Predonation Prescription Opioid Use: A Novel Risk Factor for Readmission After Living Kidney Donation
Bibliographic record
Abstract
Implications of opioid use in living kidney donors for key outcomes, including readmission rates after nephrectomy, are unknown. We integrated Scientific Registry of Transplant Recipients data with records from a nationwide pharmacy claims warehouse and administrative records from an academic hospital consortium to quantify predonation prescription opioid use and postdonation readmission events. Associations of predonation opioid use (adjusted odds ratio [aOR]) in the year before donation and other baseline clinical, procedural, and center factors with readmission within 90 days postdonation were examined by using multivariate logistic regression. Among 14 959 living donors, 11.3% filled one or more opioid prescriptions in the year before donation. Donors with the highest level of predonation opioid use (>305 mg/year) were more than twice as likely as nonusers to be readmitted (6.8% vs. 2.6%; aOR 2.49, 95% confidence interval 1.74–3.58). Adjusted readmission risk was also significantly (p < 0.05) higher for women (aOR = 1.25), African Americans (aOR = 1.45), spouses (aOR = 1.42), exchange participants (aOR = 1.46), uninsured donors (aOR = 1.40), donors with predonation estimated glomerular filtration rate <60 mL/min/1.73 m 2 (aOR = 2.68), donors with predonation pulmonary conditions (aOR = 1.54), and after robotic nephrectomy (aOR = 1.68). Predonation opioid use is independently associated with readmission after donor nephrectomy. Future research should examine underlying mechanisms and approaches to reducing risks of postdonation complications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".