What is the Impact of Frailty on Prescription of Anticoagulation in Elderly Patients with Atrial Fibrillation? A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Atrial fibrillation (AF) and frailty are both associated with advanced age. Oral anticoagulants (OAC) effectively prevent strokes in AF patients but are underutilized in the elderly, possibly due to misperception of frailty. OBJECTIVE: We performed a systematic review to determine the prevalence of frailty in patients with AF, and whether frailty was associated with reduced prescription of OAC. METHODS: We systematically searched Cochrane, MEDLINE, EMBASE, and PubMed databases. Search terms combined relevant words and MeSH headings: 1) atrial fibrillation, 2) frail elderly, and 3) geriatric assessments. Studies that measured frailty using a validated instrument, and involved OAC for AF in frail and non-frail patients were eligible for inclusion. Pooled odds ratios were calculated using random-effects model. RESULTS: Of 166 reviewed titles, only 3 studies (1204 patients) met the inclusion criteria. Two used the Reported Edmonton Frail Scale (total 509 patients), and one used the Canadian Study of Health and Aging Clinical Frailty Scale (682 patients). All 3 studies involved hospitalized patients with an average age of 85 ± 6 and 45% were male. The weighted mean prevalence of frailty in patients with atrial fibrillation was 39% (95%CI 36-42). The weighted mean rate of OAC use was 57±11%. Frailty was associated with non-prescription of OAC compared to non-frail (OR 0.49, 95% CI 0.32-0.74, I2 =45%). CONCLUSION: The prevalence of frailty in hospitalized elderly patients with AF is high, and the use of OAC is low in these patients. Frail elderly are significantly less likely to receive OAC.
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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 | 0.019 | 0.048 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.044 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".