Predictors of early renal dysfunction after heart transplantation: a report from the International Consortium on Primary Graft Dysfunction
Bibliographic record
Abstract
Abstract Background Renal dysfunction is a common complication after heart transplantation (HT). Renal replacement therapy (RRT) after HT has been associated with increased risk of death. Long-term renal dysfunction is associated mainly to immunosuppressive therapy but is also strongly related to post-transplant renal failure. Predictors of early renal dysfunction after HT have not been clearly identified. Purpose We aimed to define predictors of early renal dysfunction after HT. Methods Our consortium includes 10 centers in the US, Canada and Europe. We collected data on all consecutive single-organ HT recipients from 2010 to 2020. The primary outcome was early renal dysfunction (ERD), defined as a composite of need for RRT or creatinine ≥2.5 mg/dL 24 hours after HT. We assessed the incidence of early renal dysfunction and performed univariate and multivariate analyses to identify the recipient and transplant characteristics associated with its development. Results We included 2,764 HT recipients: 282 (10.2%) presented early renal dysfunction and 2482 (89.8%) did not. Recipients who presented postoperative renal dysfunction were more frequently male, Caucasian, with previous sternotomy, higher baseline creatinine, longer ischemic time and worse donor LVEF. They were also more likely to be under RRT, intravenous inotropes or ECMO support and there was more incidence of severe primary graft dysfunction (PGD) (Table 1). Multi-variable logistic regression demonstrated that the strongest predictors for post-transplant renal dysfunction were development of severe PGD (OR 5.26, 2.88–9.62, p<0,001) and RRT prior to HT (OR 5.80, 2.93–11.5, p<0.001). Other predictors were male sex, previous sternotomy, long ischemic time and need for inotropes prior to HT. Conclusions Early renal dysfunction is a common complication after HT with an incidence around 10% in a large and contemporary cohort. The presence of PGD and need for RRT pre-transplant were the strongest predictors for its development. Interestingly, emergent transplantation or need for MCS were not independently associated with ERD. Further studies are needed to identify patients at high risk of early and late kidney dysfunction that may benefit from combined transplantation. Funding Acknowledgement Type of funding sources: None.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".