The cost-effectiveness of Maze procedures using ablation techniques at the time of mitral valve surgery
Bibliographic record
Abstract
BACKGROUND: The classic cut and sew surgical Maze procedure has been shown to reduce atrial fibrillation (AF), and recently newer surgical ablation techniques with reduced technical complexity have been introduced. This study sought to systematically review the efficacy and safety of these newer techniques and to evaluate their long-term cost-effectiveness at the time of scheduled mitral valve (MV) surgery. METHODS: A systematic literature search and meta-analysis was performed to generate the most reliable efficacy and safety parameter estimates for a Markov decision analysis model comparing MV surgery alone to MV surgery plus an ablation Maze procedure. Both basic and probabilistic sensitivity analyses were conducted. RESULTS: Based on the six randomized controlled trials (RCTs) identified, the pooled 1-year estimate of AF after surgery alone was 71 percent (95 percent confidence interval [CI], 64 percent to 78 percent). The pooled risk ratio of AF after surgical ablation treatment at the time of mitral valve surgery relative to valve surgery alone was 0.33 (95 percent CI, 0.19 to 0.55). The pooled analyses showed that no statistical significant increases in operative mortality, permanent pacemaker implantation, and postoperative bleeding with the ablative Maze procedures. An ablation-based Maze procedure at the time of mitral valve surgery had an incremental cost-effectiveness ratio (ICER) of $4,446CAD ($3,850US) per quality-adjusted life-year (QALY) compared with valve surgery alone. Specifically costs were an extra $900CAD but with improved clinical outcomes (0.20 QALYs), including a prolonged life expectancy of 0.10 life-years. In one-way sensitivity analyses, survival time after MV surgery had the largest impact on ICER. Other variables influencing the ICER included the risk ratio of AF, utility, and cost estimates. Probabilistic sensitivity analysis suggests that 58.1 percent, 73.9 percent, and 89.3 percent of the simulations of the Maze strategy are cost-effective at willingness to pay thresholds of $20,000, $50,000, and $100,000CAD per QALY gained, respectively. CONCLUSIONS: Our meta-analysis suggests that a Maze surgical ablation procedure at the time of MV surgery is associated with a reduced postoperative AF risk. Our economic model further suggests that the surgical ablation strategy at the time of mitral valve surgery is likely a cost-effective intervention, provided patients have a good long-term postsurgical prognosis.
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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 | 0.015 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.020 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".