Procedural, pregnancy, and short‐term outcomes after fetal aortic valvuloplasty
Bibliographic record
Abstract
OBJECTIVES: We aimed to evaluate the effect of technical aspects of fetal aortic valvuloplasty (FAV) on procedural risks and pregnancy outcomes. BACKGROUND: FAV is performed in cases of severe mid-gestation aortic stenosis with the goal of preventing hypoplastic left heart syndrome (HLHS). METHODS: The International Fetal Cardiac Intervention Registry was queried for fetuses who underwent FAV from 2002 to 2018, excluding one high-volume center. RESULTS: The 108 fetuses had an attempted cardiac puncture (mean gestational age [GA] 26.1 ± 3.3 weeks). 83.3% of attempted interventions were technically successful (increased forward flow/new aortic insufficiency). The interventional cannula was larger than 19 g in 70.4%. More than one cardiac puncture was performed in 25.0%. Intraprocedural complications occurred in 48.1%, including bradycardia (34.1%), pericardial (22.2%) or pleural effusion (2.7%) requiring drainage, and balloon rupture (5.6%). Death within 48 hr occurred in 16.7% of fetuses. Of the 81 patients born alive, 59 were discharged home, 34 of whom had biventricular circulation. More than one cardiac puncture was associated with higher complication rates (p < .001). Larger cannula size was associated with higher pericardial effusion rates (p = .044). On multivariate analysis, technical success (odds ratio [OR] = 10.9, 95% confidence interval [CI] = 2.2-53.5, p = .003) and later GA at intervention (OR = 1.5, 95% CI = 1.2-1.9, p = .002) were associated with increased odds of live birth. CONCLUSIONS: FAV is an often successful but high-risk procedure. Multiple cardiac punctures are associated with increased complication and fetal mortality rates. Later GA at intervention and technical success were independently associated with increased odds of live birth. However, performing the procedure later in gestation may miss the window to prevent progression to HLHS.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".