Ventricular assist device implantation in children with a mechanical valve: An ACTION registry analysis
Bibliographic record
Abstract
Background Patients with congenital heart disease (CHD) frequently have had valve interventions, including replacement with a mechanical valve (mechV). The impact of a mechV on clinical outcomes in patients undergoing ventricular assist device (VAD) implantation is not well characterized. Objectives This study assessed VAD outcomes in patients with CHD and a mechV. Methods All patients with a history of CHD ( n = 433) in the Advanced Cardiac Therapies Improving Outcomes Network database were included in the study (January 2012-January 2023). Patient characteristics and outcomes were assessed among patients with a mechV and without a mechV. Results Twenty-seven (6%) patients with CHD had a mechV at VAD implantation. Fourteen (52%) of the patients with mechV had univentricular anatomy and 13 (48%) had biventricular anatomy. Patients with mechV were older (4.9 vs 1.9 years, p = 0.02), smaller (14.9 vs 10.6 kg, p = 0.02), and had a higher interagency registry for mechanically assisted circulatory support profile ( p = 0.01). Three (11%) patients with mechV experienced a valve-related complication. There was no difference in survival ( p = 0.4) or ischemic stroke frequency (11% vs 13%, p = 1) between patients with mechV and non-mechV. Patients with mechV had higher frequency of hemorrhagic stroke (18% vs 4.7%, p = 0.01) and major bleeding (44% vs 26%, p = 0.04). Conclusions Patients with CHD with a mechV have similar survival to patients with non-mechV; however, there is higher risk of bleeding including hemorrhagic stroke.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".