conclusion Frailty is an important consideration in anticoagulation decision making for stroke prevention in patients with AF. There is scope to improve frailty screening and treatment. Frailty status is an important risk marker and should be considered when evaluating stroke risk alongside congestive heart failure, hypertension, age ≥75 years, diabetes mellitus, prior stroke, transient ischemic attack, or thromboembolism, vascular disease, age 65–74 years, sex category (CHA2DS2‐VASc) and Hypertension, Abnormal renal/liver function, Stroke, Bleeding, Labile, Elderly, and Drugs (HAS‐BLED) scores.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".