The Fate of the Left Ventricular Outflow Tract Following Interrupted Aortic Arch Repair
Bibliographic record
Abstract
Objectives: To examine the probability of left ventricular outflow tract (LVOT) reintervention following interrupted aortic arch (IAA) repair in neonates with LVOT obstruction (LVOTO) risk. Methods: This retrospective multicenter study included 150 neonates who underwent IAA repair (2003-2017); 100 of 150 (67%) had isolated IAA repair (with ventricular septal defect closure) and 50 of 150 (33%) had concomitant LVOT intervention: conal muscle resection (n = 16), Ross-Konno (n = 7), and Yasui operation (n = 27: single-stage n = 8, staged n = 19). Demographic and morphologic characteristics were reviewed. Factors associated with LVOT reoperation were explored using multivariable analysis. Results: Concomitant LVOT intervention was more likely in neonates with type B IAA, bicuspid aortic valve, aberrant right subclavian artery, smaller aortic valve annulus, and ascending aorta dimensions. On follow-up, five-year freedom from LVOT reoperation was highest following Ross-Konno (100%), 77% following Yasui (mainly for neo-aortic regurgitation), 77% following isolated IAA repair (mainly for LVOTO), and 47% following IAA repair with concomitant conal resection, P = .033. While all patients had low peak LVOT gradient at time of discharge, those who had conal resection developed higher gradients on follow-up ( P = .007). Ross-Konno and Yasui procedures were associated with higher right ventricular outflow tract (RVOT) reoperation. In the cohort following isolated IAA repair, aortic sinus Z score was associated with LVOT reoperation. Conclusions: Both Yasui and Ross-Konno operations effectively mitigate late LVOTO risk. The highest risk of reintervention for LVOTO was associated with conal muscle resection while the lowest risk is associated with Ross-Konno. The RVOT reoperation risk in patients who had Ross-Konno or Yasui does not seem to affect survival.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".