Does adding an aortic root replacement or sinus repair during arch repair increase postoperative mortality? Evidence from the Canadian Thoracic Aortic Collaborative
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to examine the effect of the addition of an aortic root replacement or sinus repair on mortality and morbidity during aortic arch repair. METHODS: A total of 2472 patients underwent proximal or total aortic arch repair with hypothermic circulatory arrest between 2002 and 2018 at 12 centres. Multivariable logistic regressions (MV) and propensity score (PS) with inverse probability of treatment weighting (IPTW) analyses were performed. RESULTS: A total of 1099 (44.5%) patients had additional aortic root replacement (n = 934) or sinus repair (n = 165). Those with aortic root interventions were younger (61 ± 13 vs 64 ± 13 years, P < 0.001) and had less females (23% vs 35%, P < 0.001), less dissection (31% vs 36%, P = 0.004), less urgent cases (35% vs 39%, P = 0.047), more connective tissue disease (7% vs 3%, P < 0.001) and less total arch replacements (14% vs 22%, P < 0.001). On adjusted analyses, the addition of aortic root procedure was associated with increased mortality [MV: odds ratio (OR) 1.41, 95% confidence interval (CI) 1.03-1.92; PS-IPTW: risk increased by 3.7%, 95% CI 1.2-6.3%, P = 0.004]. Reoperation for bleeding was also increased with the addition of aortic root intervention (MV: OR 1.48, 95% 1.10-1.99; PS-IPTW: risk increased by 3.2%, 95% CI 0.8-5.6%, P = 0.009). The risks of stroke and dialysis-dependent renal failure were similar. When looking only at non-elective cases, the increased risk of mortality was more pronounced (MV: OR 1.60, 95% CI 1.11-2.32, P = 0.013; PS-IPTW: risk increased by 6.8%, 95 CI 1.7-11.8%, P = 0.008, and a number need to harm of 15 patients to cause 1 additional death). CONCLUSIONS: The addition of aortic root replacement or sinus repair during proximal or total aortic arch repair seems to increase postoperative mortality only in non-elective cases.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".