A Comprehensive and Contemporary Review on Immunosuppression Therapy for Heart Transplantation
Bibliographic record
Abstract
Heart transplantation is the standard of therapy for patients with end-stage heart disease. Since the first human-to-human heart transplantation, performed in 1967, advances in organ donation, surgical techniques, organ preservation, perioperative care, immunologic risk assessment, immunosuppression agents, monitoring of graft function and surveillance of long-term complications have drastically increased recipient survival. However, there are yet many challenges in the modern era of heart transplantation in which immunosuppression may play a key role in further advances in the field. A fine-tuning of immune modulation to prevent graft rejection while avoiding side effects from over immunosuppression has been the vital goal of basic and clinical research. Individualization of drug choices and strategies, taking into account the recipient's clinical characteristics, underlying heart failure diagnosis, immunologic risk and comorbidities seem to be the ideal approaches to improve post-transplant morbidity and survival while preventing both rejection and complications of immunosuppression. The aim of the present review is to provide a practical, comprehensive overview of contemporary immunosuppression in heart transplantation. Clinical evidence for immunosuppressive drugs is reviewed and practical approaches are provided. Cardiac allograft rejection classification and up-to-date management are summarized. Expanding therapies, such as photophoresis, are outlined. Drug-to-drug interactions of immunosuppressive agents focused on cardiovascular medications are summarized. Special situations involving heart transplantation such as sarcoidosis, Chagas diseases and pediatric immunosuppression are also reviewed. The evolution of phamacogenomics to individualize immunosuppressive therapy is described. Finally, future perspectives in the field of immunosuppression in heart transplantation are highlighted.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.001 |
| 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".