Abstract 18537: Aortic Valve Repair Improves Mid-term Outcomes versus Replacement for Aortic Insufficiency: A Propensity Matched Study
Bibliographic record
Abstract
Objectives: Aortic valve (AV) repair has emerged as a feasible and attractive alternative to AV replacement in patients with aortic insufficiency (AI). However, little data exists comparing outcomes following repair versus replacement. We performed a single-center comparative study in patients undergoing AV repair or replacement for AI. Methods: Patients undergoing AV replacement (n=263) or repair (n=153) for AI between 2000 and 2014 were included. Patients ineligible for both repair and replacement surgeries and patients lacking perioperative data were excluded (n=155). To adjust for baseline differences, non-parsimonious logistic regression models were used to generate propensity scores. A 1:1 greedy matching algorithm was used to create 70 propensity matched pairs. Peri-operative and long-term clinical and echocardiographic outcomes were assessed, focusing on survival and valve-related events, and analyzed using Kaplan-Meier techniques. Primary endpoints included late mortality, myocardial infarction, stroke, and AV reoperation. Total follow-up exceeded 1100 patient-years in the entire cohort and 800 patient-years in the matched cohort. Results: The matched pairs were similar in baseline attributes including age (57±14 vs. 56±15, p=0.75), sex (Female: 17% vs. 25%, p=0.22), incidence of LV dysfunction (27% vs. 24%, p=0.38), severe AI (67% vs. 74%, p=0.42) and presence of cardiac risk factors. Replacements consisted of 53% mechanical, 41% bioprosthetic, and 6% homograft valves. Repairs included 61% valve-sparing root replacements, 70% cusp repairs with 43% bicuspid AVs. In hospital mortality (1% vs. 4%, repair vs. replacement, p=0.62) and perioperative outcomes were similar between groups. Four-year survival was 97±2% and 87±4% in repair vs. replacement groups (p=0.11). A total of 5 vs. 17 valve-related events occurred in repair vs. replacement groups. Freedom from primary endpoints at 4 years was 97±2% and 82±8% in the repair vs. replacement groups (p=0.03). Conclusions: In a propensity matched cohort, AV repair for AI was associated with a mid-term reduction in death and serious valve related complications compared to AV replacement. AV repair should be considered, when technically feasible, as the preferred treatment of AI.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".