Cost-Effectiveness of Mitral Valve Repair Versus Replacement for Severe Ischemic Mitral Regurgitation
Bibliographic record
Abstract
BACKGROUND: The CTSN (Cardiothoracic Surgical Trials Network) recently reported no difference in left ventricular end-systolic volume index or in survival at 2 years between patients with severe ischemic mitral regurgitation (MR) randomized to mitral valve repair or replacement. However, replacement provided more durable correction of MR and fewer cardiovascular readmissions. Yet, costeffectiveness outcomes have not been addressed. METHODS AND RESULTS: We conducted a cost-effectiveness analysis of the surgical treatment of ischemic MR based on the CTSN trial (n=126 for repair; n=125 for replacement). Patient-level data on readmissions, survival, qualityof- life, and US hospital costs were used to estimate costs and quality-adjusted life years per patient over the trial duration and a 10-year time horizon. We performed microsimulation for extrapolation of outcomes beyond the 2 years of trial data. Bootstrap and deterministic sensitivity analyses were done to address parameter uncertainty. In-hospital cost estimates were $78 216 for replacement versus $72 761 for repair (difference: $5455; 95% uncertainty interval [UI]: −7784–21 193) while 2-year costs were $97 427 versus $96 261 (difference: $1166; 95% UI: −16 253–17 172), respectively. Quality-adjusted life years at 2 years were 1.18 for replacement versus 1.23 for repair (difference: −0.05; 95% UI: −0.17 to 0.07). Over 5 and 10 years, the benefits of reduction in cardiovascular readmission rates with replacement increased, and survival minimally improved compared with repair. At 5 years, cumulative costs and quality-adjusted life years showed no difference on average, but by 10 years, there was a small, uncertain benefit for replacement: $118 023 versus $119 837 (difference: −$1814; 95% UI: −27 144 to 22 602) and qualityadjusted life years: 4.06 versus 3.97 (difference: 0.09; 95% UI: −0.87 to 1.08). After 10 years, the incremental cost-effectiveness of replacement continued to improve. CONCLUSIONS: Our cost-effectiveness analysis predicts potential savings in cost and gains in quality-adjusted survival at 10 years when mitral valve replacement is compared with repair for severe ischemic MR. These projected benefits, however, were small and subject to variability. Efforts to further delineate predictors of long-term outcomes in patients with severe ischemic MR are needed to optimize surgical decisions for individual patients, which should yield more cost-effective care. CLINICAL TRIAL REGISTRATION: URL: https://www.clinicaltrials.gov. Unique identifier: NCT00807040.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.010 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".