Trends in US Hospitalization Rates and Rhythm Control Therapies Following Publication of the AFFIRM and RACE Trials
Bibliographic record
Abstract
INTRODUCTION: The impact of trials comparing rate versus rhythm control for AF on subsequent use of rhythm control therapies and hospitalizations at a national level has not been described. METHODS AND RESULTS: We queried the Healthcare Cost & Utilization Project on the frequency of hospital admissions and performance of specific rhythm control procedures from 1998-2006. We analyzed trends in hospitalization for AF as principal diagnosis before and after the publication of key rate versus rhythm trials in 2002. We also reviewed the use of electrical cardioversion and catheter ablation as principal procedures during hospital admissions for any cause and for AF as principal diagnosis. We additionally appraised the overall outpatient utilization of antiarrhythmic drugs during this same time frame using IMS Health's National Prescription Audit.™ Admissions for AF as a principal diagnosis increased at 5%/year from 1998-2002. Following publication of the AFFIRM and RACE trials in 2002, admissions declined by 2%/year from 2002-2004, before rising again from 2004-2006. In-hospital electrical cardioversion followed a similar pattern. National prescription volumes for antiarrhythmic drugs grew at <1% per year from 2002 to 2006, with a marked decline in the use of class I-A agents, while catheter ablations during admissions for AF as the principal diagnosis increased at 30% per year. CONCLUSION: The use of rhythm control therapies in the US declined significantly in the first few years after publication of AFFIRM and RACE. This trend reversed by 2005, at which time rapid growth in the use of catheter ablation for AF was observed.
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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 | 0.022 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".