Incidence of post-transplant cytomegalovirus viremia in patients receiving lungs after ex vivo lung perfusion
Bibliographic record
Abstract
ObjectivesCytomegalovirus infection after lung transplant is associated with increased morbidity and mortality. Inflammation, infection, and longer ischemic times are important risk factors for cytomegalovirus infection. Ex vivo lung perfusion has helped to successfully increase the use of high-risk donors over the last decade. However, the impact of ex vivo lung perfusion on post-transplant cytomegalovirus infection is unknown.MethodsWe performed a retrospective analysis of all adult lung transplant recipients from 2010 to 2020. The primary end point was comparison of cytomegalovirus viremia between patients who received ex vivo lung perfusion donor lungs and patients who received non–ex vivo lung perfusion donor lungs. Cytomegalovirus viremia was defined as cytomegalovirus viral load greater than 1000 IU/mL within 2 years post-transplant. Secondary end points were the time from lung transplant to cytomegalovirus viremia, peak cytomegalovirus viral load, and survival. Outcomes were also compared between the different donor recipient cytomegalovirus serostatus matching groups.ResultsIncluded were 902 recipients of non–ex vivo lung perfusion lungs and 403 recipients of ex vivo lung perfusion lungs. There was no significant difference in the distribution of the cytomegalovirus serostatus matching groups. A total of 34.6% of patients in the non–ex vivo lung perfusion group developed cytomegalovirus viremia, as did 30.8% in the ex vivo lung perfusion group (P = .17). There was no difference in time to viremia, peak viral loads, or survival when comparing both groups. Likewise, all outcomes were comparable in the non–ex vivo lung perfusion and ex vivo lung perfusion groups within each serostatus matching group.ConclusionsThe practice of using more injured donor organs via ex vivo lung perfusion has not affected cytomegalovirus viremia rates and severity in lung transplant recipients in our center.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".