Healthcare Resource Utilization and Costs Associated with Recurrent Episodes of Atrial Fibrillation: The FRACTAL Registry
Bibliographic record
Abstract
INTRODUCTION: Drivers of cost in the atrial fibrillation (AF) population are not fully understood. We sought to characterize the resource utilization and costs of treating new-onset AF, with emphasis on the incremental costs associated with recurrent episodes of AF over time. METHODS AND RESULTS: An inception cohort of 973 AF patients was followed at 3-6 month intervals in an observational registry over a mean of 24 +/- 9 months. AF therapies, clinical outcomes, and both inpatient and outpatient medical resource utilization were tracked at each follow-up interval. Registry patients were managed primarily with cardioversion and pharmacological therapy. Direct healthcare costs were calculated from a U.S. perspective by multiplying measures of resource utilization by representative price weights. Costs were compared among patients in whom the initial episode of AF became permanent and patients who initially achieved sinus rhythm and had either 0, 1-2, or > or = 3 documented recurrences during follow-up. Mean annual costs for these four groups were $2,372, $3,385, $6,331, and $10,312 per patient per year, respectively (P < 0.001 for trend), with the largest variation related to hospital costs. In multivariable analysis controlling for demographic characteristics and baseline cardiac and comorbid conditions, each documented recurrence of AF was found to increase annual healthcare costs by approximately $1,600. CONCLUSION: Following initial diagnosis, patients with AF treated with traditional therapies incur $4,000-$5,000 in annual direct healthcare costs. Costs are markedly higher in patients with multiple AF recurrences. These data may be helpful in evaluating the economic impact of new technologies for treating AF.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".