Severe primary graft failure: Are there lasting impacts? Analysis from the PHTS Database
Bibliographic record
Abstract
Background Primary graft failure (PGF) is a leading cause of early morbidity and mortality after heart transplantation (HTx). PGF is secondary to graft ischemia and ischemia-reperfusion injuries to the cardiomyocytes and vasculature of the donor heart after transplantation. Longer-term outcomes after PGF are not well studied. Methods Patients with an HTx (January 1, 2010 to June 30, 2022) were identified using the Pediatric Heart Transplant Society registry. PGF was defined as death, retransplantation, or need for mechanical circulatory support within 72 hours of HTx. Kaplan-Meier analysis and Cox proportional hazard modeling were utilized. Results Of the 4,982 patients with a primary HTx, 5.4% ( n = 269) met criteria for PGF. Patients with PGF were younger, with higher proportion of congenital heart disease, longer cardiopulmonary bypass and ischemic times (IT), and more likely to be on extracorporeal membrane oxygenation or ventilator at HTx (all p < 0.0001, IT p = 0.0006). PGF resulted in lower overall survival (1 year: 54% vs 94%, p < 0.001). This remained true when conditional survival was examined at 30 and 90 days but not at 1 year ( p = 0.1143). Freedom from rejection did not differ between the groups at overall or conditional on 30 days but was slightly higher for those with PGF at 90 and 365 days. There was no difference in freedom from coronary allograft vasculopathy (CAV). PGF was an independent predictor of overall graft loss (hazard ratios [HR] 4.7, p < 0.0001) and conditional survival to 30 days (HR 2.47, p < 0.0001) and 90 days (HR 1.6, p = 0.012) but not beyond 1 year. Conclusions Severe PGF is an independent predictor of early mortality post-HTx but subsequently does not further impact long-term survival, overall risk of rejection, or CAV. Understanding the impact of milder forms of PGF on survival and long-term outcomes is still needed. Methods to decrease the risk of PGF, such as alternative preservation and storage techniques, may impact early mortality post-HTx.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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 teacher head, 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".