Impact of co-morbidities on survival after heart transplant in females of childbearing age: analysis of the International Society for Heart and Lung Transplantation (ISHLT) Registry
Bibliographic record
Abstract
Abstract Background Data on life expectancy specific to female heart transplant (HT) recipients of reproductive age are lacking, but are needed to inform shared decision making at the time of pre-conception counseling. We analyzed the ISHLT registry for post-transplant survival and studied the impact of co-morbidities in women of childbearing age. Methods Female HT recipients reported to the ISHLT registry who were aged 15–45 yrs at time of HT were included in this retrospective analysis. Primary outcome was post-transplant survival. Secondary outcomes included the impact of co-morbidities on conditional survival at 5, 10 and 15 years. Results Of 121,501 HT recipients (Jan 1992–June 2018), 30,179 (24.8%) were women. 9,229 (7.6%) were 15–45 yrs at time of HT. Overall median post-transplant survival was 15.2 yrs. Diabetes mellitus (DM) and/or severe chronic kidney disease (CKD) at HT adversely impacted long-term survival (Figure 1). Co-morbidities including cardiac allograft vasculopathy (CAV), DM and CKD negatively impacted 5yr and 10yr conditional survival, individually as well as a combination (Figure 2). 15yr conditional survival confirmed the negative impact of DM and CKD, while CAV did not reach statistical significance. Conclusion Female HT recipients of childbearing age have favorable survival post-HT. Conditional survival at 5 and 10 yrs is negatively impacted by CAV, DM and CKD. The quantification of survival outcomes after heart transplant in women of childbearing age may improve counseling as part of pre-conception informed decision making. Funding Acknowledgement Type of funding sources: None.
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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".