Catheter ablation for supraventricular tachycardia and health resource utilization and expenditures: A propensity-matched cohort study
Bibliographic record
Abstract
BACKGROUND: Few data are available regarding temporal patterns of health resource utilization (HRU) and expenditures among patients undergoing catheter ablation for paroxysmal supraventricular tachycardia (PSVT). This study aimed to describe expenditures and HRU in patients with PSVT who underwent catheter ablation compared to a matched cohort of patients on medical therapy alone. METHODS: Using a large US administrative database, we identified adult patients (age 18 to 65 years) with a new PSVT diagnosis between 2008 and 2016. Propensity-score matching was used to assemble a PSVT cohort treated with ablation or medical therapy alone (N = 2556). Longitudinal trends in HRU and expenditures in the 3-years preceding and following PSVT diagnosis were compared. RESULTS: There were no significant differences in expenditures between groups except within the first year after PSVT diagnosis: $48,004 ablation vs. $17,560 medical therapy (p < 0.001). This difference was driven by procedural expenditures, where the mean cost of catheter ablation was $32,057 ± SD 26,737. In Years 2 and 3 post-ablation, HRU and expenditures decreased to the levels associated with the medical therapy group, although fewer ablation patients required any prescription for beta-blockers, calcium channel blockers, or anti-arrhythmic drugs (32% ablation vs. 42% medical therapy group, p < 0.001). CONCLUSION: Catheter ablation reduces medication burden in PSVT, yet health resource use and expenditures were similar beyond 2 years post-ablation when compared to PSVT patients on medical therapy alone. Additional studies are required to better understand drivers of these sustained health expenditures, and barriers to achieving cost-savings for a potentially curative procedure.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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) | 0.002 | 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".