Abstract 11259: Promoting Discharges in Pediatric Patients with Congenital Heart Disease on Ventricular Assist Device Support: An ACTION Quality Improvement Project
Bibliographic record
Abstract
Introduction: Discharge of pediatric ventricular assist device (VAD) patients is lagging behind adults, especially pediatric patients with concurrent congenital heart disease (CHD). This quality improvement project aimed to increase discharge rates and decrease time to discharge for pediatric patients with CHD on VAD support. Methods: Patients with CHD implanted with HeartWare HVAD or HeartMate 3 at participating Advanced Cardiac Therapies Improving Outcomes Network (ACTION) centers were included. Baseline demographics and outcomes including explant, transplant, or death, and time to outcome was collected from April 2018 until October 2019. QI interventions (VAD Flight Plan and Discharge Journey Map) were initiated in November 2019. Percentage discharged on VAD as well as time to outcome were compared before and after intervention. Results: Twenty-six patients with CHD (18 single ventricle physiology) were identified (14 pre-intervention and 12-post). The mean age was 16 years old with 27% implanted as Intermacs profile 1. Mortality on device was high at 38%. There were 77% (n=20) considered discharge eligible (alive on device > 30 days) while 42% (n=11) achieved discharge (Table 1). Mean length of stay post-implant for discharged patients was 33 days. A higher percentage of biventricular physiology were discharged (63%) compared to univentricular (33%). QI interventions did not change discharge rate (43% vs 42%), mean time to discharge (46.3 vs 43.4 days), transplant before discharge (17% vs 21%), or death before discharge (42% vs 43%) in the post-intervention group. Conclusion: Discharging CHD patients on VAD support from the hospital remains a challenge largely due to the complex and heterogenous nature of this population at the time of implant. Interventions to address optimal timing of VAD implantation and patient selection could impact outcomes.
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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 | 0.018 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".