Identifying Predictors of Cumulative Healthcare Costs in Incident Atrial Fibrillation: A Population‐Based Study
Bibliographic record
Abstract
BACKGROUND: Atrial fibrillation (AF) has substantial impacts on healthcare resource utilization. Our objective was to understand the pattern and predictors of cumulative healthcare costs in AF patients after incident diagnosis in an emergency department (ED). METHODS AND RESULTS: Patients discharged after a first presentation of AF to an ED in Ontario, Canada, were identified from April 1, 2005, through March 31, 2010. Per-patient cumulative healthcare costs were determined until death or March 31, 2012. Join-point analyses identified clinically relevant cost phases. Hierarchical generalized linear models with a logarithmic link and gamma distribution determined predictors of cost per phase. Our cohort was 17 980 patients. During a mean follow-up of 3.9 years, 17.1% of patients died. Three distinct cost phases were identified: 2-month post-index ED visit phase, 12-month predeath phase, and a stable/chronic phase. The mean cost per patient in the first month post-index ED visit was $1876 (95% CI $1822 to $1931), $8050 (95% CI $7666 to $8434) in the month before death, and $640 (95% CI $624 to $655) per month for the stable/chronic phase. The main cost component in the post-index phase was physician services (32% of all costs) and hospitalizations for the predeath phase (72% of all costs). The CHA2DS2-VASc clinical risk score was a strong predictor of costs (rate ratio 1.91 and 5.08 for score of 7 versus score of 0 in predeath phase and postindex phase, respectively). CONCLUSIONS: There are distinct phases of resource utilization in AF, with highest costs in the predeath phase.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".