Systematic review and meta-analysis of surgical outcomes in Marfan patients undergoing aortic root surgery by composite-valve graft or valve sparing root replacement
Bibliographic record
Abstract
BACKGROUND: A major, life-limiting feature of Marfan syndrome (MFS) is the presence of aneurysmal disease. Cardiovascular intervention has dramatically improved the life expectancy of Marfan patients. Traditionally, the management of aortic root disease has been undertaken with composite-valve graft replacing the aortic valve and proximal aorta; more recently, valve sparing procedures have been developed to avoid the need for anticoagulation. This meta-analysis assesses the important surgical outcomes of the two surgical techniques. METHODS: A systematic review and meta-analysis of 23 studies reporting the outcomes of aortic root surgery in Marfan patients with data extracted for outcomes of early and late mortality, thromboembolic events, late bleeding complications and surgical reintervention rates. RESULTS: The outcomes of 2,976 Marfan patients undergoing aortic root surgery were analysed, 1,624 patients were treated with composite valve graft (CVG) and 1,352 patients were treated with valve sparing root replacement (VSRR). When compared against CVG, VSRR was associated with reduced risk of thromboembolism (OR =0.32; 95% CI, 0.16-0.62, P=0.0008), late hemorrhagic complications (OR =0.18; 95% CI, 0.07-0.45; P=0.0003) and endocarditis (OR =0.27; 95% CI, 0.10-0.68; P=0.006). Importantly there was no significant difference in reintervention rates between VSRR and CVG (OR =0.89; 95% CI, 0.35-2.24; P=0.80). CONCLUSIONS: There is an increasing body of evidence that VSRR can be reliably performed in Marfan patients, resulting in a durable repair with no increased risk of re-operation compared to CVG, thus avoiding the need for systemic anticoagulation in selected patients.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.033 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".