They Deserve to Live: Impact of the Berlin Heart EXCOR on Small Children's Heart Transplant Waitlist in Low-Resource Settings
Bibliographic record
Abstract
Background: In newly emerging economy countries, the shortage of pediatric donor hearts and poor healthcare infrastructure poses a significant challenge. Although mechanical circulatory support (MCS) has been proven effective in enhancing heart transplant waiting list outcomes, economic barriers hinder its widespread adoption. Methods: A single-center retrospective study reviewed children under 10 kg on the heart transplant (HTx) list from 2012 to 2023. Elective or priority status was assigned based on their clinical condition at the time of transplant. In cases of clinical decompensation, centrifugal pump and extracorporeal membrane oxygenation, transitioned to the Berlin Heart EXCOR (BHE) pediatric ventricular assist device (VAD) was employed. Pre- and post-HTx outcomes were analyzed with descriptive statistics, Cox regression, and competing survival risks. Results: Out of 81 infants on the HTx list, 61.7% (50/81) were in critical condition. The median wait time was 224 days, and 34% (28/81) died while waiting. Out of 37 transplanted patients, 6 (16%) had graft dysfunction, and 10 (27%) had acute renal injury. Survival to discharge was 84% (31/37). Patients who received the BHE exhibited higher chances of receiving a transplant (hazard ratio: 2.3; 95% confidence interval: 1.2-4.6; P = .01). Priority status or MCS use did not significantly impact mortality post-transplant. Conclusion: Advanced MCS technologies can potentially reduce the mortality risk on the pediatric HTx waitlist. The findings highlight the significant waiting time for HTx and the critical role of the BHE in improving outcomes in children, particularly those under 10 kg. The results advocate for the adoption of ventricular assist devices as a viable interim solution to bridge critically ill children to HTx, ultimately enhancing their chances of survival despite limited donor heart availability.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".