Pretransplant Opioid Use and Survival After Lung Transplantation
Bibliographic record
Abstract
BACKGROUND: The impact of opioid use in lung transplant candidates on posttransplant outcomes is unknown. Studies on opioid therapy in kidney and liver transplant candidates have suggested increased risk of graft failure or death. We sought to analyze the relationship between pretransplant opioid use in lung transplant candidates and retransplant-free survival. METHODS: We retrospectively reviewed adult patients transplanted consecutively between November 2004 and August 2015. The exposure was any opioid use at time of transplant listing and primary outcome was retransplant-free survival, analyzed via Cox regression model adjusted for recipient age, gender, ethnicity, diagnosis, and bridging status. Secondary outcomes included duration of ventilation, intensive care unit and hospital length of stay, 3-month and 1-year survival, continuing opioid use at 1 year, and time to onset of chronic lung allograft dysfunction. RESULTS: The prevalence of opioid use at time of listing was 14% (61/425). Median daily oral morphine equivalent dose was 31 mg (18-54). Recipient ethnicity was associated with pretransplant opioid use. Opioid use at time of listing did not increase risk of death or retransplantation in an adjusted model (hazard ratio 1.12 [95% confidence interval 0.65-1.83], P = 0.6570). Secondary outcomes were similar between groups except hospital length of stay (opioid users 35 versus nonusers 27 d, P = 0.014). Continued opioid use at 1-year posttransplant was common (27/56, 48%). CONCLUSIONS: Pretransplant opioid use was not associated with retransplant-free survival in our cohort and should not necessarily preclude listing. Further work stratifying opioid use by indication and the association with opioid use disorder would be worthwhile.
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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) | 0.000 | 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 | 0.001 |
| 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".