Current Use of Hearts From Hepatitis C Viremic Donors
Bibliographic record
Abstract
BACKGROUND: Strategies to improve donor heart utilization are required in the setting of limited donor availability. One innovative strategy is to consider the use of hepatitis C viremic (HCV) nucleic acid amplification test positive donors in hepatitis C-negative recipients, given the availability of highly effective direct acting antiviral agents. We utilized United Network for Organ Sharing data to evaluate the geographic distribution, clinical characteristics, and post-transplant outcomes of HCV+ donor hearts. METHODS AND RESULTS: The United Network for Organ Sharing registry was queried for all HCV+ recovered donors and those considered for heart donation classified by sex, age group, United Network for Organ Sharing region, and cause of death from January 1, 2014, to December 31, 2017. Propensity score matching (3:1) was applied to the recipients based on the index for mortality prediction after cardiac transplantation score and donor risk index. A total of 1306 HCV+ donors were recovered from 2014 to 2017 of whom 1078 (82.5%) were 18 to 49 and predominantly from the Appalachia region (United Network for Organ Sharing regions 2, 3, and 11). A total of 64 (5%) HCV+ donor hearts were transplanted in this interval. The match-adjusted risk difference in survival was estimated to be 0.87% ( P=0.83) at 12 months. CONCLUSIONS: To meet the demands of heart transplantation, we must consider additional strategies to expand the donor pool. From 2014 to 2017, despite availability of highly effective direct acting antiviral therapy, only 5% of HCV+ donor hearts were accepted for transplantation. National efforts may be required to capitalize on this resource while we continue to carefully monitor the safety of this novel approach.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".