Incidence and Risk Factors for Stroke After Combined Heart‐Kidney and Heart‐Liver Transplantation
Bibliographic record
Abstract
OBJECTIVE: While stroke is a well-recognized complication of isolated heart transplantation, stroke in patients undergoing simultaneous heart-liver (HLT) and heart-kidney transplantation (HKT) has not been explored. This study assessed postoperative stroke incidence, risk factors, and outcomes in HLT and HKT compared with isolated heart transplant. METHODS: The United Network for Organ Sharing database was queried for adult patients undergoing HLT, HKT, and isolated heart transplants between 1994 and 2022. Patients were stratified by presence of in-hospital stroke after transplant. Post-transplant survival at 1-year was assessed using Kaplan-Meier analysis and log-rank tests. Separate multivariable logistic regression models were constructed to identify risk factors for stroke after HKT and HLT. RESULTS: Of 2326 HKT recipients, 85 experienced stroke, and of 442 HLT recipients, 19 experienced stroke. Stroke was more common after HKT and HLT than after an isolated heart transplant (3.7% vs. 4.3% vs. 2.9%, p = 0.01). One-year post-transplant survival was lower in those with stroke among both HKT recipients (64.5% vs. 88.7%, p(log-rank) < 0.001) and HLT recipients (43.8% vs. 87.4%, p(log-rank) < 0.001. Pre-transplant pVAD, prior stroke, postoperative dialysis, diabetes, prior cardiac surgery, and heart cold ischemic time were independent risk factors for stroke after HKT, after adjusting for age, sex, and need for blood transfusion on the waitlist. For HLT, postoperative dialysis was a significant risk factor. CONCLUSIONS: Stroke is more common after HKT and HLT than after isolated heart transplant, and results in poor survival. Independent risk factors for stroke include pre-transplant percutaneous VAD (HKT) and postoperative dialysis (HKT and HLT).
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".