Current-Era Outcomes of Balloon Aortic Valvotomy in Neonates and Infants
Bibliographic record
Abstract
The optimal initial treatment pathway for aortic valve stenosis remains debated. The objective of this study was to review current outcomes of balloon aortic valvotomy (BAV) in neonates and infants. Neonates and infants with a biventricular circulation treated with BAV between 2004 and 2019 were reviewed. One hundred thirty-nine infants (48% neonates) with median (Q1, Q3) age of 33(7, 84) days and weight 4.0 (3.4, 5.1) kg were followed up for 7.1 (3.3, 11.0) years. BAV reduced peak-to-peak gradient from mean (SD) 52 (16) mmHg to 18 (12) mmHg; P < 0.001. Aortic regurgitation (AI) increased with time after BAV. Three children died during follow-up. Fifty-one reinterventions (26 BAV, 19 aortic valve replacements [AVRs], and 6 surgical valvotomies) were performed on 40 children. Freedom from AVR (95% CI) was 96% (93%-99%) at 1, 91% (86%-96%) at 5, and 86% (79%-93%) at 10 years. The predictors of AVR were a unicommissural valve (hazard ratio [HR] [95% CI]: 3.7 [1.4-9.6]; P = 0.007) and moderate to severe AI after index BAV (HR [95% CI]: 3.3 [1.1-9.7]; P = 0.029). Freedom from reintervention was 84% (78%-90%) at 1, 76% (69%-83%) at 5, and 69% (60-78%) at 10 years. Main predictors of reintervention were age below 1 month (HR [95% CI]: 2.1 [1.1-4.1]; P = 0.032) and postdilation peak-to-peak gradient (per 10-mmHg increase; HR [95% CI]: 1.36 [1.02-1.79]; P = 0.032). BAV is a safe and effective treatment for aortic valve stenosis in neonates and infants. Outcomes are competitive with contemporary published data on aortic valve repair in relation to mortality, gradient relief, long-term AVR, and reintervention rates. In the absence of significant AI, surgery can be reserved for those with gradients resistant to valve dilation.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".