Prescription opioid use before and after kidney transplant: Implications for posttransplant outcomes
Bibliographic record
Abstract
Evolving literature suggests that the epidemic of prescription opioid use affects the transplant population. We examined a novel database wherein national U.S. transplant registry records were linked to a large pharmaceutical claims warehouse (2007-2015) to characterize prescription opioid use before and after kidney transplant, and associations (adjusted hazard ratio, 95% LCLaHR95% UCL) with death and graft loss. Among 75 430 eligible patients, 43.1% filled opioids in the year before transplant. Use was more common among recipients who were women, white, unemployed, publicly insured, and with longer pretransplant dialysis. Of those with the highest level of pretransplant opioid use, 60% continued high-level use posttransplant. Pretransplant opioid use had graded associations with one-year posttransplant outcomes; the highest-level use predicted 46% increased risk of death (aHR 1.281.461.66) and 28% increased risk of all-cause graft failure (aHR 1.171.281.41). Effects of high-level opioid use in the first year after transplant were stronger, predicting twice the risk of death (aHR 1.932.242.60) and 68% higher all-cause graft failure risk (aHR 1.501.681.89) over the subsequent year; increased risk persisted over five years. While associations may, in part, reflect underlying conditions or behaviors, opioid use history is relevant in assessing and providing care to transplant candidates and recipients. Evolving literature suggests that the epidemic of prescription opioid use affects the transplant population. We examined a novel database wherein national U.S. transplant registry records were linked to a large pharmaceutical claims warehouse (2007-2015) to characterize prescription opioid use before and after kidney transplant, and associations (adjusted hazard ratio, 95% LCLaHR95% UCL) with death and graft loss. Among 75 430 eligible patients, 43.1% filled opioids in the year before transplant. Use was more common among recipients who were women, white, unemployed, publicly insured, and with longer pretransplant dialysis. Of those with the highest level of pretransplant opioid use, 60% continued high-level use posttransplant. Pretransplant opioid use had graded associations with one-year posttransplant outcomes; the highest-level use predicted 46% increased risk of death (aHR 1.281.461.66) and 28% increased risk of all-cause graft failure (aHR 1.171.281.41). Effects of high-level opioid use in the first year after transplant were stronger, predicting twice the risk of death (aHR 1.932.242.60) and 68% higher all-cause graft failure risk (aHR 1.501.681.89) over the subsequent year; increased risk persisted over five years. While associations may, in part, reflect underlying conditions or behaviors, opioid use history is relevant in assessing and providing care to transplant candidates and recipients.
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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 | 0.000 |
| 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".