The Introduction of Direct Oral Anticoagulants Has Not Resolved Treatment Gaps for Frail Patients With Nonvalvular Atrial Fibrillation
Bibliographic record
Abstract
Background The extent to which the introduction of direct oral anticoagulants (DOACs) influenced treatment patterns in frail and nonfrail patients with nonvalvular atrial fibrillation (NVAF) is unclear. Methods This was a retrospective cohort study of all Albertans 20 years or older who were discharged from an emergency department or hospital with a new diagnosis of NVAF between April 1, 2009, and March 31, 2019. The Hospital Frailty Risk Score was used to define frailty and the CHA 2 DS 2 -VASc and CHADS-65 scores were used to identify if anticoagulation was indicated. Results Among 75,796 patients (median age, 75 years; 45% female) with a new diagnosis of NVAF, 17,143 (22.6%) were frail. Although guideline criteria for anticoagulation were more commonly met by frail patients than nonfrail patients (92.1% vs 74.2%, for CHA 2 DS 2 -VASc, and 96.8% vs 85.8% for CHADS-65; both P < 0.0001), frail patients were less likely to receive any anticoagulant, even after those with contraindications to anticoagulation were excluded (adjusted odds ratio, 0.61; 95% confidence interval, 0.58-0.64). After DOACs became available, anticoagulant prescribing for patients with guideline indications increased more in nonfrail patients (from 42.4% to 68.2%) than in frail patients (from 29.0% to 52.2%) and frail patients were less likely to receive a DOAC than warfarin (adjusted odds ratio, 0.66; 95% confidence interval, 0.54-0.81). Conclusions Although they stand to potentially derive greater benefits from anticoagulation, frail patients were less likely to receive an anticoagulant and, if anticoagulated, they were more likely to receive warfarin than a DOAC. The introduction of DOACs has increased anticoagulation rates but not resolved treatment gaps for frail patients with NVAF.
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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 | 0.003 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".